Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

PubMed

Matsubara, Takashi

2017-01-01

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.

Designing optimal stimuli to control neuronal spike timing

PubMed Central

Packer, Adam M.; Yuste, Rafael; Paninski, Liam

2011-01-01

Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents. PMID:21511704

Submillisecond Optogenetic Control of Neuronal Firing with Two-Photon Holographic Photoactivation of Chronos

PubMed Central

Ronzitti, Emiliano; Conti, Rossella; Zampini, Valeria; Tanese, Dimitrii; Klapoetke, Nathan; Boyden, Edward S.; Papagiakoumou, Eirini

2017-01-01

Optogenetic neuronal network manipulation promises to unravel a long-standing mystery in neuroscience: how does microcircuit activity relate causally to behavioral and pathological states? The challenge to evoke spikes with high spatial and temporal complexity necessitates further joint development of light-delivery approaches and custom opsins. Two-photon (2P) light-targeting strategies demonstrated in-depth generation of action potentials in photosensitive neurons both in vitro and in vivo, but thus far lack the temporal precision necessary to induce precisely timed spiking events. Here, we show that efficient current integration enabled by 2P holographic amplified laser illumination of Chronos, a highly light-sensitive and fast opsin, can evoke spikes with submillisecond precision and repeated firing up to 100 Hz in brain slices from Swiss male mice. These results pave the way for optogenetic manipulation with the spatial and temporal sophistication necessary to mimic natural microcircuit activity. SIGNIFICANCE STATEMENT To reveal causal links between neuronal activity and behavior, it is necessary to develop experimental strategies to induce spatially and temporally sophisticated perturbation of network microcircuits. Two-photon computer generated holography (2P-CGH) recently demonstrated 3D optogenetic control of selected pools of neurons with single-cell accuracy in depth in the brain. Here, we show that exciting the fast opsin Chronos with amplified laser 2P-CGH enables cellular-resolution targeting with unprecedented temporal control, driving spiking up to 100 Hz with submillisecond onset precision using low laser power densities. This system achieves a unique combination of spatial flexibility and temporal precision needed to pattern optogenetically inputs that mimic natural neuronal network activity patterns. PMID:28972125

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain.

PubMed

Higgins, Irina; Stringer, Simon; Schnupp, Jan

2017-01-01

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain

PubMed Central

Stringer, Simon

2017-01-01

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable. PMID:28797034

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE.

PubMed

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

PubMed Central

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M.; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis. PMID:28596729

SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

PubMed

Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

2017-01-01

This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

Bayesian Ising approximation for learning dictionaries of multispike timing patterns in premotor neurons

NASA Astrophysics Data System (ADS)

Hernandez Lahme, Damian; Sober, Samuel; Nemenman, Ilya

Important questions in computational neuroscience are whether, how much, and how information is encoded in the precise timing of neural action potentials. We recently demonstrated that, in the premotor cortex during vocal control in songbirds, spike timing is far more informative about upcoming behavior than is spike rate (Tang et al, 2014). However, identification of complete dictionaries that relate spike timing patterns with the controled behavior remains an elusive problem. Here we present a computational approach to deciphering such codes for individual neurons in the songbird premotor area RA, an analog of mammalian primary motor cortex. Specifically, we analyze which multispike patterns of neural activity predict features of the upcoming vocalization, and hence are important codewords. We use a recently introduced Bayesian Ising Approximation, which properly accounts for the fact that many codewords overlap and hence are not independent. Our results show which complex, temporally precise multispike combinations are used by individual neurons to control acoustic features of the produced song, and that these code words are different across individual neurons and across different acoustic features. This work was supported, in part, by JSMF Grant 220020321, NSF Grant 1208126, NIH Grant NS084844 and NIH Grant 1 R01 EB022872.

Development of on-off spiking in superior paraolivary nucleus neurons of the mouse

PubMed Central

Felix, Richard A.; Vonderschen, Katrin; Berrebi, Albert S.

2013-01-01

The superior paraolivary nucleus (SPON) is a prominent cell group in the auditory brain stem that has been increasingly implicated in representing temporal sound structure. Although SPON neurons selectively respond to acoustic signals important for sound periodicity, the underlying physiological specializations enabling these responses are poorly understood. We used in vitro and in vivo recordings to investigate how SPON neurons develop intrinsic cellular properties that make them well suited for encoding temporal sound features. In addition to their hallmark rebound spiking at the stimulus offset, SPON neurons were characterized by spiking patterns termed onset, adapting, and burst in response to depolarizing stimuli in vitro. Cells with burst spiking had some morphological differences compared with other SPON neurons and were localized to the dorsolateral region of the nucleus. Both membrane and spiking properties underwent strong developmental regulation, becoming more temporally precise with age for both onset and offset spiking. Single-unit recordings obtained in young mice demonstrated that SPON neurons respond with temporally precise onset spiking upon tone stimulation in vivo, in addition to the typical offset spiking. Taken together, the results of the present study demonstrate that SPON neurons develop sharp on-off spiking, which may confer sensitivity to sound amplitude modulations or abrupt sound transients. These findings are consistent with the proposed involvement of the SPON in the processing of temporal sound structure, relevant for encoding communication cues. PMID:23515791

Seasonal Plasticity of Precise Spike Timing in the Avian Auditory System

PubMed Central

Sen, Kamal; Rubel, Edwin W; Brenowitz, Eliot A.

2015-01-01

Vertebrate audition is a dynamic process, capable of exhibiting both short- and long-term adaptations to varying listening conditions. Precise spike timing has long been known to play an important role in auditory encoding, but its role in sensory plasticity remains largely unexplored. We addressed this issue in Gambel's white-crowned sparrow (Zonotrichia leucophrys gambelii), a songbird that shows pronounced seasonal fluctuations in circulating levels of sex-steroid hormones, which are known to be potent neuromodulators of auditory function. We recorded extracellular single-unit activity in the auditory forebrain of males and females under different breeding conditions and used a computational approach to explore two potential strategies for the neural discrimination of sound level: one based on spike counts and one based on spike timing reliability. We report that breeding condition has robust sex-specific effects on spike timing. Specifically, in females, breeding condition increases the proportion of cells that rely solely on spike timing information and increases the temporal resolution required for optimal intensity encoding. Furthermore, in a functionally distinct subset of cells that are particularly well suited for amplitude encoding, female breeding condition enhances spike timing-based discrimination accuracy. No effects of breeding condition were observed in males. Our results suggest that high-resolution temporal discharge patterns may provide a plastic neural substrate for sensory coding. PMID:25716843

Propagating synchrony in feed-forward networks

PubMed Central

Jahnke, Sven; Memmesheimer, Raoul-Martin; Timme, Marc

2013-01-01

Coordinated patterns of precisely timed action potentials (spikes) emerge in a variety of neural circuits but their dynamical origin is still not well understood. One hypothesis states that synchronous activity propagating through feed-forward chains of groups of neurons (synfire chains) may dynamically generate such spike patterns. Additionally, synfire chains offer the possibility to enable reliable signal transmission. So far, mostly densely connected chains, often with all-to-all connectivity between groups, have been theoretically and computationally studied. Yet, such prominent feed-forward structures have not been observed experimentally. Here we analytically and numerically investigate under which conditions diluted feed-forward chains may exhibit synchrony propagation. In addition to conventional linear input summation, we study the impact of non-linear, non-additive summation accounting for the effect of fast dendritic spikes. The non-linearities promote synchronous inputs to generate precisely timed spikes. We identify how non-additive coupling relaxes the conditions on connectivity such that it enables synchrony propagation at connectivities substantially lower than required for linearly coupled chains. Although the analytical treatment is based on a simple leaky integrate-and-fire neuron model, we show how to generalize our methods to biologically more detailed neuron models and verify our results by numerical simulations with, e.g., Hodgkin Huxley type neurons. PMID:24298251

A Spiking Neural Network System for Robust Sequence Recognition.

PubMed

Yu, Qiang; Yan, Rui; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2016-03-01

This paper proposes a biologically plausible network architecture with spiking neurons for sequence recognition. This architecture is a unified and consistent system with functional parts of sensory encoding, learning, and decoding. This is the first systematic model attempting to reveal the neural mechanisms considering both the upstream and the downstream neurons together. The whole system is a consistent temporal framework, where the precise timing of spikes is employed for information processing and cognitive computing. Experimental results show that the system is competent to perform the sequence recognition, being robust to noisy sensory inputs and invariant to changes in the intervals between input stimuli within a certain range. The classification ability of the temporal learning rule used in the system is investigated through two benchmark tasks that outperform the other two widely used learning rules for classification. The results also demonstrate the computational power of spiking neurons over perceptrons for processing spatiotemporal patterns. In summary, the system provides a general way with spiking neurons to encode external stimuli into spatiotemporal spikes, to learn the encoded spike patterns with temporal learning rules, and to decode the sequence order with downstream neurons. The system structure would be beneficial for developments in both hardware and software.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

PubMed

Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

2017-05-01

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task

PubMed Central

Torre, Emiliano; Quaglio, Pietro; Denker, Michael; Brochier, Thomas; Riehle, Alexa

2016-01-01

The computational role of spike time synchronization at millisecond precision among neurons in the cerebral cortex is hotly debated. Studies performed on data of limited size provided experimental evidence that low-order correlations occur in relation to behavior. Advances in electrophysiological technology to record from hundreds of neurons simultaneously provide the opportunity to observe coordinated spiking activity of larger populations of cells. We recently published a method that combines data mining and statistical evaluation to search for significant patterns of synchronous spikes in massively parallel spike trains (Torre et al., 2013). The method solves the computational and multiple testing problems raised by the high dimensionality of the data. In the current study, we used our method on simultaneous recordings from two macaque monkeys engaged in an instructed-delay reach-to-grasp task to determine the emergence of spike synchronization in relation to behavior. We found a multitude of synchronous spike patterns aligned in both monkeys along a preferential mediolateral orientation in brain space. The occurrence of the patterns is highly specific to behavior, indicating that different behaviors are associated with the synchronization of different groups of neurons (“cell assemblies”). However, pooled patterns that overlap in neuronal composition exhibit no specificity, suggesting that exclusive cell assemblies become active during different behaviors, but can recruit partly identical neurons. These findings are consistent across multiple recording sessions analyzed across the two monkeys. SIGNIFICANCE STATEMENT Neurons in the brain communicate via electrical impulses called spikes. How spikes are coordinated to process information is still largely unknown. Synchronous spikes are effective in triggering a spike emission in receiving neurons and have been shown to occur in relation to behavior in a number of studies on simultaneous recordings of few neurons. We recently published a method to extend this type of investigation to larger data. Here, we apply it to simultaneous recordings of hundreds of neurons from the motor cortex of macaque monkeys performing a motor task. Our analysis reveals groups of neurons selectively synchronizing their activity in relation to behavior, which sheds new light on the role of synchrony in information processing in the cerebral cortex. PMID:27511007

Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task.

PubMed

Torre, Emiliano; Quaglio, Pietro; Denker, Michael; Brochier, Thomas; Riehle, Alexa; Grün, Sonja

2016-08-10

The computational role of spike time synchronization at millisecond precision among neurons in the cerebral cortex is hotly debated. Studies performed on data of limited size provided experimental evidence that low-order correlations occur in relation to behavior. Advances in electrophysiological technology to record from hundreds of neurons simultaneously provide the opportunity to observe coordinated spiking activity of larger populations of cells. We recently published a method that combines data mining and statistical evaluation to search for significant patterns of synchronous spikes in massively parallel spike trains (Torre et al., 2013). The method solves the computational and multiple testing problems raised by the high dimensionality of the data. In the current study, we used our method on simultaneous recordings from two macaque monkeys engaged in an instructed-delay reach-to-grasp task to determine the emergence of spike synchronization in relation to behavior. We found a multitude of synchronous spike patterns aligned in both monkeys along a preferential mediolateral orientation in brain space. The occurrence of the patterns is highly specific to behavior, indicating that different behaviors are associated with the synchronization of different groups of neurons ("cell assemblies"). However, pooled patterns that overlap in neuronal composition exhibit no specificity, suggesting that exclusive cell assemblies become active during different behaviors, but can recruit partly identical neurons. These findings are consistent across multiple recording sessions analyzed across the two monkeys. Neurons in the brain communicate via electrical impulses called spikes. How spikes are coordinated to process information is still largely unknown. Synchronous spikes are effective in triggering a spike emission in receiving neurons and have been shown to occur in relation to behavior in a number of studies on simultaneous recordings of few neurons. We recently published a method to extend this type of investigation to larger data. Here, we apply it to simultaneous recordings of hundreds of neurons from the motor cortex of macaque monkeys performing a motor task. Our analysis reveals groups of neurons selectively synchronizing their activity in relation to behavior, which sheds new light on the role of synchrony in information processing in the cerebral cortex. Copyright © 2016 Torre, et al.

Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity

NASA Astrophysics Data System (ADS)

Mizusaki, Beatriz E. P.; Agnes, Everton J.; Erichsen, Rubem; Brunnet, Leonardo G.

2017-08-01

The plastic character of brain synapses is considered to be one of the foundations for the formation of memories. There are numerous kinds of such phenomenon currently described in the literature, but their role in the development of information pathways in neural networks with recurrent architectures is still not completely clear. In this paper we study the role of an activity-based process, called pre-synaptic dependent homeostatic scaling, in the organization of networks that yield precise-timed spiking patterns. It encodes spatio-temporal information in the synaptic weights as it associates a learned input with a specific response. We introduce a correlation measure to evaluate the precision of the spiking patterns and explore the effects of different inhibitory interactions and learning parameters. We find that large learning periods are important in order to improve the network learning capacity and discuss this ability in the presence of distinct inhibitory currents.

Discrimination of Dynamic Tactile Contact by Temporally Precise Event Sensing in Spiking Neuromorphic Networks

PubMed Central

Lee, Wang Wei; Kukreja, Sunil L.; Thakor, Nitish V.

2017-01-01

This paper presents a neuromorphic tactile encoding methodology that utilizes a temporally precise event-based representation of sensory signals. We introduce a novel concept where touch signals are characterized as patterns of millisecond precise binary events to denote pressure changes. This approach is amenable to a sparse signal representation and enables the extraction of relevant features from thousands of sensing elements with sub-millisecond temporal precision. We also proposed measures adopted from computational neuroscience to study the information content within the spiking representations of artificial tactile signals. Implemented on a state-of-the-art 4096 element tactile sensor array with 5.2 kHz sampling frequency, we demonstrate the classification of transient impact events while utilizing 20 times less communication bandwidth compared to frame based representations. Spiking sensor responses to a large library of contact conditions were also synthesized using finite element simulations, illustrating an 8-fold improvement in information content and a 4-fold reduction in classification latency when millisecond-precise temporal structures are available. Our research represents a significant advance, demonstrating that a neuromorphic spatiotemporal representation of touch is well suited to rapid identification of critical contact events, making it suitable for dynamic tactile sensing in robotic and prosthetic applications. PMID:28197065

Temporal precision in the visual pathway through the interplay of excitation and stimulus-driven suppression.

PubMed

Butts, Daniel A; Weng, Chong; Jin, Jianzhong; Alonso, Jose-Manuel; Paninski, Liam

2011-08-03

Visual neurons can respond with extremely precise temporal patterning to visual stimuli that change on much slower time scales. Here, we investigate how the precise timing of cat thalamic spike trains-which can have timing as precise as 1 ms-is related to the stimulus, in the context of both artificial noise and natural visual stimuli. Using a nonlinear modeling framework applied to extracellular data, we demonstrate that the precise timing of thalamic spike trains can be explained by the interplay between an excitatory input and a delayed suppressive input that resembles inhibition, such that neuronal responses only occur in brief windows where excitation exceeds suppression. The resulting description of thalamic computation resembles earlier models of contrast adaptation, suggesting a more general role for mechanisms of contrast adaptation in visual processing. Thus, we describe a more complex computation underlying thalamic responses to artificial and natural stimuli that has implications for understanding how visual information is represented in the early stages of visual processing.

Using computer simulations to determine the limitations of dynamic clamp stimuli applied at the soma in mimicking distributed conductance sources.

PubMed

Lin, Risa J; Jaeger, Dieter

2011-05-01

In previous studies we used the technique of dynamic clamp to study how temporal modulation of inhibitory and excitatory inputs control the frequency and precise timing of spikes in neurons of the deep cerebellar nuclei (DCN). Although this technique is now widely used, it is limited to interpreting conductance inputs as being location independent; i.e., all inputs that are biologically distributed across the dendritic tree are applied to the soma. We used computer simulations of a morphologically realistic model of DCN neurons to compare the effects of purely somatic vs. distributed dendritic inputs in this cell type. We applied the same conductance stimuli used in our published experiments to the model. To simulate variability in neuronal responses to repeated stimuli, we added a somatic white current noise to reproduce subthreshold fluctuations in the membrane potential. We were able to replicate our dynamic clamp results with respect to spike rates and spike precision for different patterns of background synaptic activity. We found only minor differences in the spike pattern generation between focal or distributed input in this cell type even when strong inhibitory or excitatory bursts were applied. However, the location dependence of dynamic clamp stimuli is likely to be different for each cell type examined, and the simulation approach developed in the present study will allow a careful assessment of location dependence in all cell types.

Exact event-driven implementation for recurrent networks of stochastic perfect integrate-and-fire neurons.

PubMed

Taillefumier, Thibaud; Touboul, Jonathan; Magnasco, Marcelo

2012-12-01

In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models delineate a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision. However, there are inherent difficulties in simulating their networks' dynamics in silico with standard numerical discretization schemes. Indeed, the well-posedness of the evolution of such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. Here, we answer these issues for perfect stochastic integrate-and-fire neurons by designing an exact event-driven algorithm for the simulation of recurrent networks, with delayed Dirac-like interactions. In addition to being exact from the mathematical standpoint, our proposed method is highly efficient numerically. We envision that our algorithm is especially indicated for studying the emergence of polychronized motifs in networks evolving under spike-timing-dependent plasticity with intrinsic noise.

Upregulation of transmitter release probability improves a conversion of synaptic analogue signals into neuronal digital spikes

PubMed Central

2012-01-01

Action potentials at the neurons and graded signals at the synapses are primary codes in the brain. In terms of their functional interaction, the studies were focused on the influence of presynaptic spike patterns on synaptic activities. How the synapse dynamics quantitatively regulates the encoding of postsynaptic digital spikes remains unclear. We investigated this question at unitary glutamatergic synapses on cortical GABAergic neurons, especially the quantitative influences of release probability on synapse dynamics and neuronal encoding. Glutamate release probability and synaptic strength are proportionally upregulated by presynaptic sequential spikes. The upregulation of release probability and the efficiency of probability-driven synaptic facilitation are strengthened by elevating presynaptic spike frequency and Ca2+. The upregulation of release probability improves spike capacity and timing precision at postsynaptic neuron. These results suggest that the upregulation of presynaptic glutamate release facilitates a conversion of synaptic analogue signals into digital spikes in postsynaptic neurons, i.e., a functional compatibility between presynaptic and postsynaptic partners. PMID:22852823

Proportional spike-timing precision and firing reliability underlie efficient temporal processing of periodicity and envelope shape cues

PubMed Central

Zheng, Y.

2013-01-01

Temporal sound cues are essential for sound recognition, pitch, rhythm, and timbre perception, yet how auditory neurons encode such cues is subject of ongoing debate. Rate coding theories propose that temporal sound features are represented by rate tuned modulation filters. However, overwhelming evidence also suggests that precise spike timing is an essential attribute of the neural code. Here we demonstrate that single neurons in the auditory midbrain employ a proportional code in which spike-timing precision and firing reliability covary with the sound envelope cues to provide an efficient representation of the stimulus. Spike-timing precision varied systematically with the timescale and shape of the sound envelope and yet was largely independent of the sound modulation frequency, a prominent cue for pitch. In contrast, spike-count reliability was strongly affected by the modulation frequency. Spike-timing precision extends from sub-millisecond for brief transient sounds up to tens of milliseconds for sounds with slow-varying envelope. Information theoretic analysis further confirms that spike-timing precision depends strongly on the sound envelope shape, while firing reliability was strongly affected by the sound modulation frequency. Both the information efficiency and total information were limited by the firing reliability and spike-timing precision in a manner that reflected the sound structure. This result supports a temporal coding strategy in the auditory midbrain where proportional changes in spike-timing precision and firing reliability can efficiently signal shape and periodicity temporal cues. PMID:23636724

An online supervised learning method based on gradient descent for spiking neurons.

PubMed

Xu, Yan; Yang, Jing; Zhong, Shuiming

2017-09-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Millisecond-Scale Motor Encoding in a Cortical Vocal Area

NASA Astrophysics Data System (ADS)

Nemenman, Ilya; Tang, Claire; Chehayeb, Diala; Srivastava, Kyle; Sober, Samuel

2015-03-01

Studies of motor control have almost universally examined firing rates to investigate how the brain shapes behavior. In principle, however, neurons could encode information through the precise temporal patterning of their spike trains as well as (or instead of) through their firing rates. Although the importance of spike timing has been demonstrated in sensory systems, it is largely unknown whether timing differences in motor areas could affect behavior. We tested the hypothesis that significant information about trial-by-trial variations in behavior is represented by spike timing in the songbird vocal motor system. We found that neurons in motor cortex convey information via spike timing far more often than via spike rate and that the amount of information conveyed at the millisecond timescale greatly exceeds the information available from spike counts. These results demonstrate that information can be represented by spike timing in motor circuits and suggest that timing variations evoke differences in behavior. This work was supported in part by the National Institutes of Health, National Science Foundation, and James S. McDonnell Foundation

High precision calcium isotope analysis using 42Ca-48Ca double-spike TIMS technique

NASA Astrophysics Data System (ADS)

Feng, L.; Zhou, L.; Gao, S.; Tong, S. Y.; Zhou, M. L.

2014-12-01

Double spike techniques are widely used for determining calcium isotopic compositions of natural samples. The most important factor controlling precision of the double spike technique is the choice of appropriate spike isotope pair, the composition of double spikes and the ratio of spike to sample（CSp/CN）. We propose an optimal 42Ca-48Ca double spike protocol which yields the best internal precision for calcium isotopic composition determinations among all kinds of spike pairs and various spike compositions and ratios of spike to sample, as predicted by linear error propagation method. It is suggested to use spike composition of 42Ca/(42Ca+48Ca) = 0.44 mol/mol and CSp/(CN+ CSp)= 0.12mol/mol because it takes both advantages of the largest mass dispersion between 42Ca and 48Ca (14%) and lowest spike cost. Spiked samples were purified by pass through homemade micro-column filled with Ca special resin. K, Ti and other interference elements were completely separated, while 100% calcium was recovered with negligible blank. Data collection includes integration time, idle time, focus and peakcenter frequency, which were all carefully designed for the highest internal precision and lowest analysis time. All beams were automatically measured in a sequence by Triton TIMS so as to eliminate difference of analytical conditions between samples and standards, and also to increase the analytical throughputs. The typical internal precision of 100 duty cycles for one beam is 0.012‒0.015 ‰ (2δSEM), which agrees well with the predicted internal precision of 0.0124 ‰ (2δSEM). Our methods improve internal precisions by a factor of 2‒10 compared to previous methods of determination of calcium isotopic compositions by double spike TIMS. We analyzed NIST SRM 915a, NIST SRM 915b and Pacific Seawater as well as interspersed geological samples during two months. The obtained average δ44/40Ca (all relative to NIST SRM 915a) is 0.02 ± 0.02 ‰ (n=28), 0.72±0.04 ‰ (n=10) and 1.93±0.03 ‰ (n=21) for NIST SRM 915a, NIST SRM 915b and Pacific Seawater, respectively. The long-term reproducibility is 0.10‰ (2 δSD), which is comparable to the best external precision of 0.04 ‰ (2 δSD) of previous methods, but our sample throughputs are doubled with significant reduction in amount of spike used for single samples.

Cortical activity patterns predict speech discrimination ability

PubMed Central

Engineer, Crystal T; Perez, Claudia A; Chen, YeTing H; Carraway, Ryan S; Reed, Amanda C; Shetake, Jai A; Jakkamsetti, Vikram; Chang, Kevin Q; Kilgard, Michael P

2010-01-01

Neural activity in the cerebral cortex can explain many aspects of sensory perception. Extensive psychophysical and neurophysiological studies of visual motion and vibrotactile processing show that the firing rate of cortical neurons averaged across 50–500 ms is well correlated with discrimination ability. In this study, we tested the hypothesis that primary auditory cortex (A1) neurons use temporal precision on the order of 1–10 ms to represent speech sounds shifted into the rat hearing range. Neural discrimination was highly correlated with behavioral performance on 11 consonant-discrimination tasks when spike timing was preserved and was not correlated when spike timing was eliminated. This result suggests that spike timing contributes to the auditory cortex representation of consonant sounds. PMID:18425123

Feedback enhances feedforward figure-ground segmentation by changing firing mode.

PubMed

Supèr, Hans; Romeo, August

2011-01-01

In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforward spiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses with the responses to a homogenous texture. We propose that feedback controls figure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons.

Spike Timing and Reliability in Cortical Pyramidal Neurons: Effects of EPSC Kinetics, Input Synchronization and Background Noise on Spike Timing

PubMed Central

Rodriguez-Molina, Victor M.; Aertsen, Ad; Heck, Detlef H.

2007-01-01

In vivo studies have shown that neurons in the neocortex can generate action potentials at high temporal precision. The mechanisms controlling timing and reliability of action potential generation in neocortical neurons, however, are still poorly understood. Here we investigated the temporal precision and reliability of spike firing in cortical layer V pyramidal cells at near-threshold membrane potentials. Timing and reliability of spike responses were a function of EPSC kinetics, temporal jitter of population excitatory inputs, and of background synaptic noise. We used somatic current injection to mimic population synaptic input events and measured spike probability and spike time precision (STP), the latter defined as the time window (Δt) holding 80% of response spikes. EPSC rise and decay times were varied over the known physiological spectrum. At spike threshold level, EPSC decay time had a stronger influence on STP than rise time. Generally, STP was highest (≤2.45 ms) in response to synchronous compounds of EPSCs with fast rise and decay kinetics. Compounds with slow EPSC kinetics (decay time constants>6 ms) triggered spikes at lower temporal precision (≥6.58 ms). We found an overall linear relationship between STP and spike delay. The difference in STP between fast and slow compound EPSCs could be reduced by incrementing the amplitude of slow compound EPSCs. The introduction of a temporal jitter to compound EPSCs had a comparatively small effect on STP, with a tenfold increase in jitter resulting in only a five fold decrease in STP. In the presence of simulated synaptic background activity, precisely timed spikes could still be induced by fast EPSCs, but not by slow EPSCs. PMID:17389910

The Effect of Neural Noise on Spike Time Precision in a Detailed CA3 Neuron Model

PubMed Central

Kuriscak, Eduard; Marsalek, Petr; Stroffek, Julius; Wünsch, Zdenek

2012-01-01

Experimental and computational studies emphasize the role of the millisecond precision of neuronal spike times as an important coding mechanism for transmitting and representing information in the central nervous system. We investigate the spike time precision of a multicompartmental pyramidal neuron model of the CA3 region of the hippocampus under the influence of various sources of neuronal noise. We describe differences in the contribution to noise originating from voltage-gated ion channels, synaptic vesicle release, and vesicle quantal size. We analyze the effect of interspike intervals and the voltage course preceding the firing of spikes on the spike-timing jitter. The main finding of this study is the ranking of different noise sources according to their contribution to spike time precision. The most influential is synaptic vesicle release noise, causing the spike jitter to vary from 1 ms to 7 ms of a mean value 2.5 ms. Of second importance was the noise incurred by vesicle quantal size variation causing the spike time jitter to vary from 0.03 ms to 0.6 ms. Least influential was the voltage-gated channel noise generating spike jitter from 0.02 ms to 0.15 ms. PMID:22778784

Supervised learning with decision margins in pools of spiking neurons.

PubMed

Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre

2014-10-01

Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

Origin of heterogeneous spiking patterns from continuously distributed ion channel densities: a computational study in spinal dorsal horn neurons.

PubMed

Balachandar, Arjun; Prescott, Steven A

2018-05-01

Distinct spiking patterns may arise from qualitative differences in ion channel expression (i.e. when different neurons express distinct ion channels) and/or when quantitative differences in expression levels qualitatively alter the spike generation process. We hypothesized that spiking patterns in neurons of the superficial dorsal horn (SDH) of spinal cord reflect both mechanisms. We reproduced SDH neuron spiking patterns by varying densities of K V 1- and A-type potassium conductances. Plotting the spiking patterns that emerge from different density combinations revealed spiking-pattern regions separated by boundaries (bifurcations). This map suggests that certain spiking pattern combinations occur when the distribution of potassium channel densities straddle boundaries, whereas other spiking patterns reflect distinct patterns of ion channel expression. The former mechanism may explain why certain spiking patterns co-occur in genetically identified neuron types. We also present algorithms to predict spiking pattern proportions from ion channel density distributions, and vice versa. Neurons are often classified by spiking pattern. Yet, some neurons exhibit distinct patterns under subtly different test conditions, which suggests that they operate near an abrupt transition, or bifurcation. A set of such neurons may exhibit heterogeneous spiking patterns not because of qualitative differences in which ion channels they express, but rather because quantitative differences in expression levels cause neurons to operate on opposite sides of a bifurcation. Neurons in the spinal dorsal horn, for example, respond to somatic current injection with patterns that include tonic, single, gap, delayed and reluctant spiking. It is unclear whether these patterns reflect five cell populations (defined by distinct ion channel expression patterns), heterogeneity within a single population, or some combination thereof. We reproduced all five spiking patterns in a computational model by varying the densities of a low-threshold (K V 1-type) potassium conductance and an inactivating (A-type) potassium conductance and found that single, gap, delayed and reluctant spiking arise when the joint probability distribution of those channel densities spans two intersecting bifurcations that divide the parameter space into quadrants, each associated with a different spiking pattern. Tonic spiking likely arises from a separate distribution of potassium channel densities. These results argue in favour of two cell populations, one characterized by tonic spiking and the other by heterogeneous spiking patterns. We present algorithms to predict spiking pattern proportions based on ion channel density distributions and, conversely, to estimate ion channel density distributions based on spiking pattern proportions. The implications for classifying cells based on spiking pattern are discussed. © 2018 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

PubMed

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

PubMed Central

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B.; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. PMID:26217169

Spike timing precision of neuronal circuits.

PubMed

Kilinc, Deniz; Demir, Alper

2018-06-01

Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.

Endogenous GABA and Glutamate Finely Tune the Bursting of Olfactory Bulb External Tufted Cells

PubMed Central

Hayar, Abdallah; Ennis, Matthew

2008-01-01

In rat olfactory bulb slices, external tufted (ET) cells spontaneously generate spike bursts. Although ET cell bursting is intrinsically generated, its strength and precise timing may be regulated by synaptic input. We tested this hypothesis by analyzing whether the burst properties are modulated by activation of ionotropic γ-aminobutyric acid (GABA) and glutamate receptors. Blocking GABAA receptors increased—whereas blocking ionotropic glutamate receptors decreased—the number of spikes/burst without changing the interburst frequency. The GABAA agonist (isoguvacine, 10 μM) completely inhibited bursting or reduced the number of spikes/burst, suggesting a shunting effect. These findings indicate that the properties of ET cell spontaneous bursting are differentially controlled by GABAergic and glutamatergic fast synaptic transmission. We suggest that ET cell excitatory and inhibitory inputs may be encoded as a change in the pattern of spike bursting in ET cells, which together with mitral/tufted cells constitute the output circuit of the olfactory bulb. PMID:17567771

Endogenous GABA and glutamate finely tune the bursting of olfactory bulb external tufted cells.

PubMed

Hayar, Abdallah; Ennis, Matthew

2007-08-01

In rat olfactory bulb slices, external tufted (ET) cells spontaneously generate spike bursts. Although ET cell bursting is intrinsically generated, its strength and precise timing may be regulated by synaptic input. We tested this hypothesis by analyzing whether the burst properties are modulated by activation of ionotropic gamma-aminobutyric acid (GABA) and glutamate receptors. Blocking GABA(A) receptors increased--whereas blocking ionotropic glutamate receptors decreased--the number of spikes/burst without changing the interburst frequency. The GABA(A) agonist (isoguvacine, 10 microM) completely inhibited bursting or reduced the number of spikes/burst, suggesting a shunting effect. These findings indicate that the properties of ET cell spontaneous bursting are differentially controlled by GABAergic and glutamatergic fast synaptic transmission. We suggest that ET cell excitatory and inhibitory inputs may be encoded as a change in the pattern of spike bursting in ET cells, which together with mitral/tufted cells constitute the output circuit of the olfactory bulb.

Cellular basis for singing motor pattern generation in the field cricket (Gryllus bimaculatus DeGeer)

PubMed Central

Schöneich, Stefan; Hedwig, Berthold

2012-01-01

The singing behavior of male crickets allows analyzing a central pattern generator (CPG) that was shaped by sexual selection for reliable production of species-specific communication signals. After localizing the essential ganglia for singing in Gryllus bimaculatus, we now studied the calling song CPG at the cellular level. Fictive singing was initiated by pharmacological brain stimulation. The motor pattern underlying syllables and chirps was recorded as alternating spike bursts of wing-opener and wing-closer motoneurons in a truncated wing nerve; it precisely reflected the natural calling song. During fictive singing, we intracellularly recorded and stained interneurons in thoracic and abdominal ganglia and tested their impact on the song pattern by intracellular current injections. We identified three interneurons of the metathoracic and first unfused abdominal ganglion that rhythmically de- and hyperpolarized in phase with the syllable pattern and spiked strictly before the wing-opener motoneurons. Depolarizing current injection in two of these opener interneurons caused additional rhythmic singing activity, which reliably reset the ongoing chirp rhythm. The closely intermeshing arborizations of the singing interneurons revealed the dorsal midline neuropiles of the metathoracic and three most anterior abdominal neuromeres as the anatomical location of singing pattern generation. In the same neuropiles, we also recorded several closer interneurons that rhythmically hyper- and depolarized in the syllable rhythm and spiked strictly before the wing-closer motoneurons. Some of them received pronounced inhibition at the beginning of each chirp. Hyperpolarizing current injection in the dendrite revealed postinhibitory rebound depolarization as one functional mechanism of central pattern generation in singing crickets. PMID:23170234

Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night

PubMed Central

Muller, Lyle; Piantoni, Giovanni; Koller, Dominik; Cash, Sydney S; Halgren, Eric; Sejnowski, Terrence J

2016-01-01

During sleep, the thalamus generates a characteristic pattern of transient, 11-15 Hz sleep spindle oscillations, which synchronize the cortex through large-scale thalamocortical loops. Spindles have been increasingly demonstrated to be critical for sleep-dependent consolidation of memory, but the specific neural mechanism for this process remains unclear. We show here that cortical spindles are spatiotemporally organized into circular wave-like patterns, organizing neuronal activity over tens of milliseconds, within the timescale for storing memories in large-scale networks across the cortex via spike-time dependent plasticity. These circular patterns repeat over hours of sleep with millisecond temporal precision, allowing reinforcement of the activity patterns through hundreds of reverberations. These results provide a novel mechanistic account for how global sleep oscillations and synaptic plasticity could strengthen networks distributed across the cortex to store coherent and integrated memories. DOI: http://dx.doi.org/10.7554/eLife.17267.001 PMID:27855061

Implementing Signature Neural Networks with Spiking Neurons

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks. PMID:28066221

Implementing Signature Neural Networks with Spiking Neurons.

PubMed

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.

Cortical Action Potential Backpropagation Explains Spike Threshold Variability and Rapid-Onset Kinetics

PubMed Central

Yu, Yuguo; Shu, Yousheng; McCormick, David A.

2008-01-01

Neocortical action potential responses in vivo are characterized by considerable threshold variability, and thus timing and rate variability, even under seemingly identical conditions. This finding suggests that cortical ensembles are required for accurate sensorimotor integration and processing. Intracellularly, trial-to-trial variability results not only from variation in synaptic activities, but also in the transformation of these into patterns of action potentials. Through simultaneous axonal and somatic recordings and computational simulations, we demonstrate that the initiation of action potentials in the axon initial segment followed by backpropagation of these spikes throughout the neuron results in a distortion of the relationship between the timing of synaptic and action potential events. In addition, this backpropagation also results in an unusually high rate of rise of membrane potential at the foot of the action potential. The distortion of the relationship between the amplitude time course of synaptic inputs and action potential output caused by spike back-propagation results in the appearance of high spike threshold variability at the level of the soma. At the point of spike initiation, the axon initial segment, threshold variability is considerably less. Our results indicate that spike generation in cortical neurons is largely as expected by Hodgkin—Huxley theory and is more precise than previously thought. PMID:18632930

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

PubMed Central

Storchi, Riccardo; Zippo, Antonio G.; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E. M.

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role. PMID:22586452

Predicting spike occurrence and neuronal responsiveness from LFPs in primary somatosensory cortex.

PubMed

Storchi, Riccardo; Zippo, Antonio G; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E M

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

Uncovering Neuronal Networks Defined by Consistent Between-Neuron Spike Timing from Neuronal Spike Recordings

PubMed Central

2018-01-01

Abstract It is widely assumed that distributed neuronal networks are fundamental to the functioning of the brain. Consistent spike timing between neurons is thought to be one of the key principles for the formation of these networks. This can involve synchronous spiking or spiking with time delays, forming spike sequences when the order of spiking is consistent. Finding networks defined by their sequence of time-shifted spikes, denoted here as spike timing networks, is a tremendous challenge. As neurons can participate in multiple spike sequences at multiple between-spike time delays, the possible complexity of networks is prohibitively large. We present a novel approach that is capable of (1) extracting spike timing networks regardless of their sequence complexity, and (2) that describes their spiking sequences with high temporal precision. We achieve this by decomposing frequency-transformed neuronal spiking into separate networks, characterizing each network’s spike sequence by a time delay per neuron, forming a spike sequence timeline. These networks provide a detailed template for an investigation of the experimental relevance of their spike sequences. Using simulated spike timing networks, we show network extraction is robust to spiking noise, spike timing jitter, and partial occurrences of the involved spike sequences. Using rat multineuron recordings, we demonstrate the approach is capable of revealing real spike timing networks with sub-millisecond temporal precision. By uncovering spike timing networks, the prevalence, structure, and function of complex spike sequences can be investigated in greater detail, allowing us to gain a better understanding of their role in neuronal functioning. PMID:29789811

Potassium conductance dynamics confer robust spike-time precision in a neuromorphic model of the auditory brain stem

PubMed Central

Boahen, Kwabena

2013-01-01

A fundamental question in neuroscience is how neurons perform precise operations despite inherent variability. This question also applies to neuromorphic engineering, where low-power microchips emulate the brain using large populations of diverse silicon neurons. Biological neurons in the auditory pathway display precise spike timing, critical for sound localization and interpretation of complex waveforms such as speech, even though they are a heterogeneous population. Silicon neurons are also heterogeneous, due to a key design constraint in neuromorphic engineering: smaller transistors offer lower power consumption and more neurons per unit area of silicon, but also more variability between transistors and thus between silicon neurons. Utilizing this variability in a neuromorphic model of the auditory brain stem with 1,080 silicon neurons, we found that a low-voltage-activated potassium conductance (gKL) enables precise spike timing via two mechanisms: statically reducing the resting membrane time constant and dynamically suppressing late synaptic inputs. The relative contribution of these two mechanisms is unknown because blocking gKL in vitro eliminates dynamic adaptation but also lengthens the membrane time constant. We replaced gKL with a static leak in silico to recover the short membrane time constant and found that silicon neurons could mimic the spike-time precision of their biological counterparts, but only over a narrow range of stimulus intensities and biophysical parameters. The dynamics of gKL were required for precise spike timing robust to stimulus variation across a heterogeneous population of silicon neurons, thus explaining how neural and neuromorphic systems may perform precise operations despite inherent variability. PMID:23554436

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

PubMed

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Organic electronics for high-resolution electrocorticography of the human brain.

PubMed

Khodagholy, Dion; Gelinas, Jennifer N; Zhao, Zifang; Yeh, Malcolm; Long, Michael; Greenlee, Jeremy D; Doyle, Werner; Devinsky, Orrin; Buzsáki, György

2016-11-01

Localizing neuronal patterns that generate pathological brain signals may assist with tissue resection and intervention strategies in patients with neurological diseases. Precise localization requires high spatiotemporal recording from populations of neurons while minimizing invasiveness and adverse events. We describe a large-scale, high-density, organic material-based, conformable neural interface device ("NeuroGrid") capable of simultaneously recording local field potentials (LFPs) and action potentials from the cortical surface. We demonstrate the feasibility and safety of intraoperative recording with NeuroGrids in anesthetized and awake subjects. Highly localized and propagating physiological and pathological LFP patterns were recorded, and correlated neural firing provided evidence about their local generation. Application of NeuroGrids to brain disorders, such as epilepsy, may improve diagnostic precision and therapeutic outcomes while reducing complications associated with invasive electrodes conventionally used to acquire high-resolution and spiking data.

Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform

PubMed Central

Giulioni, Massimiliano; Lagorce, Xavier; Galluppi, Francesco; Benosman, Ryad B.

2016-01-01

Estimating the speed and direction of moving objects is a crucial component of agents behaving in a dynamic world. Biological organisms perform this task by means of the neural connections originating from their retinal ganglion cells. In artificial systems the optic flow is usually extracted by comparing activity of two or more frames captured with a vision sensor. Designing artificial motion flow detectors which are as fast, robust, and efficient as the ones found in biological systems is however a challenging task. Inspired by the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina, we introduce an architecture for robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission, and the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. Mimicking ganglion cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. Hereby we describe the architectural aspects, discuss its latency, scalability, and robustness properties and demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene. This work shows how precise time of spike emission used as a computational basis, biological inspiration, and neuromorphic systems can be used together for solving specific tasks. PMID:26909015

Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform.

PubMed

Giulioni, Massimiliano; Lagorce, Xavier; Galluppi, Francesco; Benosman, Ryad B

2016-01-01

Estimating the speed and direction of moving objects is a crucial component of agents behaving in a dynamic world. Biological organisms perform this task by means of the neural connections originating from their retinal ganglion cells. In artificial systems the optic flow is usually extracted by comparing activity of two or more frames captured with a vision sensor. Designing artificial motion flow detectors which are as fast, robust, and efficient as the ones found in biological systems is however a challenging task. Inspired by the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina, we introduce an architecture for robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission, and the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. Mimicking ganglion cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. Hereby we describe the architectural aspects, discuss its latency, scalability, and robustness properties and demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene. This work shows how precise time of spike emission used as a computational basis, biological inspiration, and neuromorphic systems can be used together for solving specific tasks.

Statistical evaluation of synchronous spike patterns extracted by frequent item set mining

PubMed Central

Torre, Emiliano; Picado-Muiño, David; Denker, Michael; Borgelt, Christian; Grün, Sonja

2013-01-01

We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of their signatures, defined as the pairs of pattern size z and support c. Here, we derive in detail a statistical test for the significance of the signatures under the null hypothesis of full independence (pattern spectrum filtering, PSF) by means of surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected, yielding a low false negative rate. However, this approach is prone to additionally classify patterns resulting from chance overlap of real assembly activity and background spiking as significant. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains. PMID:24167487

Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents

PubMed Central

Chagas, André M.; Theis, Lucas; Sengupta, Biswa; Stüttgen, Maik C.; Bethge, Matthias; Schwarz, Cornelius

2013-01-01

Sensory receptors determine the type and the quantity of information available for perception. Here, we quantified and characterized the information transferred by primary afferents in the rat whisker system using neural system identification. Quantification of “how much” information is conveyed by primary afferents, using the direct method (DM), a classical information theoretic tool, revealed that primary afferents transfer huge amounts of information (up to 529 bits/s). Information theoretic analysis of instantaneous spike-triggered kinematic stimulus features was used to gain functional insight on “what” is coded by primary afferents. Amongst the kinematic variables tested—position, velocity, and acceleration—primary afferent spikes encoded velocity best. The other two variables contributed to information transfer, but only if combined with velocity. We further revealed three additional characteristics that play a role in information transfer by primary afferents. Firstly, primary afferent spikes show preference for well separated multiple stimuli (i.e., well separated sets of combinations of the three instantaneous kinematic variables). Secondly, neurons are sensitive to short strips of the stimulus trajectory (up to 10 ms pre-spike time), and thirdly, they show spike patterns (precise doublet and triplet spiking). In order to deal with these complexities, we used a flexible probabilistic neuron model fitting mixtures of Gaussians to the spike triggered stimulus distributions, which quantitatively captured the contribution of the mentioned features and allowed us to achieve a full functional analysis of the total information rate indicated by the DM. We found that instantaneous position, velocity, and acceleration explained about 50% of the total information rate. Adding a 10 ms pre-spike interval of stimulus trajectory achieved 80–90%. The final 10–20% were found to be due to non-linear coding by spike bursts. PMID:24367295

Distinct neuronal coding schemes in memory revealed by selective erasure of fast synchronous synaptic transmission.

PubMed

Xu, Wei; Morishita, Wade; Buckmaster, Paul S; Pang, Zhiping P; Malenka, Robert C; Südhof, Thomas C

2012-03-08

Neurons encode information by firing spikes in isolation or bursts and propagate information by spike-triggered neurotransmitter release that initiates synaptic transmission. Isolated spikes trigger neurotransmitter release unreliably but with high temporal precision. In contrast, bursts of spikes trigger neurotransmission reliably (i.e., boost transmission fidelity), but the resulting synaptic responses are temporally imprecise. However, the relative physiological importance of different spike-firing modes remains unclear. Here, we show that knockdown of synaptotagmin-1, the major Ca(2+) sensor for neurotransmitter release, abrogated neurotransmission evoked by isolated spikes but only delayed, without abolishing, neurotransmission evoked by bursts of spikes. Nevertheless, knockdown of synaptotagmin-1 in the hippocampal CA1 region did not impede acquisition of recent contextual fear memories, although it did impair the precision of such memories. In contrast, knockdown of synaptotagmin-1 in the prefrontal cortex impaired all remote fear memories. These results indicate that different brain circuits and types of memory employ distinct spike-coding schemes to encode and transmit information. Copyright © 2012 Elsevier Inc. All rights reserved.

Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity

PubMed Central

Waddington, Amelia; Appleby, Peter A.; De Kamps, Marc; Cohen, Netta

2012-01-01

Synfire chains have long been proposed to generate precisely timed sequences of neural activity. Such activity has been linked to numerous neural functions including sensory encoding, cognitive and motor responses. In particular, it has been argued that synfire chains underlie the precise spatiotemporal firing patterns that control song production in a variety of songbirds. Previous studies have suggested that the development of synfire chains requires either initial sparse connectivity or strong topological constraints, in addition to any synaptic learning rules. Here, we show that this necessity can be removed by using a previously reported but hitherto unconsidered spike-timing-dependent plasticity (STDP) rule and activity-dependent excitability. Under this rule the network develops stable synfire chains that possess a non-trivial, scalable multi-layer structure, in which relative layer sizes appear to follow a universal function. Using computational modeling and a coarse grained random walk model, we demonstrate the role of the STDP rule in growing, molding and stabilizing the chain, and link model parameters to the resulting structure. PMID:23162457

Cortical Inhibition Reduces Information Redundancy at Presentation of Communication Sounds in the Primary Auditory Cortex

PubMed Central

Gaucher, Quentin; Huetz, Chloé; Gourévitch, Boris

2013-01-01

In all sensory modalities, intracortical inhibition shapes the functional properties of cortical neurons but also influences the responses to natural stimuli. Studies performed in various species have revealed that auditory cortex neurons respond to conspecific vocalizations by temporal spike patterns displaying a high trial-to-trial reliability, which might result from precise timing between excitation and inhibition. Studying the guinea pig auditory cortex, we show that partial blockage of GABAA receptors by gabazine (GBZ) application (10 μm, a concentration that promotes expansion of cortical receptive fields) increased the evoked firing rate and the spike-timing reliability during presentation of communication sounds (conspecific and heterospecific vocalizations), whereas GABAB receptor antagonists [10 μm saclofen; 10–50 μm CGP55845 (p-3-aminopropyl-p-diethoxymethyl phosphoric acid)] had nonsignificant effects. Computing mutual information (MI) from the responses to vocalizations using either the evoked firing rate or the temporal spike patterns revealed that GBZ application increased the MI derived from the activity of single cortical site but did not change the MI derived from population activity. In addition, quantification of information redundancy showed that GBZ significantly increased redundancy at the population level. This result suggests that a potential role of intracortical inhibition is to reduce information redundancy during the processing of natural stimuli. PMID:23804094

Modulation of Temporal Precision in Thalamic Population Responses to Natural Visual Stimuli

PubMed Central

Desbordes, Gaëlle; Jin, Jianzhong; Alonso, Jose-Manuel; Stanley, Garrett B.

2010-01-01

Natural visual stimuli have highly structured spatial and temporal properties which influence the way visual information is encoded in the visual pathway. In response to natural scene stimuli, neurons in the lateral geniculate nucleus (LGN) are temporally precise – on a time scale of 10–25 ms – both within single cells and across cells within a population. This time scale, established by non stimulus-driven elements of neuronal firing, is significantly shorter than that of natural scenes, yet is critical for the neural representation of the spatial and temporal structure of the scene. Here, a generalized linear model (GLM) that combines stimulus-driven elements with spike-history dependence associated with intrinsic cellular dynamics is shown to predict the fine timing precision of LGN responses to natural scene stimuli, the corresponding correlation structure across nearby neurons in the population, and the continuous modulation of spike timing precision and latency across neurons. A single model captured the experimentally observed neural response, across different levels of contrasts and different classes of visual stimuli, through interactions between the stimulus correlation structure and the nonlinearity in spike generation and spike history dependence. Given the sensitivity of the thalamocortical synapse to closely timed spikes and the importance of fine timing precision for the faithful representation of natural scenes, the modulation of thalamic population timing over these time scales is likely important for cortical representations of the dynamic natural visual environment. PMID:21151356

Time and Category Information in Pattern-Based Codes

PubMed Central

Eyherabide, Hugo Gabriel; Samengo, Inés

2010-01-01

Sensory stimuli are usually composed of different features (the what) appearing at irregular times (the when). Neural responses often use spike patterns to represent sensory information. The what is hypothesized to be encoded in the identity of the elicited patterns (the pattern categories), and the when, in the time positions of patterns (the pattern timing). However, this standard view is oversimplified. In the real world, the what and the when might not be separable concepts, for instance, if they are correlated in the stimulus. In addition, neuronal dynamics can condition the pattern timing to be correlated with the pattern categories. Hence, timing and categories of patterns may not constitute independent channels of information. In this paper, we assess the role of spike patterns in the neural code, irrespective of the nature of the patterns. We first define information-theoretical quantities that allow us to quantify the information encoded by different aspects of the neural response. We also introduce the notion of synergy/redundancy between time positions and categories of patterns. We subsequently establish the relation between the what and the when in the stimulus with the timing and the categories of patterns. To that aim, we quantify the mutual information between different aspects of the stimulus and different aspects of the response. This formal framework allows us to determine the precise conditions under which the standard view holds, as well as the departures from this simple case. Finally, we study the capability of different response aspects to represent the what and the when in the neural response. PMID:21151371

Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

PubMed Central

Bi, Zedong; Zhou, Changsong

2016-01-01

Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy). PMID:27555816

Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum.

PubMed

Ponzi, Adam; Wickens, Jeff

2010-04-28

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

Neural noise and movement-related codes in the macaque supplementary motor area.

PubMed

Averbeck, Bruno B; Lee, Daeyeol

2003-08-20

We analyzed the variability of spike counts and the coding capacity of simultaneously recorded pairs of neurons in the macaque supplementary motor area (SMA). We analyzed the mean-variance functions for single neurons, as well as signal and noise correlations between pairs of neurons. All three statistics showed a strong dependence on the bin width chosen for analysis. Changes in the correlation structure of single neuron spike trains over different bin sizes affected the mean-variance function, and signal and noise correlations between pairs of neurons were much smaller at small bin widths, increasing monotonically with the width of the bin. Analyses in the frequency domain showed that the noise between pairs of neurons, on average, was most strongly correlated at low frequencies, which explained the increase in noise correlation with increasing bin width. The coding performance was analyzed to determine whether the temporal precision of spike arrival times and the interactions within and between neurons could improve the prediction of the upcoming movement. We found that in approximately 62% of neuron pairs, the arrival times of spikes at a resolution between 66 and 40 msec carried more information than spike counts in a 200 msec bin. In addition, in 19% of neuron pairs, inclusion of within (11%)- or between-neuron (8%) correlations in spike trains improved decoding accuracy. These results suggest that in some SMA neurons elements of the spatiotemporal pattern of activity may be relevant for neural coding.

Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method.

PubMed

Xie, Xiurui; Qu, Hong; Yi, Zhang; Kurths, Jurgen

2017-06-01

The spiking neural network (SNN) is the third generation of neural networks and performs remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode mechanism found in biological hippocampus enables SNN to possess more powerful computation capability than networks with other encoding schemes. However, this temporal encoding approach requires neurons to process information serially on time, which reduces learning efficiency significantly. To keep the powerful computation capability of the temporal encoding mechanism and to overcome its low efficiency in the training of SNNs, a new training algorithm, the accurate synaptic-efficiency adjustment method is proposed in this paper. Inspired by the selective attention mechanism of the primate visual system, our algorithm selects only the target spike time as attention areas, and ignores voltage states of the untarget ones, resulting in a significant reduction of training time. Besides, our algorithm employs a cost function based on the voltage difference between the potential of the output neuron and the firing threshold of the SNN, instead of the traditional precise firing time distance. A normalized spike-timing-dependent-plasticity learning window is applied to assigning this error to different synapses for instructing their training. Comprehensive simulations are conducted to investigate the learning properties of our algorithm, with input neurons emitting both single spike and multiple spikes. Simulation results indicate that our algorithm possesses higher learning performance than the existing other methods and achieves the state-of-the-art efficiency in the training of SNN.

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding

PubMed Central

Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-01-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information. PMID:27304526

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding.

PubMed

Huang, Chao; Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-06-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.

Hysteresis, neural avalanches, and critical behavior near a first-order transition of a spiking neural network

NASA Astrophysics Data System (ADS)

Scarpetta, Silvia; Apicella, Ilenia; Minati, Ludovico; de Candia, Antonio

2018-06-01

Many experimental results, both in vivo and in vitro, support the idea that the brain cortex operates near a critical point and at the same time works as a reservoir of precise spatiotemporal patterns. However, the mechanism at the basis of these observations is still not clear. In this paper we introduce a model which combines both these features, showing that scale-free avalanches are the signature of a system posed near the spinodal line of a first-order transition, with many spatiotemporal patterns stored as dynamical metastable attractors. Specifically, we studied a network of leaky integrate-and-fire neurons whose connections are the result of the learning of multiple spatiotemporal dynamical patterns, each with a randomly chosen ordering of the neurons. We found that the network shows a first-order transition between a low-spiking-rate disordered state (down), and a high-rate state characterized by the emergence of collective activity and the replay of one of the stored patterns (up). The transition is characterized by hysteresis, or alternation of up and down states, depending on the lifetime of the metastable states. In both cases, critical features and neural avalanches are observed. Notably, critical phenomena occur at the edge of a discontinuous phase transition, as recently observed in a network of glow lamps.

STDP allows fast rate-modulated coding with Poisson-like spike trains.

PubMed

Gilson, Matthieu; Masquelier, Timothée; Hugues, Etienne

2011-10-01

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (~10-20 ms) for sufficiently many inputs (~100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks.

STDP Allows Fast Rate-Modulated Coding with Poisson-Like Spike Trains

PubMed Central

Hugues, Etienne

2011-01-01

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (∼10–20 ms) for sufficiently many inputs (∼100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks. PMID:22046113

Neuronal Ensemble Synchrony during Human Focal Seizures

PubMed Central

Ahmed, Omar J.; Harrison, Matthew T.; Eskandar, Emad N.; Cosgrove, G. Rees; Madsen, Joseph R.; Blum, Andrew S.; Potter, N. Stevenson; Hochberg, Leigh R.; Cash, Sydney S.

2014-01-01

Seizures are classically characterized as the expression of hypersynchronous neural activity, yet the true degree of synchrony in neuronal spiking (action potentials) during human seizures remains a fundamental question. We quantified the temporal precision of spike synchrony in ensembles of neocortical neurons during seizures in people with pharmacologically intractable epilepsy. Two seizure types were analyzed: those characterized by sustained gamma (∼40–60 Hz) local field potential (LFP) oscillations or by spike-wave complexes (SWCs; ∼3 Hz). Fine (<10 ms) temporal synchrony was rarely present during gamma-band seizures, where neuronal spiking remained highly irregular and asynchronous. In SWC seizures, phase locking of neuronal spiking to the SWC spike phase induced synchrony at a coarse 50–100 ms level. In addition, transient fine synchrony occurred primarily during the initial ∼20 ms period of the SWC spike phase and varied across subjects and seizures. Sporadic coherence events between neuronal population spike counts and LFPs were observed during SWC seizures in high (∼80 Hz) gamma-band and during high-frequency oscillations (∼130 Hz). Maximum entropy models of the joint neuronal spiking probability, constrained only on single neurons' nonstationary coarse spiking rates and local network activation, explained most of the fine synchrony in both seizure types. Our findings indicate that fine neuronal ensemble synchrony occurs mostly during SWC, not gamma-band, seizures, and primarily during the initial phase of SWC spikes. Furthermore, these fine synchrony events result mostly from transient increases in overall neuronal network spiking rates, rather than changes in precise spiking correlations between specific pairs of neurons. PMID:25057195

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

PubMed

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation. Copyright © 2015 the authors 0270-6474/15/3510112-23$15.00/0.

Drift in Neural Population Activity Causes Working Memory to Deteriorate Over Time.

PubMed

Schneegans, Sebastian; Bays, Paul M

2018-05-23

Short-term memories are thought to be maintained in the form of sustained spiking activity in neural populations. Decreases in recall precision observed with increasing number of memorized items can be accounted for by a limit on total spiking activity, resulting in fewer spikes contributing to the representation of each individual item. Longer retention intervals likewise reduce recall precision, but it is unknown what changes in population activity produce this effect. One possibility is that spiking activity becomes attenuated over time, such that the same mechanism accounts for both effects of set size and retention duration. Alternatively, reduced performance may be caused by drift in the encoded value over time, without a decrease in overall spiking activity. Human participants of either sex performed a variable-delay cued recall task with a saccadic response, providing a precise measure of recall latency. Based on a spike integration model of decision making, if the effects of set size and retention duration are both caused by decreased spiking activity, we would predict a fixed relationship between recall precision and response latency across conditions. In contrast, the drift hypothesis predicts no systematic changes in latency with increasing delays. Our results show both an increase in latency with set size, and a decrease in response precision with longer delays within each set size, but no systematic increase in latency for increasing delay durations. These results were quantitatively reproduced by a model based on a limited neural resource in which working memories drift rather than decay with time. SIGNIFICANCE STATEMENT Rapid deterioration over seconds is a defining feature of short-term memory, but what mechanism drives this degradation of internal representations? Here, we extend a successful population coding model of working memory by introducing possible mechanisms of delay effects. We show that a decay in neural signal over time predicts that the time required for memory retrieval will increase with delay, whereas a random drift in the stored value predicts no effect of delay on retrieval time. Testing these predictions in a multi-item memory task with an eye movement response, we identified drift as a key mechanism of memory decline. These results provide evidence for a dynamic spiking basis for working memory, in contrast to recent proposals of activity-silent storage. Copyright © 2018 Schneegans and Bays.

Dynamic balance of excitation and inhibition rapidly modulates spike probability and precision in feed-forward hippocampal circuits

PubMed Central

Wahlstrom-Helgren, Sarah

2016-01-01

Feed-forward inhibitory (FFI) circuits are important for many information-processing functions. FFI circuit operations critically depend on the balance and timing between the excitatory and inhibitory components, which undergo rapid dynamic changes during neural activity due to short-term plasticity (STP) of both components. How dynamic changes in excitation/inhibition (E/I) balance during spike trains influence FFI circuit operations remains poorly understood. In the current study we examined the role of STP in the FFI circuit functions in the mouse hippocampus. Using a coincidence detection paradigm with simultaneous activation of two Schaffer collateral inputs, we found that the spiking probability in the target CA1 neuron was increased while spike precision concomitantly decreased during high-frequency bursts compared with a single spike. Blocking inhibitory synaptic transmission revealed that dynamics of inhibition predominately modulates the spike precision but not the changes in spiking probability, whereas the latter is modulated by the dynamics of excitation. Further analyses combining whole cell recordings and simulations of the FFI circuit suggested that dynamics of the inhibitory circuit component may influence spiking behavior during bursts by broadening the width of excitatory postsynaptic responses and that the strength of this modulation depends on the basal E/I ratio. We verified these predictions using a mouse model of fragile X syndrome, which has an elevated E/I ratio, and found a strongly reduced modulation of postsynaptic response width during bursts. Our results suggest that changes in the dynamics of excitatory and inhibitory circuit components due to STP play important yet distinct roles in modulating the properties of FFI circuits. PMID:27605532

Phasic spike patterning in rat supraoptic neurones in vivo and in vitro

PubMed Central

Sabatier, Nancy; Brown, Colin H; Ludwig, Mike; Leng, Gareth

2004-01-01

In vivo, most vasopressin cells of the hypothalamic supraoptic nucleus fire action potentials in a ‘phasic’ pattern when the systemic osmotic pressure is elevated, while most oxytocin cells fire continuously. The phasic firing pattern is believed to arise as a consequence of intrinsic activity-dependent changes in membrane potential, and these have been extensively studied in vitro. Here we analysed the discharge patterning of supraoptic nucleus neurones in vivo, to infer the characteristics of the post-spike sequence of hyperpolarization and depolarization from the observed spike patterning. We then compared patterning in phasic cells in vivo and in vitro, and we found systematic differences in the interspike interval distributions, and in other statistical parameters that characterized activity patterns within bursts. Analysis of hazard functions (probability of spike initiation as a function of time since the preceding spike) revealed that phasic firing in vitro appears consistent with a regenerative process arising from a relatively slow, late depolarizing afterpotential that approaches or exceeds spike threshold. By contrast, in vivo activity appears to be dominated by stochastic rather than deterministic mechanisms, and appears consistent with a relatively early and fast depolarizing afterpotential that modulates the probability that random synaptic input exceeds spike threshold. Despite superficial similarities in the phasic firing patterns observed in vivo and in vitro, there are thus fundamental differences in the underlying mechanisms. PMID:15146047

The effects of visual stimulation and selective visual attention on rhythmic neuronal synchronization in macaque area V4.

PubMed

Fries, Pascal; Womelsdorf, Thilo; Oostenveld, Robert; Desimone, Robert

2008-04-30

Selective attention lends relevant sensory input priority access to higher-level brain areas and ultimately to behavior. Recent studies have suggested that those neurons in visual areas that are activated by an attended stimulus engage in enhanced gamma-band (30-70 Hz) synchronization compared with neurons activated by a distracter. Such precise synchronization could enhance the postsynaptic impact of cells carrying behaviorally relevant information. Previous studies have used the local field potential (LFP) power spectrum or spike-LFP coherence (SFC) to indirectly estimate spike synchronization. Here, we directly demonstrate zero-phase gamma-band coherence among spike trains of V4 neurons. This synchronization was particularly evident during visual stimulation and enhanced by selective attention, thus confirming the pattern inferred from LFP power and SFC. We therefore investigated the time course of LFP gamma-band power and found rapid dynamics consistent with interactions of top-down spatial and feature attention with bottom-up saliency. In addition to the modulation of synchronization during visual stimulation, selective attention significantly changed the prestimulus pattern of synchronization. Attention inside the receptive field of the recorded neuronal population enhanced gamma-band synchronization and strongly reduced alpha-band (9-11 Hz) synchronization in the prestimulus period. These results lend further support for a functional role of rhythmic neuronal synchronization in attentional stimulus selection.

Neural coding of repetitive clicks in the medial geniculate body of cat.

PubMed

Rouiller, E; de Ribaupierre, Y; Toros-Morel, A; de Ribaupierre, F

1981-09-01

The activity of 418 medial geniculate body (MGB) units was studied in response to repetitive acoustic pulses in 35 nitrous oxide anaesthetized cats. The proportion of MGB neurons insensitive to repetitive clicks was close to 30%. On the basis of their pattern of discharge, the responsive units were divided into three categories. The majority of them (71%), classified as "lockers', showed discharges precisely time-locked to the individual clicks of the train. A few units (8%), called "groupers', had discharges loosely synchronized to low-rate repetitive clicks. When the spikes were not synchronized, the cell had transient or sustained responses for a limited frequency range and was classified as a "special responder' (21%). Responses of "lockers' were time-locked up to a limiting rate, which varied between 10 and 800 Hz; half of the "lockers' had a limiting rate of locking equal to or higher than 100 Hz. The degree of entrainment, defined as the probability that each click evokes at least one spike, regularly decreases for increasing rates; on the other hand, the precision of locking increasing increases with frequency. The time jitter observed at 100 Hz might be as small as 0.2 ms and was 1.2 ms on average. The population of "lockers' can mark with precision the transients of complex sounds and has response properties still compatible with a temporal coding of the fundamental frequency of most animal vocalizations.

Data-Driven Significance Estimation for Precise Spike Correlation

PubMed Central

Grün, Sonja

2009-01-01

The mechanisms underlying neuronal coding and, in particular, the role of temporal spike coordination are hotly debated. However, this debate is often confounded by an implicit discussion about the use of appropriate analysis methods. To avoid incorrect interpretation of data, the analysis of simultaneous spike trains for precise spike correlation needs to be properly adjusted to the features of the experimental spike trains. In particular, nonstationarity of the firing of individual neurons in time or across trials, a spike train structure deviating from Poisson, or a co-occurrence of such features in parallel spike trains are potent generators of false positives. Problems can be avoided by including these features in the null hypothesis of the significance test. In this context, the use of surrogate data becomes increasingly important, because the complexity of the data typically prevents analytical solutions. This review provides an overview of the potential obstacles in the correlation analysis of parallel spike data and possible routes to overcome them. The discussion is illustrated at every stage of the argument by referring to a specific analysis tool (the Unitary Events method). The conclusions, however, are of a general nature and hold for other analysis techniques. Thorough testing and calibration of analysis tools and the impact of potentially erroneous preprocessing stages are emphasized. PMID:19129298

Clusterless Decoding of Position From Multiunit Activity Using A Marked Point Process Filter

PubMed Central

Deng, Xinyi; Liu, Daniel F.; Kay, Kenneth; Frank, Loren M.; Eden, Uri T.

2016-01-01

Point process filters have been applied successfully to decode neural signals and track neural dynamics. Traditionally, these methods assume that multiunit spiking activity has already been correctly spike-sorted. As a result, these methods are not appropriate for situations where sorting cannot be performed with high precision such as real-time decoding for brain-computer interfaces. As the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights about clusterless decoding. Here we present a new point process decoding algorithm that does not require multiunit signals to be sorted into individual units. We use the theory of marked point processes to construct a function that characterizes the relationship between a covariate of interest (in this case, the location of a rat on a track) and features of the spike waveforms. In our example, we use tetrode recordings, and the marks represent a four-dimensional vector of the maximum amplitudes of the spike waveform on each of the four electrodes. In general, the marks may represent any features of the spike waveform. We then use Bayes’ rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and with experimental data recorded in the hippocampus of a rat moving through a linear environment. Our decoding algorithm accurately reconstructs the rat’s position from unsorted multiunit spiking activity. We then compare the quality of our decoding algorithm to that of a traditional spike-sorting and decoding algorithm. Our analyses show that the proposed decoding algorithm performs equivalently or better than algorithms based on sorted single-unit activity. These results provide a path toward accurate real-time decoding of spiking patterns that could be used to carry out content-specific manipulations of population activity in hippocampus or elsewhere in the brain. PMID:25973549

Spatial representation and cognitive modulation of response variability in the lateral intraparietal area priority map.

PubMed

Falkner, Annegret L; Goldberg, Michael E; Krishna, B Suresh

2013-10-09

The lateral intraparietal area (LIP) in the macaque contains a priority-based representation of the visual scene. We previously showed that the mean spike rate of LIP neurons is strongly influenced by spatially wide-ranging surround suppression in a manner that effectively sharpens the priority map. Reducing response variability can also improve the precision of LIP's priority map. We show that when a monkey plans a visually guided delayed saccade with an intervening distractor, variability (measured by the Fano factor) decreases both for neurons representing the saccade goal and for neurons representing the broad spatial surround. The reduction in Fano factor is maximal for neurons representing the saccade goal and steadily decreases for neurons representing more distant locations. LIP Fano factor changes are behaviorally significant: increasing expected reward leads to lower variability for the LIP representation of both the target and distractor locations, and trials with shorter latency saccades are associated with lower Fano factors in neurons representing the surround. Thus, the LIP Fano factor reflects both stimulus and behavioral engagement. Quantitative modeling shows that the interaction between mean spike count and target-receptive field (RF) distance in the surround during the predistractor epoch is multiplicative: the Fano factor increases more steeply with mean spike count further away from the RF. A negative-binomial model for LIP spike counts captures these findings quantitatively, suggests underlying mechanisms based on trial-by-trial variations in mean spike rate or burst-firing patterns, and potentially provides a principled framework to account simultaneously for the previously observed unsystematic relationships between spike rate and variability in different brain areas.

A new approach to spike sorting for multi-neuronal activities recorded with a tetrode--how ICA can be practical.

PubMed

Takahashi, Susumu; Anzai, Yuichiro; Sakurai, Yoshio

2003-07-01

Multi-neuronal recording with a tetrode is a powerful technique to reveal neuronal interactions in local circuits. However, it is difficult to detect precise spike timings among closely neighboring neurons because the spike waveforms of individual neurons overlap on the electrode when more than two neurons fire simultaneously. In addition, the spike waveforms of single neurons, especially in the presence of complex spikes, are often non-stationary. These problems limit the ability of ordinary spike sorting to sort multi-neuronal activities recorded using tetrodes into their single-neuron components. Though sorting with independent component analysis (ICA) can solve these problems, it has one serious limitation that the number of separated neurons must be less than the number of electrodes. Using a combination of ICA and the efficiency of ordinary spike sorting technique (k-means clustering), we developed an automatic procedure to solve the spike-overlapping and the non-stationarity problems with no limitation on the number of separated neurons. The results for the procedure applied to real multi-neuronal data demonstrated that some outliers which may be assigned to distinct clusters if ordinary spike-sorting methods were used can be identified as overlapping spikes, and that there are functional connections between a putative pyramidal neuron and its putative dendrite. These findings suggest that the combination of ICA and k-means clustering can provide insights into the precise nature of functional circuits among neurons, i.e. cell assemblies.

Mimickers of generalized spike and wave discharges.

PubMed

Azzam, Raed; Bhatt, Amar B

2014-06-01

Overinterpretation of benign EEG variants is a common problem that can lead to the misdiagnosis of epilepsy. We review four normal patterns that mimic generalized spike and wave discharges: phantom spike-and-wave, hyperventilation hypersynchrony, hypnagogic/ hypnopompic hypersynchrony, and mitten patterns.

Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields

PubMed Central

Lee, Christopher M.; Osman, Ahmad F.; Volgushev, Maxim; Escabí, Monty A.

2016-01-01

Mammals perceive a wide range of temporal cues in natural sounds, and the auditory cortex is essential for their detection and discrimination. The rat primary (A1), ventral (VAF), and caudal suprarhinal (cSRAF) auditory cortical fields have separate thalamocortical pathways that may support unique temporal cue sensitivities. To explore this, we record responses of single neurons in the three fields to variations in envelope shape and modulation frequency of periodic noise sequences. Spike rate, relative synchrony, and first-spike latency metrics have previously been used to quantify neural sensitivities to temporal sound cues; however, such metrics do not measure absolute spike timing of sustained responses to sound shape. To address this, in this study we quantify two forms of spike-timing precision, jitter, and reliability. In all three fields, we find that jitter decreases logarithmically with increase in the basis spline (B-spline) cutoff frequency used to shape the sound envelope. In contrast, reliability decreases logarithmically with increase in sound envelope modulation frequency. In A1, jitter and reliability vary independently, whereas in ventral cortical fields, jitter and reliability covary. Jitter time scales increase (A1 < VAF < cSRAF) and modulation frequency upper cutoffs decrease (A1 > VAF > cSRAF) with ventral progression from A1. These results suggest a transition from independent encoding of shape and periodicity sound cues on short time scales in A1 to a joint encoding of these same cues on longer time scales in ventral nonprimary cortices. PMID:26843599

Extracting information in spike time patterns with wavelets and information theory.

PubMed

Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E; Quian Quiroga, Rodrigo

2015-02-01

We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information. Copyright © 2015 the American Physiological Society.

Pyramidal cell-interneuron interactions underlie hippocampal ripple oscillations.

PubMed

Stark, Eran; Roux, Lisa; Eichler, Ronny; Senzai, Yuta; Royer, Sebastien; Buzsáki, György

2014-07-16

High-frequency ripple oscillations, observed most prominently in the hippocampal CA1 pyramidal layer, are associated with memory consolidation. The cellular and network mechanisms underlying the generation, frequency control, and spatial coherence of the rhythm are poorly understood. Using multisite optogenetic manipulations in freely behaving rodents, we found that depolarization of a small group of nearby pyramidal cells was sufficient to induce high-frequency oscillations, whereas closed-loop silencing of pyramidal cells or activation of parvalbumin- (PV) or somatostatin-immunoreactive interneurons aborted spontaneously occurring ripples. Focal pharmacological blockade of GABAA receptors abolished ripples. Localized PV interneuron activation paced ensemble spiking, and simultaneous induction of high-frequency oscillations at multiple locations resulted in a temporally coherent pattern mediated by phase-locked interneuron spiking. These results constrain competing models of ripple generation and indicate that temporally precise local interactions between excitatory and inhibitory neurons support ripple generation in the intact hippocampus. Copyright © 2014 Elsevier Inc. All rights reserved.

Pyramidal Cell-Interneuron Interactions Underlie Hippocampal Ripple Oscillations

PubMed Central

Stark, Eran; Roux, Lisa; Eichler, Ronny; Senzai, Yuta; Royer, Sebastien; Buzsáki, György

2015-01-01

SUMMARY High-frequency ripple oscillations, observed most prominently in the hippocampal CA1 pyramidal layer, are associated with memory consolidation. The cellular and network mechanisms underlying the generation, frequency control, and spatial coherence of the rhythm are poorly understood. Using multisite optogenetic manipulations in freely behaving rodents, we found that depolarization of a small group of nearby pyramidal cells was sufficient to induce high-frequency oscillations, whereas closed-loop silencing of pyramidal cells or activation of parvalbumin-(PV) or somatostatin-immunoreactive interneurons aborted spontaneously occurring ripples. Focal pharmacological blockade of GABAA receptors abolished ripples. Localized PV inter-neuron activation paced ensemble spiking, and simultaneous induction of high-frequency oscillations at multiple locations resulted in a temporally coherent pattern mediated by phase-locked inter-neuron spiking. These results constrain competing models of ripple generation and indicate that temporally precise local interactions between excitatory and inhibitory neurons support ripple generation in the intact hippocampus. PMID:25033186

Accelerated spike resampling for accurate multiple testing controls.

PubMed

Harrison, Matthew T

2013-02-01

Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using permutation tests and for testing pairwise synchrony and precise lagged-correlation between many simultaneously recorded spike trains using interval jitter.

Automated analysis of calcium spiking profiles with CaSA software: two case studies from root-microbe symbioses.

PubMed

Russo, Giulia; Spinella, Salvatore; Sciacca, Eva; Bonfante, Paola; Genre, Andrea

2013-12-26

Repeated oscillations in intracellular calcium (Ca2+) concentration, known as Ca2+ spiking signals, have been described in plants for a limited number of cellular responses to biotic or abiotic stimuli and most notably the common symbiotic signaling pathway (CSSP) which mediates the recognition by their plant hosts of two endosymbiotic microbes, arbuscular mycorrhizal (AM) fungi and nitrogen fixing rhizobia. The detailed analysis of the complexity and variability of the Ca2+ spiking patterns which have been revealed in recent studies requires both extensive datasets and sophisticated statistical tools. As a contribution, we have developed automated Ca2+ spiking analysis (CaSA) software that performs i) automated peak detection, ii) statistical analyses based on the detected peaks, iii) autocorrelation analysis of peak-to-peak intervals to highlight major traits in the spiking pattern.We have evaluated CaSA in two experimental studies. In the first, CaSA highlighted unpredicted differences in the spiking patterns induced in Medicago truncatula root epidermal cells by exudates of the AM fungus Gigaspora margarita as a function of the phosphate concentration in the growth medium of both host and fungus. In the second study we compared the spiking patterns triggered by either AM fungal or rhizobial symbiotic signals. CaSA revealed the existence of different patterns in signal periodicity, which are thought to contribute to the so-called Ca2+ signature. We therefore propose CaSA as a useful tool for characterizing oscillatory biological phenomena such as Ca2+ spiking.

Leaders and followers: quantifying consistency in spatio-temporal propagation patterns

NASA Astrophysics Data System (ADS)

Kreuz, Thomas; Satuvuori, Eero; Pofahl, Martin; Mulansky, Mario

2017-04-01

Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions of the same global propagation pattern are called a synfire pattern. For any recording of sequences of discrete events (in neuroscience terminology: sets of spike trains) the questions arise how closely it resembles such a synfire pattern and which are the spike trains that lead/follow. Here we address these questions and introduce an algorithm built on two new indicators, termed SPIKE-order and spike train order, that define the synfire indicator value, which allows to sort multiple spike trains from leader to follower and to quantify the consistency of the temporal leader-follower relationships for both the original and the optimized sorting. We demonstrate our new approach using artificially generated datasets before we apply it to analyze the consistency of propagation patterns in two real datasets from neuroscience (giant depolarized potentials in mice slices) and climatology (El Niño sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality.

Theta rhythm-like bidirectional cycling dynamics of living neuronal networks in vitro.

PubMed

Gladkov, Arseniy; Grinchuk, Oleg; Pigareva, Yana; Mukhina, Irina; Kazantsev, Victor; Pimashkin, Alexey

2018-01-01

The phenomena of synchronization, rhythmogenesis and coherence observed in brain networks are believed to be a dynamic substrate for cognitive functions such as learning and memory. However, researchers are still debating whether the rhythmic activity emerges from the network morphology that developed during neurogenesis or as a result of neuronal dynamics achieved under certain conditions. In the present study, we observed self-organized spiking activity that converged to long, complex and rhythmically repeated superbursts in neural networks formed by mature hippocampal cultures with a high cellular density. The superburst lasted for tens of seconds and consisted of hundreds of short (50-100 ms) small bursts with a high spiking rate of 139.0 ± 78.6 Hz that is associated with high-frequency oscillations in the hippocampus. In turn, the bursting frequency represents a theta rhythm (11.2 ± 1.5 Hz). The distribution of spikes within the bursts was non-random, representing a set of well-defined spatio-temporal base patterns or motifs. The long superburst was classified into two types. Each type was associated with a unique direction of spike propagation and, hence, was encoded by a binary sequence with random switching between the two "functional" states. The precisely structured bidirectional rhythmic activity that developed in self-organizing cultured networks was quite similar to the activity observed in the in vivo experiments.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

PubMed

Onken, Arno; Liu, Jian K; Karunasekara, P P Chamanthi R; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-11-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains

PubMed Central

Onken, Arno; Liu, Jian K.; Karunasekara, P. P. Chamanthi R.; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-01-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding. PMID:27814363

Millimeter-scale epileptiform spike propagation patterns and their relationship to seizures

PubMed Central

Vanleer, Ann C; Blanco, Justin A; Wagenaar, Joost B; Viventi, Jonathan; Contreras, Diego; Litt, Brian

2016-01-01

Objective Current mapping of epileptic networks in patients prior to epilepsy surgery utilizes electrode arrays with sparse spatial sampling (∼1.0 cm inter-electrode spacing). Recent research demonstrates that sub-millimeter, cortical-column-scale domains have a role in seizure generation that may be clinically significant. We use high-resolution, active, flexible surface electrode arrays with 500 μm inter-electrode spacing to explore epileptiform local field potential spike propagation patterns in two dimensions recorded from subdural micro-electrocorticographic signals in vivo in cat. In this study, we aimed to develop methods to quantitatively characterize the spatiotemporal dynamics of epileptiform activity at high-resolution. Approach We topically administered a GABA-antagonist, picrotoxin, to induce acute neocortical epileptiform activity leading up to discrete electrographic seizures. We extracted features from local field potential spikes to characterize spatiotemporal patterns in these events. We then tested the hypothesis that two dimensional spike patterns during seizures were different from those between seizures. Main results We showed that spatially correlated events can be used to distinguish ictal versus interictal spikes. Significance We conclude that sub-millimeter-scale spatiotemporal spike patterns reveal network dynamics that are invisible to standard clinical recordings and contain information related to seizure-state. PMID:26859260

Millimeter-scale epileptiform spike propagation patterns and their relationship to seizures

NASA Astrophysics Data System (ADS)

Vanleer, Ann C.; Blanco, Justin A.; Wagenaar, Joost B.; Viventi, Jonathan; Contreras, Diego; Litt, Brian

2016-04-01

Objective. Current mapping of epileptic networks in patients prior to epilepsy surgery utilizes electrode arrays with sparse spatial sampling (∼1.0 cm inter-electrode spacing). Recent research demonstrates that sub-millimeter, cortical-column-scale domains have a role in seizure generation that may be clinically significant. We use high-resolution, active, flexible surface electrode arrays with 500 μm inter-electrode spacing to explore epileptiform local field potential (LFP) spike propagation patterns in two dimensions recorded from subdural micro-electrocorticographic signals in vivo in cat. In this study, we aimed to develop methods to quantitatively characterize the spatiotemporal dynamics of epileptiform activity at high-resolution. Approach. We topically administered a GABA-antagonist, picrotoxin, to induce acute neocortical epileptiform activity leading up to discrete electrographic seizures. We extracted features from LFP spikes to characterize spatiotemporal patterns in these events. We then tested the hypothesis that two-dimensional spike patterns during seizures were different from those between seizures. Main results. We showed that spatially correlated events can be used to distinguish ictal versus interictal spikes. Significance. We conclude that sub-millimeter-scale spatiotemporal spike patterns reveal network dynamics that are invisible to standard clinical recordings and contain information related to seizure-state.

Single neuron firing properties impact correlation-based population coding

PubMed Central

Hong, Sungho; Ratté, Stéphanie; Prescott, Steven A.; De Schutter, Erik

2012-01-01

Correlated spiking has been widely observed but its impact on neural coding remains controversial. Correlation arising from co-modulation of rates across neurons has been shown to vary with the firing rates of individual neurons. This translates into rate and correlation being equivalently tuned to the stimulus; under those conditions, correlated spiking does not provide information beyond that already available from individual neuron firing rates. Such correlations are irrelevant and can reduce coding efficiency by introducing redundancy. Using simulations and experiments in rat hippocampal neurons, we show here that pairs of neurons receiving correlated input also exhibit correlations arising from precise spike-time synchronization. Contrary to rate co-modulation, spike-time synchronization is unaffected by firing rate, thus enabling synchrony- and rate-based coding to operate independently. The type of output correlation depends on whether intrinsic neuron properties promote integration or coincidence detection: “ideal” integrators (with spike generation sensitive to stimulus mean) exhibit rate co-modulation whereas “ideal” coincidence detectors (with spike generation sensitive to stimulus variance) exhibit precise spike-time synchronization. Pyramidal neurons are sensitive to both stimulus mean and variance, and thus exhibit both types of output correlation proportioned according to which operating mode is dominant. Our results explain how different types of correlations arise based on how individual neurons generate spikes, and why spike-time synchronization and rate co-modulation can encode different stimulus properties. Our results also highlight the importance of neuronal properties for population-level coding insofar as neural networks can employ different coding schemes depending on the dominant operating mode of their constituent neurons. PMID:22279226

Spike-train communities: finding groups of similar spike trains.

PubMed

Humphries, Mark D

2011-02-09

Identifying similar spike-train patterns is a key element in understanding neural coding and computation. For single neurons, similar spike patterns evoked by stimuli are evidence of common coding. Across multiple neurons, similar spike trains indicate potential cell assemblies. As recording technology advances, so does the urgent need for grouping methods to make sense of large-scale datasets of spike trains. Existing methods require specifying the number of groups in advance, limiting their use in exploratory analyses. I derive a new method from network theory that solves this key difficulty: it self-determines the maximum number of groups in any set of spike trains, and groups them to maximize intragroup similarity. This method brings us revealing new insights into the encoding of aversive stimuli by dopaminergic neurons, and the organization of spontaneous neural activity in cortex. I show that the characteristic pause response of a rat's dopaminergic neuron depends on the state of the superior colliculus: when it is inactive, aversive stimuli invoke a single pattern of dopaminergic neuron spiking; when active, multiple patterns occur, yet the spike timing in each is reliable. In spontaneous multineuron activity from the cortex of anesthetized cat, I show the existence of neural ensembles that evolve in membership and characteristic timescale of organization during global slow oscillations. I validate these findings by showing that the method both is remarkably reliable at detecting known groups and can detect large-scale organization of dynamics in a model of the striatum.

Temporally coordinated spiking activity of human induced pluripotent stem cell-derived neurons co-cultured with astrocytes.

PubMed

Kayama, Tasuku; Suzuki, Ikuro; Odawara, Aoi; Sasaki, Takuya; Ikegaya, Yuji

2018-01-01

In culture conditions, human induced-pluripotent stem cells (hiPSC)-derived neurons form synaptic connections with other cells and establish neuronal networks, which are expected to be an in vitro model system for drug discovery screening and toxicity testing. While early studies demonstrated effects of co-culture of hiPSC-derived neurons with astroglial cells on survival and maturation of hiPSC-derived neurons, the population spiking patterns of such hiPSC-derived neurons have not been fully characterized. In this study, we analyzed temporal spiking patterns of hiPSC-derived neurons recorded by a multi-electrode array system. We discovered that specific sets of hiPSC-derived neurons co-cultured with astrocytes showed more frequent and highly coherent non-random synchronized spike trains and more dynamic changes in overall spike patterns over time. These temporally coordinated spiking patterns are physiological signs of organized circuits of hiPSC-derived neurons and suggest benefits of co-culture of hiPSC-derived neurons with astrocytes. Copyright © 2017 Elsevier Inc. All rights reserved.

Automated analysis of calcium spiking profiles with CaSA software: two case studies from root-microbe symbioses

PubMed Central

2013-01-01

Background Repeated oscillations in intracellular calcium (Ca2+) concentration, known as Ca2+ spiking signals, have been described in plants for a limited number of cellular responses to biotic or abiotic stimuli and most notably the common symbiotic signaling pathway (CSSP) which mediates the recognition by their plant hosts of two endosymbiotic microbes, arbuscular mycorrhizal (AM) fungi and nitrogen fixing rhizobia. The detailed analysis of the complexity and variability of the Ca2+ spiking patterns which have been revealed in recent studies requires both extensive datasets and sophisticated statistical tools. Results As a contribution, we have developed automated Ca2+ spiking analysis (CaSA) software that performs i) automated peak detection, ii) statistical analyses based on the detected peaks, iii) autocorrelation analysis of peak-to-peak intervals to highlight major traits in the spiking pattern. We have evaluated CaSA in two experimental studies. In the first, CaSA highlighted unpredicted differences in the spiking patterns induced in Medicago truncatula root epidermal cells by exudates of the AM fungus Gigaspora margarita as a function of the phosphate concentration in the growth medium of both host and fungus. In the second study we compared the spiking patterns triggered by either AM fungal or rhizobial symbiotic signals. CaSA revealed the existence of different patterns in signal periodicity, which are thought to contribute to the so-called Ca2+ signature. Conclusions We therefore propose CaSA as a useful tool for characterizing oscillatory biological phenomena such as Ca2+ spiking. PMID:24369773

Determination of (187)Os in molybdenite by ICP-MS with neutron-induced (186)Os and (188)Os spikes.

PubMed

Qu, W; Du, A; Zhao, D

2001-10-31

The article describes a method for the determination of (187)Os in molybdenite by isotope dilution inductively coupled plasma-mass spectrometry (ID-ICP-MS) with neutron-induced (186)Os and (188)Os spike. The spike used in the present work was prepared in line with the principle by which artificial nuclides are produced in a nuclear reaction. The concentration and isotopic composition of osmium in the prepared spike were evaluated accurately with the isotope dilution method, using negative thermal ion mass spectrometry (N-TIMS). The advantage of this method is that using (186)Os and (188)Os double spikes can effectively compensate for the mass discrimination effects of ICP-MS. Thus, the common correction practice for mass bias in the isotope dilution method with a single spike is unnecessary. In addition, the method enables one to reduce the determined error arising from instrumental instability. The precision for the (187)Os/((186)Os+(188)Os) ratio was approximately 2% (2sigma, RSD), but in the case of (187)Os/(186)Os, (187)Os/(188)Os and (186)Os/(188)Os, precision ranged from 2.0 to 8% (2sigma, RSD). The results for (187)Os concentration in a molybdenite sample determined with this method showed good agreement with reference values.

Characterizing the complexity of spontaneous motor unit patterns of amyotrophic lateral sclerosis using approximate entropy

NASA Astrophysics Data System (ADS)

Zhou, Ping; Barkhaus, Paul E.; Zhang, Xu; Zev Rymer, William

2011-10-01

This paper presents a novel application of the approximate entropy (ApEn) measurement for characterizing spontaneous motor unit activity of amyotrophic lateral sclerosis (ALS) patients. High-density surface electromyography (EMG) was used to record spontaneous motor unit activity bilaterally from the thenar muscles of nine ALS subjects. Three distinct patterns of spontaneous motor unit activity (sporadic spikes, tonic spikes and high-frequency repetitive spikes) were observed. For each pattern, complexity was characterized by calculating the ApEn values of the representative signal segments. A sliding window over each segment was also introduced to quantify the dynamic changes in complexity for the different spontaneous motor unit patterns. We found that the ApEn values for the sporadic spikes were the highest, while those of the high-frequency repetitive spikes were the lowest. There is a significant difference in mean ApEn values between two arbitrary groups of the three spontaneous motor unit patterns (P < 0.001). The dynamic ApEn curve from the sliding window analysis is capable of tracking variations in EMG activity, thus providing a vivid, distinctive description for different patterns of spontaneous motor unit action potentials in terms of their complexity. These findings expand the existing knowledge of spontaneous motor unit activity in ALS beyond what was previously obtained using conventional linear methods such as firing rate or inter-spike interval statistics.

Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution

PubMed Central

Espinal, Andres; Rostro-Gonzalez, Horacio; Carpio, Martin; Guerra-Hernandez, Erick I.; Ornelas-Rodriguez, Manuel; Sotelo-Figueroa, Marco

2016-01-01

This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented. PMID:27516737

A single-cell spiking model for the origin of grid-cell patterns

PubMed Central

Kempter, Richard

2017-01-01

Spatial cognition in mammals is thought to rely on the activity of grid cells in the entorhinal cortex, yet the fundamental principles underlying the origin of grid-cell firing are still debated. Grid-like patterns could emerge via Hebbian learning and neuronal adaptation, but current computational models remained too abstract to allow direct confrontation with experimental data. Here, we propose a single-cell spiking model that generates grid firing fields via spike-rate adaptation and spike-timing dependent plasticity. Through rigorous mathematical analysis applicable in the linear limit, we quantitatively predict the requirements for grid-pattern formation, and we establish a direct link to classical pattern-forming systems of the Turing type. Our study lays the groundwork for biophysically-realistic models of grid-cell activity. PMID:28968386

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

PubMed

Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

2015-12-01

Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

Classification of epileptiform and wicket spike of EEG pattern using backpropagation neural network

NASA Astrophysics Data System (ADS)

Puspita, Juni Wijayanti; Jaya, Agus Indra; Gunadharma, Suryani

2017-03-01

Epilepsy is characterized by recurrent seizures that is resulted by permanent brain abnormalities. One of tools to support the diagnosis of epilepsy is Electroencephalograph (EEG), which describes the recording of brain electrical activity. Abnormal EEG patterns in epilepsy patients consist of Spike and Sharp waves. While both waves, there is a normal pattern that sometimes misinterpreted as epileptiform by electroenchepalographer (EEGer), namely Wicket Spike. The main difference of the three waves are on the time duration that related to the frequency. In this study, we proposed a method to classify a EEG wave into Sharp wave, Spike wave or Wicket spike group using Backpropagation Neural Network based on the frequency and amplitude of each wave. The results show that the proposed method can classifies the three group of waves with good accuracy.

Streaming parallel GPU acceleration of large-scale filter-based spiking neural networks.

PubMed

Slażyński, Leszek; Bohte, Sander

2012-01-01

The arrival of graphics processing (GPU) cards suitable for massively parallel computing promises affordable large-scale neural network simulation previously only available at supercomputing facilities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of magnitude, the challenge is to develop fine-grained parallel algorithms to fully exploit the particulars of GPUs. Computation in a neural network is inherently parallel and thus a natural match for GPU architectures: given inputs, the internal state for each neuron can be updated in parallel. We show that for filter-based spiking neurons, like the Spike Response Model, the additive nature of membrane potential dynamics enables additional update parallelism. This also reduces the accumulation of numerical errors when using single precision computation, the native precision of GPUs. We further show that optimizing simulation algorithms and data structures to the GPU's architecture has a large pay-off: for example, matching iterative neural updating to the memory architecture of the GPU speeds up this simulation step by a factor of three to five. With such optimizations, we can simulate in better-than-realtime plausible spiking neural networks of up to 50 000 neurons, processing over 35 million spiking events per second.

Feedback Enhances Feedforward Figure-Ground Segmentation by Changing Firing Mode

PubMed Central

Supèr, Hans; Romeo, August

2011-01-01

In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforwardspiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses withthe responses to a homogenous texture. We propose that feedback controlsfigure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons. PMID:21738747

In vivo analysis of Purkinje cell firing properties during postnatal mouse development

PubMed Central

Arancillo, Marife; White, Joshua J.; Lin, Tao; Stay, Trace L.

2014-01-01

Purkinje cell activity is essential for controlling motor behavior. During motor behavior Purkinje cells fire two types of action potentials: simple spikes that are generated intrinsically and complex spikes that are induced by climbing fiber inputs. Although the functions of these spikes are becoming clear, how they are established is still poorly understood. Here, we used in vivo electrophysiology approaches conducted in anesthetized and awake mice to record Purkinje cell activity starting from the second postnatal week of development through to adulthood. We found that the rate of complex spike firing increases sharply at 3 wk of age whereas the rate of simple spike firing gradually increases until 4 wk of age. We also found that compared with adult, the pattern of simple spike firing during development is more irregular as the cells tend to fire in bursts that are interrupted by long pauses. The regularity in simple spike firing only reached maturity at 4 wk of age. In contrast, the adult complex spike pattern was already evident by the second week of life, remaining consistent across all ages. Analyses of Purkinje cells in alert behaving mice suggested that the adult patterns are attained more than a week after the completion of key morphogenetic processes such as migration, lamination, and foliation. Purkinje cell activity is therefore dynamically sculpted throughout postnatal development, traversing several critical events that are required for circuit formation. Overall, we show that simple spike and complex spike firing develop with unique developmental trajectories. PMID:25355961

Sound Rhythms Are Encoded by Postinhibitory Rebound Spiking in the Superior Paraolivary Nucleus

PubMed Central

Felix, Richard A.; Fridberger, Anders; Leijon, Sara; Berrebi, Albert S.; Magnusson, Anna K.

2013-01-01

The superior paraolivary nucleus (SPON) is a prominent structure in the auditory brainstem. In contrast to the principal superior olivary nuclei with identified roles in processing binaural sound localization cues, the role of the SPON in hearing is not well understood. A combined in vitro and in vivo approach was used to investigate the cellular properties of SPON neurons in the mouse. Patch-clamp recordings in brain slices revealed that brief and well timed postinhibitory rebound spiking, generated by the interaction of two subthreshold-activated ion currents, is a hallmark of SPON neurons. The Ih current determines the timing of the rebound, whereas the T-type Ca2+ current boosts the rebound to spike threshold. This precisely timed rebound spiking provides a physiological explanation for the sensitivity of SPON neurons to sinusoidally amplitude-modulated (SAM) tones in vivo, where peaks in the sound envelope drive inhibitory inputs and SPON neurons fire action potentials during the waveform troughs. Consistent with this notion, SPON neurons display intrinsic tuning to frequency-modulated sinusoidal currents (1–15Hz) in vitro and discharge with strong synchrony to SAMs with modulation frequencies between 1 and 20 Hz in vivo. The results of this study suggest that the SPON is particularly well suited to encode rhythmic sound patterns. Such temporal periodicity information is likely important for detection of communication cues, such as the acoustic envelopes of animal vocalizations and speech signals. PMID:21880918

Sound rhythms are encoded by postinhibitory rebound spiking in the superior paraolivary nucleus.

PubMed

Felix, Richard A; Fridberger, Anders; Leijon, Sara; Berrebi, Albert S; Magnusson, Anna K

2011-08-31

The superior paraolivary nucleus (SPON) is a prominent structure in the auditory brainstem. In contrast to the principal superior olivary nuclei with identified roles in processing binaural sound localization cues, the role of the SPON in hearing is not well understood. A combined in vitro and in vivo approach was used to investigate the cellular properties of SPON neurons in the mouse. Patch-clamp recordings in brain slices revealed that brief and well timed postinhibitory rebound spiking, generated by the interaction of two subthreshold-activated ion currents, is a hallmark of SPON neurons. The I(h) current determines the timing of the rebound, whereas the T-type Ca(2+) current boosts the rebound to spike threshold. This precisely timed rebound spiking provides a physiological explanation for the sensitivity of SPON neurons to sinusoidally amplitude-modulated (SAM) tones in vivo, where peaks in the sound envelope drive inhibitory inputs and SPON neurons fire action potentials during the waveform troughs. Consistent with this notion, SPON neurons display intrinsic tuning to frequency-modulated sinusoidal currents (1-15Hz) in vitro and discharge with strong synchrony to SAMs with modulation frequencies between 1 and 20 Hz in vivo. The results of this study suggest that the SPON is particularly well suited to encode rhythmic sound patterns. Such temporal periodicity information is likely important for detection of communication cues, such as the acoustic envelopes of animal vocalizations and speech signals.

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

PubMed

Zenke, Friedemann; Ganguli, Surya

2018-06-01

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Forced phase-locked states and information retrieval in a two-layer network of oscillatory neurons with directional connectivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kazantsev, Victor; Pimashkin, Alexey; Department of Neurodynamics and Neurobiology, Nizhny Novgorod State University, 23 Gagarin Ave., 603950 Nizhny Novgorod

We propose two-layer architecture of associative memory oscillatory network with directional interlayer connectivity. The network is capable to store information in the form of phase-locked (in-phase and antiphase) oscillatory patterns. The first (input) layer takes an input pattern to be recognized and their units are unidirectionally connected with all units of the second (control) layer. The connection strengths are weighted using the Hebbian rule. The output (retrieved) patterns appear as forced-phase locked states of the control layer. The conditions are found and analytically expressed for pattern retrieval in response on incoming stimulus. It is shown that the system is capablemore » to recover patterns with a certain level of distortions or noises in their profiles. The architecture is implemented with the Kuramoto phase model and using synaptically coupled neural oscillators with spikes. It is found that the spiking model is capable to retrieve patterns using the spiking phase that translates memorized patterns into the spiking phase shifts at different time scales.« less

An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation.

PubMed

Wang, Runchun; Cohen, Gregory; Stiefel, Klaus M; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, André

2013-01-01

We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. The proposed neural network generates delay paths de novo, so that only connections that actually appear in the training patterns will be created. This allows the proposed network to use all the axons (variables) to store information. Spike Timing Dependent Delay Plasticity is used to fine-tune and add dynamics to the network. We use a time multiplexing approach allowing us to achieve 4096 (4k) neurons and up to 1.15 million programmable delay axons on a Virtex 6 FPGA. Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. The tests also show that the neural network is robust to noise from random input spikes.

Correlation transfer from basal ganglia to thalamus in Parkinson's disease

PubMed Central

Pamela, Reitsma; Brent, Doiron; Jonathan, Rubin

2011-01-01

Spike trains from neurons in the basal ganglia of parkinsonian primates show increased pairwise correlations, oscillatory activity, and burst rate compared to those from neurons recorded during normal brain activity. However, it is not known how these changes affect the behavior of downstream thalamic neurons. To understand how patterns of basal ganglia population activity may affect thalamic spike statistics, we study pairs of model thalamocortical (TC) relay neurons receiving correlated inhibitory input from the internal segment of the globus pallidus (GPi), a primary output nucleus of the basal ganglia. We observe that the strength of correlations of TC neuron spike trains increases with the GPi correlation level, and bursty firing patterns such as those seen in the parkinsonian GPi allow for stronger transfer of correlations than do firing patterns found under normal conditions. We also show that the T-current in the TC neurons does not significantly affect correlation transfer, despite its pronounced effects on spiking. Oscillatory firing patterns in GPi are shown to affect the timescale at which correlations are best transferred through the system. To explain this last result, we analytically compute the spike count correlation coefficient for oscillatory cases in a reduced point process model. Our analysis indicates that the dependence of the timescale of correlation transfer is robust to different levels of input spike and rate correlations and arises due to differences in instantaneous spike correlations, even when the long timescale rhythmic modulations of neurons are identical. Overall, these results show that parkinsonian firing patterns in GPi do affect the transfer of correlations to the thalamus. PMID:22355287

Structure-function relationships between aldolase C/zebrin II expression and complex spike synchrony in the cerebellum.

PubMed

Tsutsumi, Shinichiro; Yamazaki, Maya; Miyazaki, Taisuke; Watanabe, Masahiko; Sakimura, Kenji; Kano, Masanobu; Kitamura, Kazuo

2015-01-14

Simple and regular anatomical structure is a hallmark of the cerebellar cortex. Parasagittally arrayed alternate expression of aldolase C/zebrin II in Purkinje cells (PCs) has been extensively studied, but surprisingly little is known about its functional significance. Here we found a precise structure-function relationship between aldolase C expression and synchrony of PC complex spike activities that reflect climbing fiber inputs to PCs. We performed two-photon calcium imaging in transgenic mice in which aldolase C compartments can be visualized in vivo, and identified highly synchronous complex spike activities among aldolase C-positive or aldolase C-negative PCs, but not across these populations. The boundary of aldolase C compartments corresponded to that of complex spike synchrony at single-cell resolution. Sensory stimulation evoked aldolase C compartment-specific complex spike responses and synchrony. This result further revealed the structure-function segregation. In awake animals, complex spike synchrony both within and between PC populations across the aldolase C boundary were enhanced in response to sensory stimuli, in a way that two functionally distinct PC ensembles are coactivated. These results suggest that PC populations characterized by aldolase C expression precisely represent distinct functional units of the cerebellar cortex, and these functional units can cooperate to process sensory information in awake animals. Copyright © 2015 the authors 0270-6474/15/350843-10$15.00/0.

Exact computation of the maximum-entropy potential of spiking neural-network models.

PubMed

Cofré, R; Cessac, B

2014-05-01

Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in neural networks is a central question in computational neuroscience. The maximum-entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. However, in spite of good performance in terms of prediction, the fitting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuromimetic models) provide a probabilistic mapping between the stimulus, network architecture, and spike patterns in terms of conditional probabilities. In this paper we build an exact analytical mapping between neuromimetic and maximum-entropy models.

Hippocampal Spike-Timing Correlations Lead to Hexagonal Grid Fields

NASA Astrophysics Data System (ADS)

Monsalve-Mercado, Mauro M.; Leibold, Christian

2017-07-01

Space is represented in the mammalian brain by the activity of hippocampal place cells, as well as in their spike-timing correlations. Here, we propose a theory for how this temporal code is transformed to spatial firing rate patterns via spike-timing-dependent synaptic plasticity. The resulting dynamics of synaptic weights resembles well-known pattern formation models in which a lateral inhibition mechanism gives rise to a Turing instability. We identify parameter regimes in which hexagonal firing patterns develop as they have been found in medial entorhinal cortex.

A new supervised learning algorithm for spiking neurons.

PubMed

Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

2013-06-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

Spatiotemporal activity patterns detected from single cell measurements from behaving animals

NASA Astrophysics Data System (ADS)

Villa, Alessandro E. P.; Tetko, Igor V.

1999-03-01

Precise temporal patterning of activity within and between neurons has been predicted on theoretical grounds, and found in the spike trains of neurons recorded from anesthetized and conscious animals, in association with sensor stimuli and particular phases of task performance. However, the functional significance of such patterning in the generation of behavior has not been confirmed. We recorded from multiple single neurons in regions of rat auditory cortex during the waiting period of a Go/NoGo task. During this time the animal waited for an auditory signal with high cognitive load. Of note is the fact that neural activity during the period analyzed was essentially stationary, with no event related variability in firing. Detected patterns therefore provide a measure of brain state that could not be addressed by standard methods relying on analysis of changes in mean discharge rate. The possibility is discussed that some patterns might reflect a preset bias to a particular response, formed in the waiting period. Others patterns might reflect a state of prior preparation of appropriate neural assemblies for analyzing a signal that is expected but of unknown behavioral valence.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Myhra, S., E-mail: sverre.myhra@materials.ox.ac.uk; Chakalova, R.; Falzone, N.

A method for detection and characterization of single MeV α-particle and recoil tracks in PMMA photoresist by atomic force microscopy (AFM) analysis has been demonstrated. The energy deposition along the track is shown to lead to a latent pattern in the resist due to contrast reversal. It has been shown that the pattern, consisting of conical spikes, can be developed by conventional processing as a result of the dissolution rate of poly(methyl methacrylate) (PMMA) being greater than that for the modified material in the cylindrical volume of the track core. The spikes can be imaged and counted by routine AFMmore » analysis. Investigations by angular-resolved near-grazing incidence reveal additional tracks that correspond to recoil tracks. The observations have been correlated with modelling, and shown to be in qualitative agreement with prevailing descriptions of collision cascades. The results may be relevant to technologies that are based on detection and characterization of single energetic ions. In particular, the direct visualization of the collision cascade may allow more accurate estimates of the actual interaction volume, which in turn will permit more precise assessment of dose distribution of α-emitting radionuclides used for targeted radiotherapy. The results could also be relevant to other diagnostic or process technologies based on interaction of energetic ions with matter.« less

Multiple-Color Optical Activation, Silencing, and Desynchronization of Neural Activity, with Single-Spike Temporal Resolution

PubMed Central

Han, Xue; Boyden, Edward S.

2007-01-01

The quest to determine how precise neural activity patterns mediate computation, behavior, and pathology would be greatly aided by a set of tools for reliably activating and inactivating genetically targeted neurons, in a temporally precise and rapidly reversible fashion. Having earlier adapted a light-activated cation channel, channelrhodopsin-2 (ChR2), for allowing neurons to be stimulated by blue light, we searched for a complementary tool that would enable optical neuronal inhibition, driven by light of a second color. Here we report that targeting the codon-optimized form of the light-driven chloride pump halorhodopsin from the archaebacterium Natronomas pharaonis (hereafter abbreviated Halo) to genetically-specified neurons enables them to be silenced reliably, and reversibly, by millisecond-timescale pulses of yellow light. We show that trains of yellow and blue light pulses can drive high-fidelity sequences of hyperpolarizations and depolarizations in neurons simultaneously expressing yellow light-driven Halo and blue light-driven ChR2, allowing for the first time manipulations of neural synchrony without perturbation of other parameters such as spiking rates. The Halo/ChR2 system thus constitutes a powerful toolbox for multichannel photoinhibition and photostimulation of virally or transgenically targeted neural circuits without need for exogenous chemicals, enabling systematic analysis and engineering of the brain, and quantitative bioengineering of excitable cells. PMID:17375185

Bidirectional control of spike timing by GABA(A) receptor-mediated inhibition during theta oscillation in CA1 pyramidal neurons.

PubMed

Kwag, Jeehyun; Paulsen, Ole

2009-08-26

Precisely controlled spike times relative to theta-frequency network oscillations play an important role in hippocampal memory processing. Here we study how inhibitory synaptic input during theta oscillation contributes to the control of spike timing. Using whole-cell patch-clamp recordings from CA1 pyramidal cells in vitro with dynamic clamp to simulate theta-frequency oscillation (5 Hz), we show that gamma-aminobutyric acid-A (GABA(A)) receptor-mediated inhibitory postsynaptic potentials (IPSPs) can not only delay but also advance the postsynaptic spike depending on the timing of the inhibition relative to the oscillation. Spike time advancement with IPSP was abolished by the h-channel blocker ZD7288 (10 microM), suggesting that IPSPs can interact with intrinsic membrane conductances to yield bidirectional control of spike timing.

The Relationship between Respiration-Related Membrane Potential Slow Oscillations and Discharge Patterns in Mitral/Tufted Cells: What Are the Rules?

PubMed Central

Briffaud, Virginie; Fourcaud-Trocmé, Nicolas; Messaoudi, Belkacem; Buonviso, Nathalie; Amat, Corine

2012-01-01

Background A slow respiration-related rhythm strongly shapes the activity of the olfactory bulb. This rhythm appears as a slow oscillation that is detectable in the membrane potential, the respiration-related spike discharge of the mitral/tufted cells and the bulbar local field potential. Here, we investigated the rules that govern the manifestation of membrane potential slow oscillations (MPSOs) and respiration-related discharge activities under various afferent input conditions and cellular excitability states. Methodology and Principal Findings We recorded the intracellular membrane potential signals in the mitral/tufted cells of freely breathing anesthetized rats. We first demonstrated the existence of multiple types of MPSOs, which were influenced by odor stimulation and discharge activity patterns. Complementary studies using changes in the intracellular excitability state and a computational model of the mitral cell demonstrated that slow oscillations in the mitral/tufted cell membrane potential were also modulated by the intracellular excitability state, whereas the respiration-related spike activity primarily reflected the afferent input. Based on our data regarding MPSOs and spike patterns, we found that cells exhibiting an unsynchronized discharge pattern never exhibited an MPSO. In contrast, cells with a respiration-synchronized discharge pattern always exhibited an MPSO. In addition, we demonstrated that the association between spike patterns and MPSO types appeared complex. Conclusion We propose that both the intracellular excitability state and input strength underlie specific MPSOs, which, in turn, constrain the types of spike patterns exhibited. PMID:22952828

The neural dynamics of song syntax in songbirds

NASA Astrophysics Data System (ADS)

Jin, Dezhe

2010-03-01

Songbird is ``the hydrogen atom'' of the neuroscience of complex, learned vocalizations such as human speech. Songs of Bengalese finch consist of sequences of syllables. While syllables are temporally stereotypical, syllable sequences can vary and follow complex, probabilistic syntactic rules, which are rudimentarily similar to grammars in human language. Songbird brain is accessible to experimental probes, and is understood well enough to construct biologically constrained, predictive computational models. In this talk, I will discuss the structure and dynamics of neural networks underlying the stereotypy of the birdsong syllables and the flexibility of syllable sequences. Recent experiments and computational models suggest that a syllable is encoded in a chain network of projection neurons in premotor nucleus HVC (proper name). Precisely timed spikes propagate along the chain, driving vocalization of the syllable through downstream nuclei. Through a computational model, I show that that variable syllable sequences can be generated through spike propagations in a network in HVC in which the syllable-encoding chain networks are connected into a branching chain pattern. The neurons mutually inhibit each other through the inhibitory HVC interneurons, and are driven by external inputs from nuclei upstream of HVC. At a branching point that connects the final group of a chain to the first groups of several chains, the spike activity selects one branch to continue the propagation. The selection is probabilistic, and is due to the winner-take-all mechanism mediated by the inhibition and noise. The model predicts that the syllable sequences statistically follow partially observable Markov models. Experimental results supporting this and other predictions of the model will be presented. We suggest that the syntax of birdsong syllable sequences is embedded in the connection patterns of HVC projection neurons.

Low-noise encoding of active touch by layer 4 in the somatosensory cortex.

PubMed

Hires, Samuel Andrew; Gutnisky, Diego A; Yu, Jianing; O'Connor, Daniel H; Svoboda, Karel

2015-08-06

Cortical spike trains often appear noisy, with the timing and number of spikes varying across repetitions of stimuli. Spiking variability can arise from internal (behavioral state, unreliable neurons, or chaotic dynamics in neural circuits) and external (uncontrolled behavior or sensory stimuli) sources. The amount of irreducible internal noise in spike trains, an important constraint on models of cortical networks, has been difficult to estimate, since behavior and brain state must be precisely controlled or tracked. We recorded from excitatory barrel cortex neurons in layer 4 during active behavior, where mice control tactile input through learned whisker movements. Touch was the dominant sensorimotor feature, with >70% spikes occurring in millisecond timescale epochs after touch onset. The variance of touch responses was smaller than expected from Poisson processes, often reaching the theoretical minimum. Layer 4 spike trains thus reflect the millisecond-timescale structure of tactile input with little noise.

Synchrony detection and amplification by silicon neurons with STDP synapses.

PubMed

Bofill-i-petit, Adria; Murray, Alan F

2004-09-01

Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.

Optimal Design for Hetero-Associative Memory: Hippocampal CA1 Phase Response Curve and Spike-Timing-Dependent Plasticity

PubMed Central

Miyata, Ryota; Ota, Keisuke; Aonishi, Toru

2013-01-01

Recently reported experimental findings suggest that the hippocampal CA1 network stores spatio-temporal spike patterns and retrieves temporally reversed and spread-out patterns. In this paper, we explore the idea that the properties of the neural interactions and the synaptic plasticity rule in the CA1 network enable it to function as a hetero-associative memory recalling such reversed and spread-out spike patterns. In line with Lengyel’s speculation (Lengyel et al., 2005), we firstly derive optimally designed spike-timing-dependent plasticity (STDP) rules that are matched to neural interactions formalized in terms of phase response curves (PRCs) for performing the hetero-associative memory function. By maximizing object functions formulated in terms of mutual information for evaluating memory retrieval performance, we search for STDP window functions that are optimal for retrieval of normal and doubly spread-out patterns under the constraint that the PRCs are those of CA1 pyramidal neurons. The system, which can retrieve normal and doubly spread-out patterns, can also retrieve reversed patterns with the same quality. Finally, we demonstrate that purposely designed STDP window functions qualitatively conform to typical ones found in CA1 pyramidal neurons. PMID:24204822

A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.

PubMed

Pillow, Jonathan W; Shlens, Jonathon; Chichilnisky, E J; Simoncelli, Eero P

2013-01-01

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings

PubMed Central

Chichilnisky, E. J.; Simoncelli, Eero P.

2013-01-01

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call “binary pursuit”. The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth. PMID:23671583

Compositionality in neural control: an interdisciplinary study of scribbling movements in primates

PubMed Central

Abeles, Moshe; Diesmann, Markus; Flash, Tamar; Geisel, Theo; Herrmann, Michael; Teicher, Mina

2013-01-01

This article discusses the compositional structure of hand movements by analyzing and modeling neural and behavioral data obtained from experiments where a monkey (Macaca fascicularis) performed scribbling movements induced by a search task. Using geometrically based approaches to movement segmentation, it is shown that the hand trajectories are composed of elementary segments that are primarily parabolic in shape. The segments could be categorized into a small number of classes on the basis of decreasing intra-class variance over the course of training. A separate classification of the neural data employing a hidden Markov model showed a coincidence of the neural states with the behavioral categories. An additional analysis of both types of data by a data mining method provided evidence that the neural activity patterns underlying the behavioral primitives were formed by sets of specific and precise spike patterns. A geometric description of the movement trajectories, together with precise neural timing data indicates a compositional variant of a realistic synfire chain model. This model reproduces the typical shapes and temporal properties of the trajectories; hence the structure and composition of the primitives may reflect meaningful behavior. PMID:24062679

Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity

PubMed Central

Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon

2011-01-01

Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification. PMID:22203800

Characterizing neural activities evoked by manual acupuncture through spiking irregularity measures

NASA Astrophysics Data System (ADS)

Xue, Ming; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Yu, Hai-Tao; Chen, Ying-Yuan

2013-09-01

The neural system characterizes information in external stimulations by different spiking patterns. In order to examine how neural spiking patterns are related to acupuncture manipulations, experiments are designed in such a way that different types of manual acupuncture (MA) manipulations are taken at the ‘Zusanli’ point of experimental rats, and the induced electrical signals in the spinal dorsal root ganglion are detected and recorded. The interspike interval (ISI) statistical histogram is fitted by the gamma distribution, which has two parameters: one is the time-dependent firing rate and the other is a shape parameter characterizing the spiking irregularities. The shape parameter is the measure of spiking irregularities and can be used to identify the type of MA manipulations. The coefficient of variation is mostly used to measure the spike time irregularity, but it overestimates the irregularity in the case of pronounced firing rate changes. However, experiments show that each acupuncture manipulation will lead to changes in the firing rate. So we combine four relatively rate-independent measures to study the irregularity of spike trains evoked by different types of MA manipulations. Results suggest that the MA manipulations possess unique spiking statistics and characteristics and can be distinguished according to the spiking irregularity measures. These studies have offered new insights into the coding processes and information transfer of acupuncture.

Spiking irregularity and frequency modulate the behavioral report of single-neuron stimulation.

PubMed

Doron, Guy; von Heimendahl, Moritz; Schlattmann, Peter; Houweling, Arthur R; Brecht, Michael

2014-02-05

The action potential activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and neuronal subtypes affect the evoked sensations. Here, we examined the effects of spike train irregularity, spike frequency, and spike number on the detectability of single-neuron stimulation in rat somatosensory cortex. For regular-spiking, putative excitatory neurons, detectability increased with spike train irregularity and decreasing spike frequencies but was not affected by spike number. Stimulation of single, fast-spiking, putative inhibitory neurons led to a larger sensory effect compared to regular-spiking neurons, and the effect size depended only on spike irregularity. An ideal-observer analysis suggests that, under our experimental conditions, rats were using integration windows of a few hundred milliseconds or more. Our data imply that the behaving animal is sensitive to single neurons' spikes and even to their temporal patterning. Copyright © 2014 Elsevier Inc. All rights reserved.

Kv1 channels control spike threshold dynamics and spike timing in cortical pyramidal neurones

PubMed Central

Higgs, Matthew H; Spain, William J

2011-01-01

Abstract Previous studies showed that cortical pyramidal neurones (PNs) have a dynamic spike threshold that functions as a high-pass filter, enhancing spike timing in response to high-frequency input. While it is commonly assumed that Na+ channel inactivation is the primary mechanism of threshold accommodation, the possible role of K+ channel activation in fast threshold changes has not been well characterized. The present study tested the hypothesis that low-voltage activated Kv1 channels affect threshold dynamics in layer 2–3 PNs, using α-dendrotoxin (DTX) or 4-aminopyridine (4-AP) to block these conductances. We found that Kv1 blockade reduced the dynamic changes of spike threshold in response to a variety of stimuli, including stimulus-evoked synaptic input, current steps and ramps of varied duration, and noise. Analysis of the responses to noise showed that Kv1 channels increased the coherence of spike output with high-frequency components of the stimulus. A simple model demonstrates that a dynamic spike threshold can account for this effect. Our results show that the Kv1 conductance is a major mechanism that contributes to the dynamic spike threshold and precise spike timing of cortical PNs. PMID:21911608

Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity

PubMed Central

Hussain, Shaista; Basu, Arindam

2016-01-01

The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best “k” out of “d” inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike classifiers. We show that our system can achieve classification accuracy within 1 − 2% of other reported spike-based classifiers while using much less synaptic resources (only 7%) compared to that used by other methods. Further, an ensemble classifier created with adaptively learned sizes can attain accuracy of 96.4% which is at par with the best reported performance of spike-based classifiers. Moreover, the proposed method achieves this by using about 20% of the synapses used by other spike algorithms. We also present results of applying our algorithm to classify the MNIST-DVS dataset collected from a real spike-based image sensor and show results comparable to the best reported ones (88.1% accuracy). For VLSI implementations, we show that the reduced synaptic memory can save upto 4X area compared to conventional crossbar topologies. Finally, we also present a biologically realistic spike-based version for calculating the correlations required by the structural learning rule and demonstrate the correspondence between the rate-based and spike-based methods of learning. PMID:27065782

A General and Efficient Method for Incorporating Precise Spike Times in Globally Time-Driven Simulations

PubMed Central

Hanuschkin, Alexander; Kunkel, Susanne; Helias, Moritz; Morrison, Abigail; Diesmann, Markus

2010-01-01

Traditionally, event-driven simulations have been limited to the very restricted class of neuronal models for which the timing of future spikes can be expressed in closed form. Recently, the class of models that is amenable to event-driven simulation has been extended by the development of techniques to accurately calculate firing times for some integrate-and-fire neuron models that do not enable the prediction of future spikes in closed form. The motivation of this development is the general perception that time-driven simulations are imprecise. Here, we demonstrate that a globally time-driven scheme can calculate firing times that cannot be discriminated from those calculated by an event-driven implementation of the same model; moreover, the time-driven scheme incurs lower computational costs. The key insight is that time-driven methods are based on identifying a threshold crossing in the recent past, which can be implemented by a much simpler algorithm than the techniques for predicting future threshold crossings that are necessary for event-driven approaches. As run time is dominated by the cost of the operations performed at each incoming spike, which includes spike prediction in the case of event-driven simulation and retrospective detection in the case of time-driven simulation, the simple time-driven algorithm outperforms the event-driven approaches. Additionally, our method is generally applicable to all commonly used integrate-and-fire neuronal models; we show that a non-linear model employing a standard adaptive solver can reproduce a reference spike train with a high degree of precision. PMID:21031031

Learning complex temporal patterns with resource-dependent spike timing-dependent plasticity.

PubMed

Hunzinger, Jason F; Chan, Victor H; Froemke, Robert C

2012-07-01

Studies of spike timing-dependent plasticity (STDP) have revealed that long-term changes in the strength of a synapse may be modulated substantially by temporal relationships between multiple presynaptic and postsynaptic spikes. Whereas long-term potentiation (LTP) and long-term depression (LTD) of synaptic strength have been modeled as distinct or separate functional mechanisms, here, we propose a new shared resource model. A functional consequence of our model is fast, stable, and diverse unsupervised learning of temporal multispike patterns with a biologically consistent spiking neural network. Due to interdependencies between LTP and LTD, dendritic delays, and proactive homeostatic aspects of the model, neurons are equipped to learn to decode temporally coded information within spike bursts. Moreover, neurons learn spike timing with few exposures in substantial noise and jitter. Surprisingly, despite having only one parameter, the model also accurately predicts in vitro observations of STDP in more complex multispike trains, as well as rate-dependent effects. We discuss candidate commonalities in natural long-term plasticity mechanisms.

Mixed-mode oscillations and interspike interval statistics in the stochastic FitzHugh-Nagumo model

NASA Astrophysics Data System (ADS)

Berglund, Nils; Landon, Damien

2012-08-01

We study the stochastic FitzHugh-Nagumo equations, modelling the dynamics of neuronal action potentials in parameter regimes characterized by mixed-mode oscillations. The interspike time interval is related to the random number of small-amplitude oscillations separating consecutive spikes. We prove that this number has an asymptotically geometric distribution, whose parameter is related to the principal eigenvalue of a substochastic Markov chain. We provide rigorous bounds on this eigenvalue in the small-noise regime and derive an approximation of its dependence on the system's parameters for a large range of noise intensities. This yields a precise description of the probability distribution of observed mixed-mode patterns and interspike intervals.

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

PubMed Central

2017-01-01

Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making. PMID:28961245

PySpike-A Python library for analyzing spike train synchrony

NASA Astrophysics Data System (ADS)

Mulansky, Mario; Kreuz, Thomas

Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute similarity and dissimilarity profiles, averaged values and distance matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hurtado, Antonio, E-mail: antonio.hurtado@strath.ac.uk; Javaloyes, Julien

Multiple controllable spiking patterns are achieved in a 1310 nm Vertical-Cavity Surface Emitting Laser (VCSEL) in response to induced perturbations and for two different cases of polarized optical injection, namely, parallel and orthogonal. Furthermore, reproducible spiking responses are demonstrated experimentally at sub-nanosecond speed resolution and with a controlled number of spikes fired. This work opens therefore exciting research avenues for the use of VCSELs in ultrafast neuromorphic photonic systems for non-traditional computing applications, such as all-optical binary-to-spiking format conversion and spiking information encoding.

Evoking prescribed spike times in stochastic neurons

NASA Astrophysics Data System (ADS)

Doose, Jens; Lindner, Benjamin

2017-09-01

Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.

Neuronal Networks in Children with Continuous Spikes and Waves during Slow Sleep

ERIC Educational Resources Information Center

Siniatchkin, Michael; Groening, Kristina; Moehring, Jan; Moeller, Friederike; Boor, Rainer; Brodbeck, Verena; Michel, Christoph M.; Rodionov, Roman; Lemieux, Louis; Stephani, Ulrich

2010-01-01

Epileptic encephalopathy with continuous spikes and waves during slow sleep is an age-related disorder characterized by the presence of interictal epileptiform discharges during at least greater than 85% of sleep and cognitive deficits associated with this electroencephalography pattern. The pathophysiological mechanisms of continuous spikes and…

Millisecond-timescale local network coding in the rat primary somatosensory cortex.

PubMed

Eldawlatly, Seif; Oweiss, Karim G

2011-01-01

Correlation among neocortical neurons is thought to play an indispensable role in mediating sensory processing of external stimuli. The role of temporal precision in this correlation has been hypothesized to enhance information flow along sensory pathways. Its role in mediating the integration of information at the output of these pathways, however, remains poorly understood. Here, we examined spike timing correlation between simultaneously recorded layer V neurons within and across columns of the primary somatosensory cortex of anesthetized rats during unilateral whisker stimulation. We used bayesian statistics and information theory to quantify the causal influence between the recorded cells with millisecond precision. For each stimulated whisker, we inferred stable, whisker-specific, dynamic bayesian networks over many repeated trials, with network similarity of 83.3±6% within whisker, compared to only 50.3±18% across whiskers. These networks further provided information about whisker identity that was approximately 6 times higher than what was provided by the latency to first spike and 13 times higher than what was provided by the spike count of individual neurons examined separately. Furthermore, prediction of individual neurons' precise firing conditioned on knowledge of putative pre-synaptic cell firing was 3 times higher than predictions conditioned on stimulus onset alone. Taken together, these results suggest the presence of a temporally precise network coding mechanism that integrates information across neighboring columns within layer V about vibrissa position and whisking kinetics to mediate whisker movement by motor areas innervated by layer V.

Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements

PubMed Central

Riehle, Alexa; Wirtssohn, Sarah; Grün, Sonja; Brochier, Thomas

2013-01-01

Grasping an object involves shaping the hand and fingers in relation to the object’s physical properties. Following object contact, it also requires a fine adjustment of grasp forces for secure manipulation. Earlier studies suggest that the control of hand shaping and grasp force involve partially segregated motor cortical networks. However, it is still unclear how information originating from these networks is processed and integrated. We addressed this issue by analyzing massively parallel signals from population measures (local field potentials, LFPs) and single neuron spiking activities recorded simultaneously during a delayed reach-to-grasp task, by using a 100-electrode array chronically implanted in monkey motor cortex. Motor cortical LFPs exhibit a large multi-component movement-related potential (MRP) around movement onset. Here, we show that the peak amplitude of each MRP component and its latency with respect to movement onset vary along the cortical surface covered by the array. Using a comparative mapping approach, we suggest that the spatio-temporal structure of the MRP reflects the complex physical properties of the reach-to-grasp movement. In addition, we explored how the spatio-temporal structure of the MRP relates to two other measures of neuronal activity: the temporal profile of single neuron spiking activity at each electrode site and the somatosensory receptive field properties of single neuron activities. We observe that the spatial representations of LFP and spiking activities overlap extensively and relate to the spatial distribution of proximal and distal representations of the upper limb. Altogether, these data show that, in motor cortex, a precise spatio-temporal pattern of activation is involved for the control of reach-to-grasp movements and provide some new insight about the functional organization of motor cortex during reaching and object manipulation. PMID:23543888

Auto Guided Oil Palm Planter by using multi-GNSS

NASA Astrophysics Data System (ADS)

Nur Aini, I.; W, Aimrun; Amin, M. S. M.; Ezrin, M. H.; Shafri, H. Z.

2014-06-01

Planting is one of the most important operations in plantation because it could affect the total area of productivity since it is the starting point in cultivation. In oil palm plantation, lining and spacing of oil palm shall be laid out and coincided with the topographic area and a system of drains. Conventionally, planting of oil palm will require the polarization process in order to prevent and overcome the lack of influence of the sun rise and get a regular crop row. Polarization is done after the completion of the opening area by using the spike wood with 1 m length painted at the top and 100 m length of wire. This process will generally require at least five persons at a time to pull the wire and carry the spikes while the other two persons will act as observer and spikes craftsmen respectively with the ability of the team is 3ha/day. Therefore, the aim of this project is to develop the oil palm planting technique by using multi- GNSS (Global Navigation Satellite System). Generally, this project will involve five main steps mainly; design of planting pattern by using SOLIDWORKS software, determine the boundary coordinate of planting area, georeference process with ArcGIS, stakeout process with Tracy software and finally marking up the location with the wooden spikes. The results proved that the multi- GNSS is capable to provide the high accuracy with less than 1 m in precise positioning system without augmentation data. With the ability of one person, time taken to complete 70 m × 50 m planting area is 290 min, which is 25 min faster than using GPS (Global Positioning System) only.

Fitting neuron models to spike trains.

PubMed

Rossant, Cyrille; Goodman, Dan F M; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K; Brette, Romain

2011-01-01

Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input-output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model.

Dynamical spike solutions in a nonlocal model of pattern formation

NASA Astrophysics Data System (ADS)

Marciniak-Czochra, Anna; Härting, Steffen; Karch, Grzegorz; Suzuki, Kanako

2018-05-01

Coupling a reaction-diffusion equation with ordinary differential equa- tions (ODE) may lead to diffusion-driven instability (DDI) which, in contrast to the classical reaction-diffusion models, causes destabilization of both, constant solutions and Turing patterns. Using a shadow-type limit of a reaction-diffusion-ODE model, we show that in such cases the instability driven by nonlocal terms (a counterpart of DDI) may lead to formation of unbounded spike patterns.

Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.

PubMed

Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon

2016-01-01

Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.

Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events

PubMed Central

Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon

2016-01-01

Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate. PMID:28066225

Animation of natural scene by virtual eye-movements evokes high precision and low noise in V1 neurons

PubMed Central

Baudot, Pierre; Levy, Manuel; Marre, Olivier; Monier, Cyril; Pananceau, Marc; Frégnac, Yves

2013-01-01

Synaptic noise is thought to be a limiting factor for computational efficiency in the brain. In visual cortex (V1), ongoing activity is present in vivo, and spiking responses to simple stimuli are highly unreliable across trials. Stimulus statistics used to plot receptive fields, however, are quite different from those experienced during natural visuomotor exploration. We recorded V1 neurons intracellularly in the anaesthetized and paralyzed cat and compared their spiking and synaptic responses to full field natural images animated by simulated eye-movements to those evoked by simpler (grating) or higher dimensionality statistics (dense noise). In most cells, natural scene animation was the only condition where high temporal precision (in the 10–20 ms range) was maintained during sparse and reliable activity. At the subthreshold level, irregular but highly reproducible membrane potential dynamics were observed, even during long (several 100 ms) “spike-less” periods. We showed that both the spatial structure of natural scenes and the temporal dynamics of eye-movements increase the signal-to-noise ratio by a non-linear amplification of the signal combined with a reduction of the subthreshold contextual noise. These data support the view that the sparsening and the time precision of the neural code in V1 may depend primarily on three factors: (1) broadband input spectrum: the bandwidth must be rich enough for recruiting optimally the diversity of spatial and time constants during recurrent processing; (2) tight temporal interplay of excitation and inhibition: conductance measurements demonstrate that natural scene statistics narrow selectively the duration of the spiking opportunity window during which the balance between excitation and inhibition changes transiently and reversibly; (3) signal energy in the lower frequency band: a minimal level of power is needed below 10 Hz to reach consistently the spiking threshold, a situation rarely reached with visual dense noise. PMID:24409121

Animation of natural scene by virtual eye-movements evokes high precision and low noise in V1 neurons.

PubMed

Baudot, Pierre; Levy, Manuel; Marre, Olivier; Monier, Cyril; Pananceau, Marc; Frégnac, Yves

2013-01-01

Synaptic noise is thought to be a limiting factor for computational efficiency in the brain. In visual cortex (V1), ongoing activity is present in vivo, and spiking responses to simple stimuli are highly unreliable across trials. Stimulus statistics used to plot receptive fields, however, are quite different from those experienced during natural visuomotor exploration. We recorded V1 neurons intracellularly in the anaesthetized and paralyzed cat and compared their spiking and synaptic responses to full field natural images animated by simulated eye-movements to those evoked by simpler (grating) or higher dimensionality statistics (dense noise). In most cells, natural scene animation was the only condition where high temporal precision (in the 10-20 ms range) was maintained during sparse and reliable activity. At the subthreshold level, irregular but highly reproducible membrane potential dynamics were observed, even during long (several 100 ms) "spike-less" periods. We showed that both the spatial structure of natural scenes and the temporal dynamics of eye-movements increase the signal-to-noise ratio by a non-linear amplification of the signal combined with a reduction of the subthreshold contextual noise. These data support the view that the sparsening and the time precision of the neural code in V1 may depend primarily on three factors: (1) broadband input spectrum: the bandwidth must be rich enough for recruiting optimally the diversity of spatial and time constants during recurrent processing; (2) tight temporal interplay of excitation and inhibition: conductance measurements demonstrate that natural scene statistics narrow selectively the duration of the spiking opportunity window during which the balance between excitation and inhibition changes transiently and reversibly; (3) signal energy in the lower frequency band: a minimal level of power is needed below 10 Hz to reach consistently the spiking threshold, a situation rarely reached with visual dense noise.

Common and Distinctive Patterns of Cognitive Dysfunction in Children With Benign Epilepsy Syndromes.

PubMed

Cheng, Dazhi; Yan, Xiuxian; Gao, Zhijie; Xu, Keming; Zhou, Xinlin; Chen, Qian

2017-07-01

Childhood absence epilepsy and benign childhood epilepsy with centrotemporal spikes are the most common forms of benign epilepsy syndromes. Although cognitive dysfunctions occur in children with both childhood absence epilepsy and benign childhood epilepsy with centrotemporal spikes, the similarity between their patterns of underlying cognitive impairments is not well understood. To describe these patterns, we examined multiple cognitive functions in children with childhood absence epilepsy and benign childhood epilepsy with centrotemporal spikes. In this study, 43 children with childhood absence epilepsy, 47 children with benign childhood epilepsy with centrotemporal spikes, and 64 control subjects were recruited; all received a standardized assessment (i.e., computerized test battery) assessing processing speed, spatial skills, calculation, language ability, intelligence, visual attention, and executive function. Groups were compared in these cognitive domains. Simple regression analysis was used to analyze the effects of epilepsy-related clinical variables on cognitive test scores. Compared with control subjects, children with childhood absence epilepsy and benign childhood epilepsy with centrotemporal spikes showed cognitive deficits in intelligence and executive function, but performed normally in language processing. Impairment in visual attention was specific to patients with childhood absence epilepsy, whereas impaired spatial ability was specific to the children with benign childhood epilepsy with centrotemporal spikes. Simple regression analysis showed syndrome-related clinical variables did not affect cognitive functions. This study provides evidence of both common and distinctive cognitive features underlying the relative cognitive difficulties in children with childhood absence epilepsy and benign childhood epilepsy with centrotemporal spikes. Our data suggest that clinicians should pay particular attention to the specific cognitive deficits in children with childhood absence epilepsy and benign childhood epilepsy with centrotemporal spikes, to allow for more discriminative and potentially more effective interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

An empirical investigation of motion effects in eMRI of interictal epileptiform spikes.

PubMed

Sundaram, Padmavathi; Mulkern, Robert V; Wells, William M; Triantafyllou, Christina; Loddenkemper, Tobias; Bubrick, Ellen J; Orbach, Darren B

2011-12-01

We recently developed a functional neuroimaging technique called encephalographic magnetic resonance imaging (eMRI). Our method acquires rapid single-shot gradient-echo echo-planar MRI (repetition time=47 ms); it attempts to measure an MR signal more directly linked to neuronal electromagnetic activity than existing methods. To increase the likelihood of detecting such an MR signal, we recorded concurrent MRI and scalp electroencephalography (EEG) during fast (20-200 ms), localized, high-amplitude (>50 μV on EEG) cortical discharges in a cohort of focal epilepsy patients. Seen on EEG as interictal spikes, these discharges occur in between seizures and induced easily detectable MR magnitude and phase changes concurrent with the spikes with a lag of milliseconds to tens of milliseconds. Due to the time scale of the responses, localized changes in blood flow or hemoglobin oxygenation are unlikely to cause the MR signal changes that we observed. While the precise underlying mechanisms are unclear, in this study, we empirically investigate one potentially important confounding variable - motion. Head motion in the scanner affects both EEG and MR recording. It can produce brief "spike-like" artifacts on EEG and induce large MR signal changes similar to our interictal spike-related signal changes. In order to explore the possibility that interictal spikes were associated with head motions (although such an association had never been reported), we had previously tracked head position in epilepsy patients during interictal spikes and explicitly demonstrated a lack of associated head motion. However, that study was performed outside the MR scanner, and the root-mean-square error in the head position measurement was 0.7 mm. The large inaccuracy in this measurement therefore did not definitively rule out motion as a possible signal generator. In this study, we instructed healthy subjects to make deliberate brief (<500 ms) head motions inside the MR scanner and imaged these head motions with concurrent EEG and MRI. We compared these artifactual MR and EEG data to genuine interictal spikes. While per-voxel MR and per-electrode EEG time courses for the motion case can mimic the corresponding time courses associated with a genuine interictal spike, head motion can be unambiguously differentiated from interictal spikes via scalp EEG potential maps. Motion induces widespread changes in scalp potential, whereas interictal spikes are localized and have a regional fall-off in amplitude. These findings make bulk head motion an unlikely generator of the large spike-related MR signal changes that we had observed. Further work is required to precisely identify the underlying mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Unique Configurations of Compression and Truncation of Neuronal Activity Underlie l-DOPA-Induced Selection of Motor Patterns in Aplysia.

PubMed

Neveu, Curtis L; Costa, Renan M; Homma, Ryota; Nagayama, Shin; Baxter, Douglas A; Byrne, John H

2017-01-01

A key issue in neuroscience is understanding the ways in which neuromodulators such as dopamine modify neuronal activity to mediate selection of distinct motor patterns. We addressed this issue by applying either low or high concentrations of l-DOPA (40 or 250 μM) and then monitoring activity of up to 130 neurons simultaneously in the feeding circuitry of Aplysia using a voltage-sensitive dye (RH-155). l-DOPA selected one of two distinct buccal motor patterns (BMPs): intermediate (low l-DOPA) or bite (high l-DOPA) patterns. The selection of intermediate BMPs was associated with shortening of the second phase of the BMP (retraction), whereas the selection of bite BMPs was associated with shortening of both phases of the BMP (protraction and retraction). Selection of intermediate BMPs was also associated with truncation of individual neuron spike activity (decreased burst duration but no change in spike frequency or burst latency) in neurons active during retraction. In contrast, selection of bite BMPs was associated with compression of spike activity (decreased burst latency and duration and increased spike frequency) in neurons projecting through specific nerves, as well as increased spike frequency of protraction neurons. Finally, large-scale voltage-sensitive dye recordings delineated the spatial distribution of neurons active during BMPs and the modification of that distribution by the two concentrations of l-DOPA.

Unique Configurations of Compression and Truncation of Neuronal Activity Underlie l-DOPA–Induced Selection of Motor Patterns in Aplysia

PubMed Central

Homma, Ryota; Nagayama, Shin; Baxter, Douglas A.

2017-01-01

A key issue in neuroscience is understanding the ways in which neuromodulators such as dopamine modify neuronal activity to mediate selection of distinct motor patterns. We addressed this issue by applying either low or high concentrations of l-DOPA (40 or 250 μM) and then monitoring activity of up to 130 neurons simultaneously in the feeding circuitry of Aplysia using a voltage-sensitive dye (RH-155). l-DOPA selected one of two distinct buccal motor patterns (BMPs): intermediate (low l-DOPA) or bite (high l-DOPA) patterns. The selection of intermediate BMPs was associated with shortening of the second phase of the BMP (retraction), whereas the selection of bite BMPs was associated with shortening of both phases of the BMP (protraction and retraction). Selection of intermediate BMPs was also associated with truncation of individual neuron spike activity (decreased burst duration but no change in spike frequency or burst latency) in neurons active during retraction. In contrast, selection of bite BMPs was associated with compression of spike activity (decreased burst latency and duration and increased spike frequency) in neurons projecting through specific nerves, as well as increased spike frequency of protraction neurons. Finally, large-scale voltage-sensitive dye recordings delineated the spatial distribution of neurons active during BMPs and the modification of that distribution by the two concentrations of l-DOPA. PMID:29071298

Thermodynamics and signatures of criticality in a network of neurons.

PubMed

Tkačik, Gašper; Mora, Thierry; Marre, Olivier; Amodei, Dario; Palmer, Stephanie E; Berry, Michael J; Bialek, William

2015-09-15

The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance.

Analysis of noise-induced temporal correlations in neuronal spike sequences

NASA Astrophysics Data System (ADS)

Reinoso, José A.; Torrent, M. C.; Masoller, Cristina

2016-11-01

We investigate temporal correlations in sequences of noise-induced neuronal spikes, using a symbolic method of time-series analysis. We focus on the sequence of time-intervals between consecutive spikes (inter-spike-intervals, ISIs). The analysis method, known as ordinal analysis, transforms the ISI sequence into a sequence of ordinal patterns (OPs), which are defined in terms of the relative ordering of consecutive ISIs. The ISI sequences are obtained from extensive simulations of two neuron models (FitzHugh-Nagumo, FHN, and integrate-and-fire, IF), with correlated noise. We find that, as the noise strength increases, temporal order gradually emerges, revealed by the existence of more frequent ordinal patterns in the ISI sequence. While in the FHN model the most frequent OP depends on the noise strength, in the IF model it is independent of the noise strength. In both models, the correlation time of the noise affects the OP probabilities but does not modify the most probable pattern.

Magnetoencephalography with temporal spread imaging to visualize propagation of epileptic activity.

PubMed

Shibata, Sumiya; Matsuhashi, Masao; Kunieda, Takeharu; Yamao, Yukihiro; Inano, Rika; Kikuchi, Takayuki; Imamura, Hisaji; Takaya, Shigetoshi; Matsumoto, Riki; Ikeda, Akio; Takahashi, Ryosuke; Mima, Tatsuya; Fukuyama, Hidenao; Mikuni, Nobuhiro; Miyamoto, Susumu

2017-05-01

We describe temporal spread imaging (TSI) that can identify the spatiotemporal pattern of epileptic activity using Magnetoencephalography (MEG). A three-dimensional grid of voxels covering the brain is created. The array-gain minimum-variance spatial filter is applied to an interictal spike to estimate the magnitude of the source and the time (Ta) when the magnitude exceeds a predefined threshold at each voxel. This calculation is performed through all spikes. Each voxel has the mean Ta () and spike number (N sp ), which is the number of spikes whose source exceeds the threshold. Then, a random resampling method is used to determine the cutoff value of N sp for the statistically reproducible pattern of the activity. Finally, all the voxels where the source exceeds the threshold reproducibly shown on the MRI with a color scale representing . Four patients with intractable mesial temporal lobe epilepsy (MTLE) were analyzed. In three patients, the common pattern of the overlap between the propagation and the hypometabolism shown by fluorodeoxyglucose-positron emission tomography (FDG-PET) was identified. TSI can visualize statistically reproducible patterns of the temporal and spatial spread of epileptic activity. TSI can assess the statistical significance of the spatiotemporal pattern based on its reproducibility. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Synchronous behaviour in network model based on human cortico-cortical connections.

PubMed

Protachevicz, Paulo Ricardo; Borges, Rafael Ribaski; Reis, Adriane da Silva; Borges, Fernando da Silva; Iarosz, Kelly Cristina; Caldas, Ibere Luiz; Lameu, Ewandson Luiz; Macau, Elbert Einstein Nehrer; Viana, Ricardo Luiz; Sokolov, Igor M; Ferrari, Fabiano A S; Kurths, Jürgen; Batista, Antonio Marcos

2018-06-22

We consider a network topology according to the cortico-cortical connec- tion network of the human brain, where each cortical area is composed of a random network of adaptive exponential integrate-and-fire neurons. Depending on the parameters, this neuron model can exhibit spike or burst patterns. As a diagnostic tool to identify spike and burst patterns we utilise the coefficient of variation of the neuronal inter-spike interval. In our neuronal network, we verify the existence of spike and burst synchronisation in different cortical areas. Our simulations show that the network arrangement, i.e., its rich-club organisation, plays an important role in the transition of the areas from desynchronous to synchronous behaviours. © 2018 Institute of Physics and Engineering in Medicine.

A point process approach to identifying and tracking transitions in neural spiking dynamics in the subthalamic nucleus of Parkinson's patients

NASA Astrophysics Data System (ADS)

Deng, Xinyi; Eskandar, Emad N.; Eden, Uri T.

2013-12-01

Understanding the role of rhythmic dynamics in normal and diseased brain function is an important area of research in neural electrophysiology. Identifying and tracking changes in rhythms associated with spike trains present an additional challenge, because standard approaches for continuous-valued neural recordings—such as local field potential, magnetoencephalography, and electroencephalography data—require assumptions that do not typically hold for point process data. Additionally, subtle changes in the history dependent structure of a spike train have been shown to lead to robust changes in rhythmic firing patterns. Here, we propose a point process modeling framework to characterize the rhythmic spiking dynamics in spike trains, test for statistically significant changes to those dynamics, and track the temporal evolution of such changes. We first construct a two-state point process model incorporating spiking history and develop a likelihood ratio test to detect changes in the firing structure. We then apply adaptive state-space filters and smoothers to track these changes through time. We illustrate our approach with a simulation study as well as with experimental data recorded in the subthalamic nucleus of Parkinson's patients performing an arm movement task. Our analyses show that during the arm movement task, neurons underwent a complex pattern of modulation of spiking intensity characterized initially by a release of inhibitory control at 20-40 ms after a spike, followed by a decrease in excitatory influence at 40-60 ms after a spike.

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

PubMed Central

Logiaco, Laureline; Quilodran, René; Procyk, Emmanuel; Arleo, Angelo

2015-01-01

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators. PMID:26266537

Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity.

PubMed

Pedretti, G; Milo, V; Ambrogio, S; Carboni, R; Bianchi, S; Calderoni, A; Ramaswamy, N; Spinelli, A S; Ielmini, D

2017-07-13

Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~10 4 ) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre- and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks.

Application of Savitzky-Golay differentiation filters and Fourier functions to simultaneous determination of cefepime and the co-administered drug, levofloxacin, in spiked human plasma

NASA Astrophysics Data System (ADS)

Abdel-Aziz, Omar; Abdel-Ghany, Maha F.; Nagi, Reham; Abdel-Fattah, Laila

2015-03-01

The present work is concerned with simultaneous determination of cefepime (CEF) and the co-administered drug, levofloxacin (LEV), in spiked human plasma by applying a new approach, Savitzky-Golay differentiation filters, and combined trigonometric Fourier functions to their ratio spectra. The different parameters associated with the calculation of Savitzky-Golay and Fourier coefficients were optimized. The proposed methods were validated and applied for determination of the two drugs in laboratory prepared mixtures and spiked human plasma. The results were statistically compared with reported HPLC methods and were found accurate and precise.

Seizure-onset patterns in focal cortical dysplasia and neurodevelopmental tumors: Relationship with surgical prognosis and neuropathologic subtypes.

PubMed

Lagarde, Stanislas; Bonini, Francesca; McGonigal, Aileen; Chauvel, Patrick; Gavaret, Martine; Scavarda, Didier; Carron, Romain; Régis, Jean; Aubert, Sandrine; Villeneuve, Nathalie; Giusiano, Bernard; Figarella-Branger, Dominique; Trebuchon, Agnès; Bartolomei, Fabrice

2016-09-01

The study of intracerebral electroencephalography (EEG) seizure-onset patterns is crucial to accurately define the epileptogenic zone and guide successful surgical resection. It also raises important pathophysiologic issues concerning mechanisms of seizure generation. Until now, several seizure-onset patterns have been described using distinct recording methods (subdural, depth electrode), mostly in temporal lobe epilepsies or with heterogeneous neocortical lesions. We analyzed data from a cohort of 53 consecutive patients explored by stereoelectroencephalography (SEEG) and with pathologically confirmed malformation of cortical development (MCD; including focal cortical dysplasia [FCD] and neurodevelopmental tumors [NDTs]). We identified six seizure-onset patterns using visual and time-frequency analysis: low-voltage fast activity (LVFA); preictal spiking followed by LVFA; burst of polyspikes followed by LVFA; slow wave/DC shift followed by LVFA; theta/alpha sharp waves; and rhythmic spikes/spike-waves. We found a high prevalence of patterns that included LVFA (83%), indicating nevertheless that LVFA is not a constant characteristic of seizure onset. An association between seizure-onset patterns and histologic types was found (p = 001). The more prevalent patterns were as follows: (1) in FCD type I LVFA (23.1%) and slow wave/baseline shift followed by LVFA (15.4%); (2) in FCD type II burst of polyspikes followed by LVFA (31%), LVFA (27.6%), and preictal spiking followed by LVFA (27.6%); (3) in NDT, LVFA (54.5%). We found that a seizure-onset pattern that included LVFA was associated with favorable postsurgical outcome, but the completeness of the EZ resection was the sole independent predictive variable. Six different seizure-onset patterns can be described in FCD and NDT. Better postsurgical outcome is associated with patterns that incorporate LVFA. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Mode-Locked Spike Trains in Responses of Ventral Cochlear Nucleus Chopper and Onset Neurons to Periodic Stimuli

PubMed Central

Laudanski, Jonathan; Coombes, Stephen; Palmer, Alan R.

2010-01-01

We report evidence of mode-locking to the envelope of a periodic stimulus in chopper units of the ventral cochlear nucleus (VCN). Mode-locking is a generalized description of how responses in periodically forced nonlinear systems can be closely linked to the input envelope, while showing temporal patterns of higher order than seen during pure phase-locking. Re-analyzing a previously unpublished dataset in response to amplitude modulated tones, we find that of 55% of cells (6/11) demonstrated stochastic mode-locking in response to sinusoidally amplitude modulated (SAM) pure tones at 50% modulation depth. At 100% modulation depth SAM, most units (3/4) showed mode-locking. We use interspike interval (ISI) scattergrams to unravel the temporal structure present in chopper mode-locked responses. These responses compared well to a leaky integrate-and-fire model (LIF) model of chopper units. Thus the timing of spikes in chopper unit responses to periodic stimuli can be understood in terms of the complex dynamics of periodically forced nonlinear systems. A larger set of onset (33) and chopper units (24) of the VCN also shows mode-locked responses to steady-state vowels and cosine-phase harmonic complexes. However, while 80% of chopper responses to complex stimuli meet our criterion for the presence of mode-locking, only 40% of onset cells show similar complex-modes of spike patterns. We found a correlation between a unit's regularity and its tendency to display mode-locked spike trains as well as a correlation in the number of spikes per cycle and the presence of complex-modes of spike patterns. These spiking patterns are sensitive to the envelope as well as the fundamental frequency of complex sounds, suggesting that complex cell dynamics may play a role in encoding periodic stimuli and envelopes in the VCN. PMID:20042702

Single-unit muscle sympathetic nervous activity and its relation to cardiac noradrenaline spillover

PubMed Central

Lambert, Elisabeth A; Schlaich, Markus P; Dawood, Tye; Sari, Carolina; Chopra, Reena; Barton, David A; Kaye, David M; Elam, Mikael; Esler, Murray D; Lambert, Gavin W

2011-01-01

Abstract Recent work using single-unit sympathetic nerve recording techniques has demonstrated aberrations in the firing pattern of sympathetic nerves in a variety of patient groups. We sought to examine whether nerve firing pattern is associated with increased noradrenaline release. Using single-unit muscle sympathetic nerve recording techniques coupled with direct cardiac catheterisation and noradrenaline isotope dilution methodology we examined the relationship between single-unit firing patterns and cardiac and whole body noradrenaline spillover to plasma. Participants comprised patients with hypertension (n = 6), depression (n = 7) and panic disorder (n = 9) who were drawn from our ongoing studies. The patient groups examined did not differ in their single-unit muscle sympathetic nerve firing characteristics nor in the rate of spillover of noradrenaline to plasma from the heart. The median incidence of multiple spikes per beat was 9%. Patients were stratified according to the firing pattern: low level of incidence (less than 9% incidence of multiple spikes per beat) and high level of incidence (greater than 9% incidence of multiple spikes per beat). High incidence of multiple spikes within a cardiac cycle was associated with higher firing rates (P < 0.0001) and increased probability of firing (P < 0.0001). Whole body noradrenaline spillover to plasma and (multi-unit) muscle sympathetic nerve activity in subjects with low incidence of multiple spikes was not different to that of those with high incidence of multiple spikes. In those with high incidence of multiple spikes there occurred a parallel activation of the sympathetic outflow to the heart, with cardiac noradrenaline spillover to plasma being two times that of subjects with low nerve firing rates (11.0 ± 1.5 vs. 22.0 ± 4.5 ng min−1, P < 0.05). This study indicates that multiple within-burst firing and increased single-unit firing rates of the sympathetic outflow to the skeletal muscle vasculature is associated with high cardiac noradrenaline spillover. PMID:21486790

Emergence of spike correlations in periodically forced excitable systems

NASA Astrophysics Data System (ADS)

Reinoso, José A.; Torrent, M. C.; Masoller, Cristina

2016-09-01

In sensory neurons the presence of noise can facilitate the detection of weak information-carrying signals, which are encoded and transmitted via correlated sequences of spikes. Here we investigate the relative temporal order in spike sequences induced by a subthreshold periodic input in the presence of white Gaussian noise. To simulate the spikes, we use the FitzHugh-Nagumo model and to investigate the output sequence of interspike intervals (ISIs), we use the symbolic method of ordinal analysis. We find different types of relative temporal order in the form of preferred ordinal patterns that depend on both the strength of the noise and the period of the input signal. We also demonstrate a resonancelike behavior, as certain periods and noise levels enhance temporal ordering in the ISI sequence, maximizing the probability of the preferred patterns. Our findings could be relevant for understanding the mechanisms underlying temporal coding, by which single sensory neurons represent in spike sequences the information about weak periodic stimuli.

Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

PubMed Central

Bennett, James E. M.; Bair, Wyeth

2015-01-01

Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli. PMID:26308406

Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity.

PubMed

Bennett, James E M; Bair, Wyeth

2015-08-01

Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli.

Power-Law Dynamics of Membrane Conductances Increase Spiking Diversity in a Hodgkin-Huxley Model.

PubMed

Teka, Wondimu; Stockton, David; Santamaria, Fidel

2016-03-01

We studied the effects of non-Markovian power-law voltage dependent conductances on the generation of action potentials and spiking patterns in a Hodgkin-Huxley model. To implement slow-adapting power-law dynamics of the gating variables of the potassium, n, and sodium, m and h, conductances we used fractional derivatives of order η≤1. The fractional derivatives were used to solve the kinetic equations of each gate. We systematically classified the properties of each gate as a function of η. We then tested if the full model could generate action potentials with the different power-law behaving gates. Finally, we studied the patterns of action potential that emerged in each case. Our results show the model produces a wide range of action potential shapes and spiking patterns in response to constant current stimulation as a function of η. In comparison with the classical model, the action potential shapes for power-law behaving potassium conductance (n gate) showed a longer peak and shallow hyperpolarization; for power-law activation of the sodium conductance (m gate), the action potentials had a sharp rise time; and for power-law inactivation of the sodium conductance (h gate) the spikes had wider peak that for low values of η replicated pituitary- and cardiac-type action potentials. With all physiological parameters fixed a wide range of spiking patterns emerged as a function of the value of the constant input current and η, such as square wave bursting, mixed mode oscillations, and pseudo-plateau potentials. Our analyses show that the intrinsic memory trace of the fractional derivative provides a negative feedback mechanism between the voltage trace and the activity of the power-law behaving gate variable. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron.

Inference of neuronal network spike dynamics and topology from calcium imaging data

PubMed Central

Lütcke, Henry; Gerhard, Felipe; Zenke, Friedemann; Gerstner, Wulfram; Helmchen, Fritjof

2013-01-01

Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence (“spike trains”) from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties. PMID:24399936

Characterization of emergent synaptic topologies in noisy neural networks

NASA Astrophysics Data System (ADS)

Miller, Aaron James

Learned behaviors are one of the key contributors to an animal's ultimate survival. It is widely believed that the brain's microcircuitry undergoes structural changes when a new behavior is learned. In particular, motor learning, during which an animal learns a sequence of muscular movements, often requires precisely-timed coordination between muscles and becomes very natural once ingrained. Experiments show that neurons in the motor cortex exhibit precisely-timed spike activity when performing a learned motor behavior, and constituent stereotypical elements of the behavior can last several hundred milliseconds. The subject of this manuscript concerns how organized synaptic structures that produce stereotypical spike sequences emerge from random, dynamical networks. After a brief introduction in Chapter 1, we begin Chapter 2 by introducing a spike-timing-dependent plasticity (STDP) rule that defines how the activity of the network drives changes in network topology. The rule is then applied to idealized networks of leaky integrate-and-fire neurons (LIF). These neurons are not subjected to the variability that typically characterize neurons in vivo. In noiseless networks, synapses develop closed loops of strong connectivity that reproduce stereotypical, precisely-timed spike patterns from an initially random network. We demonstrate the characteristics of the asymptotic synaptic configuration are dependent on the statistics of the initial random network. The spike timings of the neurons simulated in Chapter 2 are generated exactly by a computationally economical, nonlinear mapping which is extended to LIF neurons injected with fluctuating current in Chapter 3. Development of an economical mapping that incorporates noise provides a practical solution to the long simulation times required to produce asymptotic synaptic topologies in networks with STDP in the presence of realistic neuronal variability. The mapping relies on generating numerical solutions to the dynamics of a LIF neuron subjected to Gaussian white noise (GWN). The system reduces to the Ornstein-Uhlenbeck first passage time problem, the solution of which we build into the mapping method of Chapter 2. We demonstrate that simulations using the stochastic mapping have reduced computation time compared to traditional Runge-Kutta methods by more than a factor of 150. In Chapter 4, we use the stochastic mapping to study the dynamics of emerging synaptic topologies in noisy networks. With the addition of membrane noise, networks with dynamical synapses can admit states in which the distribution of the synaptic weights is static under spontaneous activity, but the random connectivity between neurons is dynamical. The widely cited problem of instabilities in networks with STDP is avoided with the implementation of a synaptic decay and an activation threshold on each synapse. When such networks are presented with stimulus modeled by a focused excitatory current, chain-like networks can emerge with the addition of an axon-remodeling plasticity rule, a topological constraint on the connectivity modeling the finite resources available to each neuron. The emergent topologies are the result of an iterative stochastic process. The dynamics of the growth process suggest a strong interplay between the network topology and the spike sequences they produce during development. Namely, the existence of an embedded spike sequence alters the distribution of synaptic weights through the entire network. The roles of model parameters that affect the interplay between network structure and activity are elucidated. Finally, we propose two mathematical growth models, which are complementary, that capture the essence of the growth dynamics observed in simulations. In Chapter 5, we present an extension of the stochastic mapping that allows the possibility of neuronal cooperation. We demonstrate that synaptic topologies admitting stereotypical sequences can emerge in yet higher, biologically realistic levels of membrane potential variability when neurons cooperate to innervate shared targets. The structure that is most robust to the variability is that of a synfire chain. The principles of growth dynamics detailed in Chapter 4 are the same that sculpt the emergent synfire topologies. We conclude by discussing avenues for extensions of these results.

The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

PubMed

Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet

2011-06-01

The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.

On the performance of voltage stepping for the simulation of adaptive, nonlinear integrate-and-fire neuronal networks.

PubMed

Kaabi, Mohamed Ghaith; Tonnelier, Arnaud; Martinez, Dominique

2011-05-01

In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced an approximate event-driven strategy, named voltage stepping, that allows the generic simulation of nonlinear spiking neurons. Promising results were achieved in the simulation of single quadratic integrate-and-fire neurons. Here, we assess the performance of voltage stepping in network simulations by considering more complex neurons (quadratic integrate-and-fire neurons with adaptation) coupled with multiple synapses. To handle the discrete nature of synaptic interactions, we recast voltage stepping in a general framework, the discrete event system specification. The efficiency of the method is assessed through simulations and comparisons with a modified time-stepping scheme of the Runge-Kutta type. We demonstrated numerically that the original order of voltage stepping is preserved when simulating connected spiking neurons, independent of the network activity and connectivity.

Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

PubMed Central

Palma, Jesse; Grossberg, Stephen; Versace, Massimiliano

2012-01-01

Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM). Theorems in the 1970's showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP) currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh) can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 ms or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all (WTA) stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners when the network stabilizes. PMID:22754524

Behavioral and Single-Neuron Sensitivity to Millisecond Variations in Temporally Patterned Communication Signals

PubMed Central

Baker, Christa A.; Ma, Lisa; Casareale, Chelsea R.

2016-01-01

In many sensory pathways, central neurons serve as temporal filters for timing patterns in communication signals. However, how a population of neurons with diverse temporal filtering properties codes for natural variation in communication signals is unknown. Here we addressed this question in the weakly electric fish Brienomyrus brachyistius, which varies the time intervals between successive electric organ discharges to communicate. These fish produce an individually stereotyped signal called a scallop, which consists of a distinctive temporal pattern of ∼8–12 electric pulses. We manipulated the temporal structure of natural scallops during behavioral playback and in vivo electrophysiology experiments to probe the temporal sensitivity of scallop encoding and recognition. We found that presenting time-reversed, randomized, or jittered scallops increased behavioral response thresholds, demonstrating that fish's electric signaling behavior was sensitive to the precise temporal structure of scallops. Next, using in vivo intracellular recordings and discriminant function analysis, we found that the responses of interval-selective midbrain neurons were also sensitive to the precise temporal structure of scallops. Subthreshold changes in membrane potential recorded from single neurons discriminated natural scallops from time-reversed, randomized, and jittered sequences. Pooling the responses of multiple neurons improved the discriminability of natural sequences from temporally manipulated sequences. Finally, we found that single-neuron responses were sensitive to interindividual variation in scallop sequences, raising the question of whether fish may analyze scallop structure to gain information about the sender. Collectively, these results demonstrate that a population of interval-selective neurons can encode behaviorally relevant temporal patterns with millisecond precision. SIGNIFICANCE STATEMENT The timing patterns of action potentials, or spikes, play important roles in representing information in the nervous system. However, how these temporal patterns are recognized by downstream neurons is not well understood. Here we use the electrosensory system of mormyrid weakly electric fish to investigate how a population of neurons with diverse temporal filtering properties encodes behaviorally relevant input timing patterns, and how this relates to behavioral sensitivity. We show that fish are behaviorally sensitive to millisecond variations in natural, temporally patterned communication signals, and that the responses of individual midbrain neurons are also sensitive to variation in these patterns. In fact, the output of single neurons contains enough information to discriminate stereotyped communication signals produced by different individuals. PMID:27559179

Behavioral and Single-Neuron Sensitivity to Millisecond Variations in Temporally Patterned Communication Signals.

PubMed

Baker, Christa A; Ma, Lisa; Casareale, Chelsea R; Carlson, Bruce A

2016-08-24

In many sensory pathways, central neurons serve as temporal filters for timing patterns in communication signals. However, how a population of neurons with diverse temporal filtering properties codes for natural variation in communication signals is unknown. Here we addressed this question in the weakly electric fish Brienomyrus brachyistius, which varies the time intervals between successive electric organ discharges to communicate. These fish produce an individually stereotyped signal called a scallop, which consists of a distinctive temporal pattern of ∼8-12 electric pulses. We manipulated the temporal structure of natural scallops during behavioral playback and in vivo electrophysiology experiments to probe the temporal sensitivity of scallop encoding and recognition. We found that presenting time-reversed, randomized, or jittered scallops increased behavioral response thresholds, demonstrating that fish's electric signaling behavior was sensitive to the precise temporal structure of scallops. Next, using in vivo intracellular recordings and discriminant function analysis, we found that the responses of interval-selective midbrain neurons were also sensitive to the precise temporal structure of scallops. Subthreshold changes in membrane potential recorded from single neurons discriminated natural scallops from time-reversed, randomized, and jittered sequences. Pooling the responses of multiple neurons improved the discriminability of natural sequences from temporally manipulated sequences. Finally, we found that single-neuron responses were sensitive to interindividual variation in scallop sequences, raising the question of whether fish may analyze scallop structure to gain information about the sender. Collectively, these results demonstrate that a population of interval-selective neurons can encode behaviorally relevant temporal patterns with millisecond precision. The timing patterns of action potentials, or spikes, play important roles in representing information in the nervous system. However, how these temporal patterns are recognized by downstream neurons is not well understood. Here we use the electrosensory system of mormyrid weakly electric fish to investigate how a population of neurons with diverse temporal filtering properties encodes behaviorally relevant input timing patterns, and how this relates to behavioral sensitivity. We show that fish are behaviorally sensitive to millisecond variations in natural, temporally patterned communication signals, and that the responses of individual midbrain neurons are also sensitive to variation in these patterns. In fact, the output of single neurons contains enough information to discriminate stereotyped communication signals produced by different individuals. Copyright © 2016 the authors 0270-6474/16/368985-16$15.00/0.

Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

PubMed

Mostafa, Hesham

2017-08-01

Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

Spiking Neurons for Analysis of Patterns

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance

2008-01-01

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Addition of deep brain stimulation signal to a local field potential driven Izhikevich model masks the pathological firing pattern of an STN neuron.

PubMed

Michmizos, Kostis P; Nikita, Konstantina S

2011-01-01

The crucial engagement of the subthalamic nucleus (STN) with the neurosurgical procedure of deep brain stimulation (DBS) that alleviates medically intractable Parkinsonian tremor augments the need to refine our current understanding of STN. To enhance the efficacy of DBS as a result of precise targeting, STN boundaries are accurately mapped using extracellular microelectrode recordings (MERs). We utilized the intranuclear MER to acquire the local field potential (LFP) and drive an Izhikevich model of an STN neuron. Using the model as the test bed for clinically acquired data, we demonstrated that stimulation of the STN neuron produces excitatory responses that tonically increase its average firing rate and alter the pattern of its neuronal activity. We also found that the spiking rhythm increases linearly with the increase of amplitude, frequency, and duration of the DBS pulse, inside the clinical range. Our results are in agreement with the current hypothesis that DBS increases the firing rate of STN and masks its pathological bursting firing pattern.

Nonlinear decoding of a complex movie from the mammalian retina

PubMed Central

Deny, Stéphane; Martius, Georg

2018-01-01

Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed “pixel-by-pixel”. We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains. PMID:29746463

Fitting Neuron Models to Spike Trains

PubMed Central

Rossant, Cyrille; Goodman, Dan F. M.; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K.; Brette, Romain

2011-01-01

Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. PMID:21415925

Mental arithmetic leads to multiple discrete changes from baseline in the firing patterns of human thalamic neurons.

PubMed

Kim, J H; Ohara, S; Lenz, F A

2009-04-01

Primate thalamic action potential bursts associated with low-threshold spikes (LTS) occur during waking sensory and motor activity. We now test the hypothesis that different firing and LTS burst characteristics occur during quiet wakefulness (spontaneous condition) versus mental arithmetic (counting condition). This hypothesis was tested by thalamic recordings during the surgical treatment of tremor. Across all neurons and epochs, preburst interspike intervals (ISIs) were bimodal at median values, consistent with the duration of type A and type B gamma-aminobutyric acid inhibitory postsynaptic potentials. Neuronal spike trains (117 neurons) were categorized by joint ISI distributions into those firing as LTS bursts (G, grouped), firing as single spikes (NG, nongrouped), or firing as single spikes with sporadic LTS bursting (I, intermediate). During the spontaneous condition (46 neurons) only I spike trains changed category. Overall, burst rates (BRs) were lower and firing rates (FRs) were higher during the counting versus the spontaneous condition. Spike trains in the G category sometimes changed to I and NG categories at the transition from the spontaneous to the counting condition, whereas those in the I category often changed to NG. Among spike trains that did not change category by condition, G spike trains had lower BRs during counting, whereas NG spike trains had higher FRs. BRs were significantly greater than zero for G and I categories during wakefulness (both conditions). The changes between the spontaneous and counting conditions are most pronounced for the I category, which may be a transitional firing pattern between the bursting (G) and relay modes of thalamic firing (NG).

Fractional-order leaky integrate-and-fire model with long-term memory and power law dynamics.

PubMed

Teka, Wondimu W; Upadhyay, Ranjit Kumar; Mondal, Argha

2017-09-01

Pyramidal neurons produce different spiking patterns to process information, communicate with each other and transform information. These spiking patterns have complex and multiple time scale dynamics that have been described with the fractional-order leaky integrate-and-Fire (FLIF) model. Models with fractional (non-integer) order differentiation that generalize power law dynamics can be used to describe complex temporal voltage dynamics. The main characteristic of FLIF model is that it depends on all past values of the voltage that causes long-term memory. The model produces spikes with high interspike interval variability and displays several spiking properties such as upward spike-frequency adaptation and long spike latency in response to a constant stimulus. We show that the subthreshold voltage and the firing rate of the fractional-order model make transitions from exponential to power law dynamics when the fractional order α decreases from 1 to smaller values. The firing rate displays different types of spike timing adaptation caused by changes on initial values. We also show that the voltage-memory trace and fractional coefficient are the causes of these different types of spiking properties. The voltage-memory trace that represents the long-term memory has a feedback regulatory mechanism and affects spiking activity. The results suggest that fractional-order models might be appropriate for understanding multiple time scale neuronal dynamics. Overall, a neuron with fractional dynamics displays history dependent activities that might be very useful and powerful for effective information processing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of Savitzky-Golay differentiation filters and Fourier functions to simultaneous determination of cefepime and the co-administered drug, levofloxacin, in spiked human plasma.

PubMed

Abdel-Aziz, Omar; Abdel-Ghany, Maha F; Nagi, Reham; Abdel-Fattah, Laila

2015-03-15

The present work is concerned with simultaneous determination of cefepime (CEF) and the co-administered drug, levofloxacin (LEV), in spiked human plasma by applying a new approach, Savitzky-Golay differentiation filters, and combined trigonometric Fourier functions to their ratio spectra. The different parameters associated with the calculation of Savitzky-Golay and Fourier coefficients were optimized. The proposed methods were validated and applied for determination of the two drugs in laboratory prepared mixtures and spiked human plasma. The results were statistically compared with reported HPLC methods and were found accurate and precise. Copyright © 2014 Elsevier B.V. All rights reserved.

Large-scale recording of neuronal ensembles.

PubMed

Buzsáki, György

2004-05-01

How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.

A Neuro-Musculo-Skeletal Model for Insects With Data-driven Optimization.

PubMed

Guo, Shihui; Lin, Juncong; Wöhrl, Toni; Liao, Minghong

2018-02-01

Simulating the locomotion of insects is beneficial to many areas such as experimental biology, computer animation and robotics. This work proposes a neuro-musculo-skeletal model, which integrates the biological inspirations from real insects and reproduces the gait pattern on virtual insects. The neural system is a network of spiking neurons, whose spiking patterns are controlled by the input currents. The spiking pattern provides a uniform representation of sensory information, high-level commands and control strategy. The muscle models are designed following the characteristic Hill-type muscle with customized force-length and force-velocity relationships. The model parameters, including both the neural and muscular components, are optimized via an approach of evolutionary optimization, with the data captured from real insects. The results show that the simulated gait pattern, including joint trajectories, matches the experimental data collected from real ants walking in the free mode. The simulated character is capable of moving at different directions and traversing uneven terrains.

Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity

PubMed Central

Sahasranamam, Ajith; Vlachos, Ioannis; Aertsen, Ad; Kumar, Arvind

2016-01-01

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points. PMID:27212008

Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity.

PubMed

Sahasranamam, Ajith; Vlachos, Ioannis; Aertsen, Ad; Kumar, Arvind

2016-05-23

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.

A double-spike method for K-Ar measurement: A technique for high precision in situ dating on Mars and other planetary surfaces

NASA Astrophysics Data System (ADS)

Farley, K. A.; Hurowitz, J. A.; Asimow, P. D.; Jacobson, N. S.; Cartwright, J. A.

2013-06-01

A new method for K-Ar dating using a double isotope dilution technique is proposed and demonstrated. The method is designed to eliminate known difficulties facing in situ dating on planetary surfaces, especially instrument complexity and power availability. It may also have applicability in some terrestrial dating applications. Key to the method is the use of a solid tracer spike enriched in both 39Ar and 41K. When mixed with lithium borate flux in a Knudsen effusion cell, this tracer spike and a sample to be dated can be successfully fused and degassed of Ar at <1000 °C. The evolved 40Ar∗/39Ar ratio can be measured to high precision using noble gas mass spectrometry. After argon measurement the sample melt is heated to a slightly higher temperature (˜1030 °C) to volatilize potassium, and the evolved 39K/41K ratio measured by Knudsen effusion mass spectrometry. Combined with the known composition of the tracer spike, these two ratios define the K-Ar age using a single sample aliquot and without the need for extreme temperature or a mass determination. In principle the method can be implemented using a single mass spectrometer. Experiments indicate that quantitative extraction of argon from a basalt sample occurs at a sufficiently low temperature that potassium loss in this step is unimportant. Similarly, potassium isotope ratios measured in the Knudsen apparatus indicate good sample-spike equilibration and acceptably small isotopic fractionation. When applied to a flood basalt from the Viluy Traps, Siberia, a K-Ar age of 351 ± 19 Ma was obtained, a result within 1% of the independently known age. For practical reasons this measurement was made on two separate mass spectrometers, but a scheme for combining the measurements in a single analytical instrument is described. Because both parent and daughter are determined by isotope dilution, the precision on K-Ar ages obtained by the double isotope dilution method should routinely approach that of a pair of isotope ratio determinations, likely better than ±5%.

Cochlear spike synchronization and neuron coincidence detection model

NASA Astrophysics Data System (ADS)

Bader, Rolf

2018-02-01

Coincidence detection of a spike pattern fed from the cochlea into a single neuron is investigated using a physical Finite-Difference model of the cochlea and a physiologically motivated neuron model. Previous studies have shown experimental evidence of increased spike synchronization in the nucleus cochlearis and the trapezoid body [Joris et al., J. Neurophysiol. 71(3), 1022-1036 and 1037-1051 (1994)] and models show tone partial phase synchronization at the transition from mechanical waves on the basilar membrane into spike patterns [Ch. F. Babbs, J. Biophys. 2011, 435135]. Still the traveling speed of waves on the basilar membrane cause a frequency-dependent time delay of simultaneously incoming sound wavefronts up to 10 ms. The present model shows nearly perfect synchronization of multiple spike inputs as neuron outputs with interspike intervals (ISI) at the periodicity of the incoming sound for frequencies from about 30 to 300 Hz for two different amounts of afferent nerve fiber neuron inputs. Coincidence detection serves here as a fusion of multiple inputs into one single event enhancing pitch periodicity detection for low frequencies, impulse detection, or increased sound or speech intelligibility due to dereverberation.

Linear and quadratic models of point process systems: contributions of patterned input to output.

PubMed

Lindsay, K A; Rosenberg, J R

2012-08-01

In the 1880's Volterra characterised a nonlinear system using a functional series connecting continuous input and continuous output. Norbert Wiener, in the 1940's, circumvented problems associated with the application of Volterra series to physical problems by deriving from it a new series of terms that are mutually uncorrelated with respect to Gaussian processes. Subsequently, Brillinger, in the 1970's, introduced a point-process analogue of Volterra's series connecting point-process inputs to the instantaneous rate of point-process output. We derive here a new series from this analogue in which its terms are mutually uncorrelated with respect to Poisson processes. This new series expresses how patterned input in a spike train, represented by third-order cross-cumulants, is converted into the instantaneous rate of an output point-process. Given experimental records of suitable duration, the contribution of arbitrary patterned input to an output process can, in principle, be determined. Solutions for linear and quadratic point-process models with one and two inputs and a single output are investigated. Our theoretical results are applied to isolated muscle spindle data in which the spike trains from the primary and secondary endings from the same muscle spindle are recorded in response to stimulation of one and then two static fusimotor axons in the absence and presence of a random length change imposed on the parent muscle. For a fixed mean rate of input spikes, the analysis of the experimental data makes explicit which patterns of two input spikes contribute to an output spike. Copyright © 2012 Elsevier Ltd. All rights reserved.

A case for spiking neural network simulation based on configurable multiple-FPGA systems.

PubMed

Yang, Shufan; Wu, Qiang; Li, Renfa

2011-09-01

Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

A 16-Channel Nonparametric Spike Detection ASIC Based on EC-PC Decomposition.

PubMed

Wu, Tong; Xu, Jian; Lian, Yong; Khalili, Azam; Rastegarnia, Amir; Guan, Cuntai; Yang, Zhi

2016-02-01

In extracellular neural recording experiments, detecting neural spikes is an important step for reliable information decoding. A successful implementation in integrated circuits can achieve substantial data volume reduction, potentially enabling a wireless operation and closed-loop system. In this paper, we report a 16-channel neural spike detection chip based on a customized spike detection method named as exponential component-polynomial component (EC-PC) algorithm. This algorithm features a reliable prediction of spikes by applying a probability threshold. The chip takes raw data as input and outputs three data streams simultaneously: field potentials, band-pass filtered neural data, and spiking probability maps. The algorithm parameters are on-chip configured automatically based on input data, which avoids manual parameter tuning. The chip has been tested with both in vivo experiments for functional verification and bench-top experiments for quantitative performance assessment. The system has a total power consumption of 1.36 mW and occupies an area of 6.71 mm (2) for 16 channels. When tested on synthesized datasets with spikes and noise segments extracted from in vivo preparations and scaled according to required precisions, the chip outperforms other detectors. A credit card sized prototype board is developed to provide power and data management through a USB port.

Critical Slowing Down Governs the Transition to Neuron Spiking

PubMed Central

Meisel, Christian; Klaus, Andreas; Kuehn, Christian; Plenz, Dietmar

2015-01-01

Many complex systems have been found to exhibit critical transitions, or so-called tipping points, which are sudden changes to a qualitatively different system state. These changes can profoundly impact the functioning of a system ranging from controlled state switching to a catastrophic break-down; signals that predict critical transitions are therefore highly desirable. To this end, research efforts have focused on utilizing qualitative changes in markers related to a system’s tendency to recover more slowly from a perturbation the closer it gets to the transition—a phenomenon called critical slowing down. The recently studied scaling of critical slowing down offers a refined path to understand critical transitions: to identify the transition mechanism and improve transition prediction using scaling laws. Here, we outline and apply this strategy for the first time in a real-world system by studying the transition to spiking in neurons of the mammalian cortex. The dynamical system approach has identified two robust mechanisms for the transition from subthreshold activity to spiking, saddle-node and Hopf bifurcation. Although theory provides precise predictions on signatures of critical slowing down near the bifurcation to spiking, quantitative experimental evidence has been lacking. Using whole-cell patch-clamp recordings from pyramidal neurons and fast-spiking interneurons, we show that 1) the transition to spiking dynamically corresponds to a critical transition exhibiting slowing down, 2) the scaling laws suggest a saddle-node bifurcation governing slowing down, and 3) these precise scaling laws can be used to predict the bifurcation point from a limited window of observation. To our knowledge this is the first report of scaling laws of critical slowing down in an experiment. They present a missing link for a broad class of neuroscience modeling and suggest improved estimation of tipping points by incorporating scaling laws of critical slowing down as a strategy applicable to other complex systems. PMID:25706912

Irregular synchronous activity in stochastically-coupled networks of integrate-and-fire neurons.

PubMed

Lin, J K; Pawelzik, K; Ernst, U; Sejnowski, T J

1998-08-01

We investigate the spatial and temporal aspects of firing patterns in a network of integrate-and-fire neurons arranged in a one-dimensional ring topology. The coupling is stochastic and shaped like a Mexican hat with local excitation and lateral inhibition. With perfect precision in the couplings, the attractors of activity in the network occur at every position in the ring. Inhomogeneities in the coupling break the translational invariance of localized attractors and lead to synchronization within highly active as well as weakly active clusters. The interspike interval variability is high, consistent with recent observations of spike time distributions in visual cortex. The robustness of our results is demonstrated with more realistic simulations on a network of McGregor neurons which model conductance changes and after-hyperpolarization potassium currents.

Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device.

PubMed

McKinstry, Jeffrey L; Edelman, Gerald M

2013-01-01

Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.

Comparing neuronal spike trains with inhomogeneous Poisson distribution: evaluation procedure and experimental application in cases of cyclic activity.

PubMed

Fiore, Lorenzo; Lorenzetti, Walter; Ratti, Giovannino

2005-11-30

A procedure is proposed to compare single-unit spiking activity elicited in repetitive cycles with an inhomogeneous Poisson process (IPP). Each spike sequence in a cycle is discretized and represented as a point process on a circle. The interspike interval probability density predicted for an IPP is computed on the basis of the experimental firing probability density; differences from the experimental interval distribution are assessed. This procedure was applied to spike trains which were repetitively induced by opening-closing movements of the distal article of a lobster leg. As expected, the density of short interspike intervals, less than 20-40 ms in length, was found to lie greatly below the level predicted for an IPP, reflecting the occurrence of the refractory period. Conversely, longer intervals, ranging from 20-40 to 100-120 ms, were markedly more abundant than expected; this provided evidence for a time window of increased tendency to fire again after a spike. Less consistently, a weak depression of spike generation was observed for longer intervals. A Monte Carlo procedure, implemented for comparison, produced quite similar results, but was slightly less precise and more demanding as concerns computation time.

Electroporation followed by electrochemical measurement of quantal transmitter release from single cells using a patterned microelectrode.

PubMed

Ghosh, Jaya; Liu, Xin; Gillis, Kevin D

2013-06-07

An electrochemical microelectrode located immediately adjacent to a single neuroendocrine cell can record spikes of amperometric current that result from exocytosis of oxidizable transmitter from individual vesicles, i.e., quantal exocytosis. Here, we report the development of an efficient method where the same electrochemical microelectrode is used to electropermeabilize an adjacent chromaffin cell and then measure the consequent quantal catecholamine release using amperometry. Trains of voltage pulses, 5-7 V in amplitude and 0.1-0.2 ms in duration, were used to reliably trigger release from cells using gold electrodes. Amperometric spikes induced by electropermeabilization had similar areas, peak heights and durations as amperometric spikes elicited by depolarizing high K(+) solutions, therefore release occurs from individual secretory granules. Uptake of trypan blue stain into cells demonstrated that the plasma membrane is permeabilized by the voltage stimulus. Voltage pulses did not degrade the electrochemical sensitivity of the electrodes assayed using a test analyte. Surprisingly, robust quantal release was elicited upon electroporation in the absence of Ca(2+) in the bath solution (0 Ca(2+)/5 mM EGTA). In contrast, electropermeabilization-induced transmitter release required Cl(-) in the bath solution in that bracketed experiments demonstrated a steep dependence of the rate of electropermeabilization-induced transmitter release on [Cl(-)] between 2 and 32 mM. Using the same electrochemical electrode to electroporate and record quantal release of catecholamines from an individual chromaffin cell allows precise timing of the stimulus, stimulation of a single cell at a time, and can be used to load membrane-impermeant substances into a cell.

Competitive STDP Learning of Overlapping Spatial Patterns.

PubMed

Krunglevicius, Dalius

2015-08-01

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron's responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron's forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

NASA Astrophysics Data System (ADS)

Pyle, Ryan; Rosenbaum, Robert

2017-01-01

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

Burst Firing in Bee Gustatory Neurons Prevents Adaptation.

PubMed

Miriyala, Ashwin; Kessler, Sébastien; Rind, F Claire; Wright, Geraldine A

2018-05-01

Animals detect changes in the environment using modality-specific, peripheral sensory neurons. The insect gustatory system encodes tastant identity and concentration through the independent firing of gustatory receptor neurons (GRNs) that spike rapidly at stimulus onset and quickly adapt. Here, we show the first evidence that concentrated sugar evokes a temporally structured burst pattern of spiking involving two GRNs within the gustatory sensilla of bumblebees. Bursts of spikes resulted when a sucrose-activated GRN was inhibited by another GRN at a frequency of ∼22 Hz during the first 1 s of stimulation. Pharmacological blockade of gap junctions abolished bursting, indicating that bee GRNs have electrical synapses that produce a temporal pattern of spikes when one GRN is activated by a sugar ligand. Bursting permitted bee GRNs to maintain a high rate of spiking and to exhibit the slowest rate of adaptation of any insect species. Feeding bout duration correlated with coherent bursting; only sugar concentrations that produced bursting evoked the bumblebee's feeding reflex. Volume of solution imbibed was a direct function of time in contact with food. We propose that gap junctions among GRNs enable a sustained rate of GRN spiking that is necessary to drive continuous feeding by the bee proboscis. Copyright © 2018 Elsevier Ltd. All rights reserved.

Short-term memory in olfactory network dynamics

NASA Astrophysics Data System (ADS)

Stopfer, Mark; Laurent, Gilles

1999-12-01

Neural assemblies in a number of animal species display self-organized, synchronized oscillations in response to sensory stimuli in a variety of brain areas.. In the olfactory system of insects, odour-evoked oscillatory synchronization of antennal lobe projection neurons (PNs) is superimposed on slower and stimulus-specific temporal activity patterns. Hence, each odour activates a specific and dynamic projection neuron assembly whose evolution during a stimulus is locked to the oscillation clock. Here we examine, using locusts, the changes in population dynamics of projection-neuron assemblies over repeated odour stimulations, as would occur when an animal first encounters and then repeatedly samples an odour for identification or localization. We find that the responses of these assemblies rapidly decrease in intensity, while they show a marked increase in spike time precision and inter-neuronal oscillatory coherence. Once established, this enhanced precision in the representation endures for several minutes. This change is stimulus-specific, and depends on events within the antennal lobe circuits, independent of olfactory receptor adaptation: it may thus constitute a form of sensory memory. Our results suggest that this progressive change in olfactory network dynamics serves to converge, over repeated odour samplings, on a more precise and readily classifiable odour representation, using relational information contained across neural assemblies.

Simulation of networks of spiking neurons: A review of tools and strategies

PubMed Central

Brette, Romain; Rudolph, Michelle; Carnevale, Ted; Hines, Michael; Beeman, David; Bower, James M.; Diesmann, Markus; Morrison, Abigail; Goodman, Philip H.; Harris, Frederick C.; Zirpe, Milind; Natschläger, Thomas; Pecevski, Dejan; Ermentrout, Bard; Djurfeldt, Mikael; Lansner, Anders; Rochel, Olivier; Vieville, Thierry; Muller, Eilif; Davison, Andrew P.; El Boustani, Sami

2009-01-01

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin–Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks. PMID:17629781

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

NASA Astrophysics Data System (ADS)

Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

2017-10-01

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system.

PubMed

Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

2017-10-06

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

Coincidence detection in the medial superior olive: mechanistic implications of an analysis of input spiking patterns

PubMed Central

Franken, Tom P.; Bremen, Peter; Joris, Philip X.

2014-01-01

Coincidence detection by binaural neurons in the medial superior olive underlies sensitivity to interaural time difference (ITD) and interaural correlation (ρ). It is unclear whether this process is akin to a counting of individual coinciding spikes, or rather to a correlation of membrane potential waveforms resulting from converging inputs from each side. We analyzed spike trains of axons of the cat trapezoid body (TB) and auditory nerve (AN) in a binaural coincidence scheme. ITD was studied by delaying “ipsi-” vs. “contralateral” inputs; ρ was studied by using responses to different noises. We varied the number of inputs; the monaural and binaural threshold and the coincidence window duration. We examined physiological plausibility of output “spike trains” by comparing their rate and tuning to ITD and ρ to those of binaural cells. We found that multiple inputs are required to obtain a plausible output spike rate. In contrast to previous suggestions, monaural threshold almost invariably needed to exceed binaural threshold. Elevation of the binaural threshold to values larger than 2 spikes caused a drastic decrease in rate for a short coincidence window. Longer coincidence windows allowed a lower number of inputs and higher binaural thresholds, but decreased the depth of modulation. Compared to AN fibers, TB fibers allowed higher output spike rates for a low number of inputs, but also generated more monaural coincidences. We conclude that, within the parameter space explored, the temporal patterns of monaural fibers require convergence of multiple inputs to achieve physiological binaural spike rates; that monaural coincidences have to be suppressed relative to binaural ones; and that the neuron has to be sensitive to single binaural coincidences of spikes, for a number of excitatory inputs per side of 10 or less. These findings suggest that the fundamental operation in the mammalian binaural circuit is coincidence counting of single binaural input spikes. PMID:24822037

T-type calcium channels cause bursts of spikes in motor but not sensory thalamic neurons during mimicry of natural patterns of synaptic input.

PubMed

Kim, Haram R; Hong, Su Z; Fiorillo, Christopher D

2015-01-01

Although neurons within intact nervous systems can be classified as 'sensory' or 'motor,' it is not known whether there is any general distinction between sensory and motor neurons at the cellular or molecular levels. Here, we extend and test a theory according to which activation of certain subtypes of voltage-gated ion channel (VGC) generate patterns of spikes in neurons of motor systems, whereas VGC are proposed to counteract patterns in sensory neurons. We previously reported experimental evidence for the theory from visual thalamus, where we found that T-type calcium channels (TtCCs) did not cause bursts of spikes but instead served the function of 'predictive homeostasis' to maximize the causal and informational link between retinogeniculate excitation and spike output. Here, we have recorded neurons in brain slices from eight sensory and motor regions of rat thalamus while mimicking key features of natural excitatory and inhibitory post-synaptic potentials. As predicted by theory, TtCC did cause bursts of spikes in motor thalamus. TtCC-mediated responses in motor thalamus were activated at more hyperpolarized potentials and caused larger depolarizations with more spikes than in visual and auditory thalamus. Somatosensory thalamus is known to be more closely connected to motor regions relative to auditory and visual thalamus, and likewise the strength of its TtCC responses was intermediate between these regions and motor thalamus. We also observed lower input resistance, as well as limited evidence of stronger hyperpolarization-induced ('H-type') depolarization, in nuclei closer to motor output. These findings support our theory of a specific difference between sensory and motor neurons at the cellular level.

Gravimetric Analysis of Particulate Matter using Air Samplers Housing Internal Filtration Capsules.

PubMed

O'Connor, Sean; O'Connor, Paula Fey; Feng, H Amy; Ashley, Kevin

2014-10-01

An evaluation was carried out to investigate the suitability of polyvinyl chloride (PVC) internal capsules, housed within air sampling devices, for gravimetric analysis of airborne particles collected in workplaces. Experiments were carried out using blank PVC capsules and PVC capsules spiked with 0,1 - 4 mg of National Institute of Standards and Technology Standard Reference Material ® (NIST SRM) 1648 (Urban Particulate Matter) and Arizona Road Dust (Air Cleaner Test Dust). The capsules were housed within plastic closed-face cassette samplers (CFCs). A method detection limit (MDL) of 0,075 mg per sample was estimated. Precision S r at 0,5 - 4 mg per sample was 0,031 and the estimated bias was 0,058. Weight stability over 28 days was verified for both blanks and spiked capsules. Independent laboratory testing on blanks and field samples verified long-term weight stability as well as sampling and analysis precision and bias estimates. An overall precision estimate Ŝ rt of 0,059 was obtained. An accuracy measure of ±15,5% was found for the gravimetric method using PVC internal capsules.

Gravimetric Analysis of Particulate Matter using Air Samplers Housing Internal Filtration Capsules

PubMed Central

O'Connor, Sean; O'Connor, Paula Fey; Feng, H. Amy

2015-01-01

Summary An evaluation was carried out to investigate the suitability of polyvinyl chloride (PVC) internal capsules, housed within air sampling devices, for gravimetric analysis of airborne particles collected in workplaces. Experiments were carried out using blank PVC capsules and PVC capsules spiked with 0,1 – 4 mg of National Institute of Standards and Technology Standard Reference Material® (NIST SRM) 1648 (Urban Particulate Matter) and Arizona Road Dust (Air Cleaner Test Dust). The capsules were housed within plastic closed-face cassette samplers (CFCs). A method detection limit (MDL) of 0,075 mg per sample was estimated. Precision Sr at 0,5 - 4 mg per sample was 0,031 and the estimated bias was 0,058. Weight stability over 28 days was verified for both blanks and spiked capsules. Independent laboratory testing on blanks and field samples verified long-term weight stability as well as sampling and analysis precision and bias estimates. An overall precision estimate Ŝrt of 0,059 was obtained. An accuracy measure of ±15,5% was found for the gravimetric method using PVC internal capsules. PMID:26435581

Tellurium Stable Isotope Fractionation in Chondritic Meteorites

NASA Astrophysics Data System (ADS)

Fehr, M. A.; Hammond, S. J.; Parkinson, I. J.

2014-09-01

New Te double spike procedures were set up to obtain high-precision accurate Te stable isotope data. Tellurium stable isotope data for 16 chondrite falls are presented, providing evidence for significant Te stable isotope fractionation.

Direct linking of Greenland and Antarctic ice cores at the Toba eruption (74 ka BP)

NASA Astrophysics Data System (ADS)

Svensson, A.; Bigler, M.; Blunier, T.; Clausen, H. B.; Dahl-Jensen, D.; Fischer, H.; Fujita, S.; Goto-Azuma, K.; Johnsen, S. J.; Kawamura, K.; Kipfstuhl, S.; Kohno, M.; Parrenin, F.; Popp, T.; Rasmussen, S. O.; Schwander, J.; Seierstad, I.; Severi, M.; Steffensen, J. P.; Udisti, R.; Uemura, R.; Vallelonga, P.; Vinther, B. M.; Wegner, A.; Wilhelms, F.; Winstrup, M.

2013-03-01

The Toba eruption that occurred some 74 ka ago in Sumatra, Indonesia, is among the largest volcanic events on Earth over the last 2 million years. Tephra from this eruption has been spread over vast areas in Asia, where it constitutes a major time marker close to the Marine Isotope Stage 4/5 boundary. As yet, no tephra associated with Toba has been identified in Greenland or Antarctic ice cores. Based on new accurate dating of Toba tephra and on accurately dated European stalagmites, the Toba event is known to occur between the onsets of Greenland interstadials (GI) 19 and 20. Furthermore, the existing linking of Greenland and Antarctic ice cores by gas records and by the bipolar seesaw hypothesis suggests that the Antarctic counterpart is situated between Antarctic Isotope Maxima (AIM) 19 and 20. In this work we suggest a direct synchronization of Greenland (NGRIP) and Antarctic (EDML) ice cores at the Toba eruption based on matching of a pattern of bipolar volcanic spikes. Annual layer counting between volcanic spikes in both cores allows for a unique match. We first demonstrate this bipolar matching technique at the already synchronized Laschamp geomagnetic excursion (41 ka BP) before we apply it to the suggested Toba interval. The Toba synchronization pattern covers some 2000 yr in GI-20 and AIM-19/20 and includes nine acidity peaks that are recognized in both ice cores. The suggested bipolar Toba synchronization has decadal precision. It thus allows a determination of the exact phasing of inter-hemispheric climate in a time interval of poorly constrained ice core records, and it allows for a discussion of the climatic impact of the Toba eruption in a global perspective. The bipolar linking gives no support for a long-term global cooling caused by the Toba eruption as Antarctica experiences a major warming shortly after the event. Furthermore, our bipolar match provides a way to place palaeo-environmental records other than ice cores into a precise climatic context.

Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.

PubMed

Naud, Richard; Gerstner, Wulfram

2012-01-01

The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractoriness and adaptation. As a modeling framework, we use the spike response model, also known as the generalized linear neuron model. Because of refractoriness, the effect of the most recent spike on the spiking probability a few milliseconds later is very strong. The influence of the last spike needs therefore to be described with high precision, while the rest of the neuronal spiking history merely introduces an average self-inhibition or adaptation that depends on the expected number of past spikes but not on the exact spike timings. Based on these insights, we derive a 'quasi-renewal equation' which is shown to yield an excellent description of the firing rate of adapting neurons. We explore the domain of validity of the quasi-renewal equation and compare it with other rate equations for populations of spiking neurons. The problem of decoding the stimulus from the population response (or PSTH) is addressed analogously. We find that for small levels of activity and weak adaptation, a simple accumulator of the past activity is sufficient to decode the original input, but when refractory effects become large decoding becomes a non-linear function of the past activity. The results presented here can be applied to the mean-field analysis of coupled neuron networks, but also to arbitrary point processes with negative self-interaction.

An integrated multi-electrode-optrode array for in vitro optogenetics

PubMed Central

Welkenhuysen, Marleen; Hoffman, Luis; Luo, Zhengxiang; De Proft, Anabel; Van den Haute, Chris; Baekelandt, Veerle; Debyser, Zeger; Gielen, Georges; Puers, Robert; Braeken, Dries

2016-01-01

Modulation of a group of cells or tissue needs to be very precise in order to exercise effective control over the cell population under investigation. Optogenetic tools have already demonstrated to be of great value in the study of neuronal circuits and in neuromodulation. Ideally, they should permit very accurate resolution, preferably down to the single cell level. Further, to address a spatially distributed sample, independently addressable multiple optical outputs should be present. In current techniques, at least one of these requirements is not fulfilled. In addition to this, it is interesting to directly monitor feedback of the modulation by electrical registration of the activity of the stimulated cells. Here, we present the fabrication and characterization of a fully integrated silicon-based multi-electrode-optrode array (MEOA) for in vitro optogenetics. We demonstrate that this device allows for artifact-free electrical recording. Moreover, the MEOA was used to reliably elicit spiking activity from ChR2-transduced neurons. Thanks to the single cell resolution stimulation capability, we could determine spatial and temporal activation patterns and spike latencies of the neuronal network. This integrated approach to multi-site combined optical stimulation and electrical recording significantly advances today’s tool set for neuroscientists in their search to unravel neuronal network dynamics. PMID:26832455

The value of magnetoencephalography for seizure-onset zone localization in magnetic resonance imaging-negative partial epilepsy

PubMed Central

Bouet, Romain; Delpuech, Claude; Ryvlin, Philippe; Isnard, Jean; Guenot, Marc; Bertrand, Olivier; Hammers, Alexander; Mauguière, François

2013-01-01

Surgical treatment of epilepsy is a challenge for patients with non-contributive brain magnetic resonance imaging. However, surgery is feasible if the seizure-onset zone is precisely delineated through intracranial electroencephalography recording. We recently described a method, volumetric imaging of epileptic spikes, to delineate the spiking volume of patients with focal epilepsy using magnetoencephalography. We postulated that the extent of the spiking volume delineated with volumetric imaging of epileptic spikes could predict the localizability of the seizure-onset zone by intracranial electroencephalography investigation and outcome of surgical treatment. Twenty-one patients with non-contributive magnetic resonance imaging findings were included. All patients underwent intracerebral electroencephalography investigation through stereotactically implanted depth electrodes (stereo-electroencephalography) and magnetoencephalography with delineation of the spiking volume using volumetric imaging of epileptic spikes. We evaluated the spatial congruence between the spiking volume determined by magnetoencephalography and the localization of the seizure-onset zone determined by stereo-electroencephalography. We also evaluated the outcome of stereo-electroencephalography and surgical treatment according to the extent of the spiking volume (focal, lateralized but non-focal or non-lateralized). For all patients, we found a spatial overlap between the seizure-onset zone and the spiking volume. For patients with a focal spiking volume, the seizure-onset zone defined by stereo-electroencephalography was clearly localized in all cases and most patients (6/7, 86%) had a good surgical outcome. Conversely, stereo-electroencephalography failed to delineate a seizure-onset zone in 57% of patients with a lateralized spiking volume, and in the two patients with bilateral spiking volume. Four of the 12 patients with non-focal spiking volumes were operated upon, none became seizure-free. As a whole, patients having focal magnetoencephalography results with volumetric imaging of epileptic spikes are good surgical candidates and the implantation strategy should incorporate volumetric imaging of epileptic spikes results. On the contrary, patients with non-focal magnetoencephalography results are less likely to have a localized seizure-onset zone and stereo electroencephalography is not advised unless clear localizing information is provided by other presurgical investigation methods. PMID:24014520

The value of magnetoencephalography for seizure-onset zone localization in magnetic resonance imaging-negative partial epilepsy.

PubMed

Jung, Julien; Bouet, Romain; Delpuech, Claude; Ryvlin, Philippe; Isnard, Jean; Guenot, Marc; Bertrand, Olivier; Hammers, Alexander; Mauguière, François

2013-10-01

Surgical treatment of epilepsy is a challenge for patients with non-contributive brain magnetic resonance imaging. However, surgery is feasible if the seizure-onset zone is precisely delineated through intracranial electroencephalography recording. We recently described a method, volumetric imaging of epileptic spikes, to delineate the spiking volume of patients with focal epilepsy using magnetoencephalography. We postulated that the extent of the spiking volume delineated with volumetric imaging of epileptic spikes could predict the localizability of the seizure-onset zone by intracranial electroencephalography investigation and outcome of surgical treatment. Twenty-one patients with non-contributive magnetic resonance imaging findings were included. All patients underwent intracerebral electroencephalography investigation through stereotactically implanted depth electrodes (stereo-electroencephalography) and magnetoencephalography with delineation of the spiking volume using volumetric imaging of epileptic spikes. We evaluated the spatial congruence between the spiking volume determined by magnetoencephalography and the localization of the seizure-onset zone determined by stereo-electroencephalography. We also evaluated the outcome of stereo-electroencephalography and surgical treatment according to the extent of the spiking volume (focal, lateralized but non-focal or non-lateralized). For all patients, we found a spatial overlap between the seizure-onset zone and the spiking volume. For patients with a focal spiking volume, the seizure-onset zone defined by stereo-electroencephalography was clearly localized in all cases and most patients (6/7, 86%) had a good surgical outcome. Conversely, stereo-electroencephalography failed to delineate a seizure-onset zone in 57% of patients with a lateralized spiking volume, and in the two patients with bilateral spiking volume. Four of the 12 patients with non-focal spiking volumes were operated upon, none became seizure-free. As a whole, patients having focal magnetoencephalography results with volumetric imaging of epileptic spikes are good surgical candidates and the implantation strategy should incorporate volumetric imaging of epileptic spikes results. On the contrary, patients with non-focal magnetoencephalography results are less likely to have a localized seizure-onset zone and stereo electroencephalography is not advised unless clear localizing information is provided by other presurgical investigation methods.

Reduced spike-timing reliability correlates with the emergence of fast ripples in the rat epileptic hippocampus.

PubMed

Foffani, Guglielmo; Uzcategui, Yoryani G; Gal, Beatriz; Menendez de la Prida, Liset

2007-09-20

Ripples are sharp-wave-associated field oscillations (100-300 Hz) recorded in the hippocampus during behavioral immobility and slow-wave sleep. In epileptic rats and humans, a different and faster oscillation (200-600 Hz), termed fast ripples, has been described. However, the basic mechanisms are unknown. Here, we propose that fast ripples emerge from a disorganized ripple pattern caused by unreliable firing in the epileptic hippocampus. Enhanced synaptic activity is responsible for the irregular bursting of CA3 pyramidal cells due to large membrane potential fluctuations. Lower field interactions and a reduced spike-timing reliability concur with decreased spatial synchronization and the emergence of fast ripples. Reducing synaptically driven membrane potential fluctuations improves both spike-timing reliability and spatial synchronization and restores ripples in the epileptic hippocampus. Conversely, a lower spike-timing reliability, with reduced potassium currents, is associated with ripple shuffling in normal hippocampus. Therefore, fast ripples may reflect a pathological desynchronization of the normal ripple pattern.

Cognon Neural Model Software Verification and Hardware Implementation Design

NASA Astrophysics Data System (ADS)

Haro Negre, Pau

Little is known yet about how the brain can recognize arbitrary sensory patterns within milliseconds using neural spikes to communicate information between neurons. In a typical brain there are several layers of neurons, with each neuron axon connecting to ˜104 synapses of neurons in an adjacent layer. The information necessary for cognition is contained in theses synapses, which strengthen during the learning phase in response to newly presented spike patterns. Continuing on the model proposed in "Models for Neural Spike Computation and Cognition" by David H. Staelin and Carl H. Staelin, this study seeks to understand cognition from an information theoretic perspective and develop potential models for artificial implementation of cognition based on neuronal models. To do so we focus on the mathematical properties and limitations of spike-based cognition consistent with existing neurological observations. We validate the cognon model through software simulation and develop concepts for an optical hardware implementation of a network of artificial neural cognons.

Adjustment of Pesticide Concentrations for Temporal Changes in Analytical Recovery, 1992-2006

USGS Publications Warehouse

Martin, Jeffrey D.; Stone, Wesley W.; Wydoski, Duane S.; Sandstrom, Mark W.

2009-01-01

Recovery is the proportion of a target analyte that is quantified by an analytical method and is a primary indicator of the analytical bias of a measurement. Recovery is measured by analysis of quality-control (QC) water samples that have known amounts of target analytes added ('spiked' QC samples). For pesticides, recovery is the measured amount of pesticide in the spiked QC sample expressed as percentage of the amount spiked, ideally 100 percent. Temporal changes in recovery have the potential to adversely affect time-trend analysis of pesticide concentrations by introducing trends in environmental concentrations that are caused by trends in performance of the analytical method rather than by trends in pesticide use or other environmental conditions. This report examines temporal changes in the recovery of 44 pesticides and 8 pesticide degradates (hereafter referred to as 'pesticides') that were selected for a national analysis of time trends in pesticide concentrations in streams. Water samples were analyzed for these pesticides from 1992 to 2006 by gas chromatography/mass spectrometry. Recovery was measured by analysis of pesticide-spiked QC water samples. Temporal changes in pesticide recovery were investigated by calculating robust, locally weighted scatterplot smooths (lowess smooths) for the time series of pesticide recoveries in 5,132 laboratory reagent spikes; 1,234 stream-water matrix spikes; and 863 groundwater matrix spikes. A 10-percent smoothing window was selected to show broad, 6- to 12-month time scale changes in recovery for most of the 52 pesticides. Temporal patterns in recovery were similar (in phase) for laboratory reagent spikes and for matrix spikes for most pesticides. In-phase temporal changes among spike types support the hypothesis that temporal change in method performance is the primary cause of temporal change in recovery. Although temporal patterns of recovery were in phase for most pesticides, recovery in matrix spikes was greater than recovery in reagent spikes for nearly every pesticide. Models of recovery based on matrix spikes are deemed more appropriate for adjusting concentrations of pesticides measured in groundwater and stream-water samples than models based on laboratory reagent spikes because (1) matrix spikes are expected to more closely match the matrix of environmental water samples than are reagent spikes and (2) method performance is often matrix dependent, as was shown by higher recovery in matrix spikes for most of the pesticides. Models of recovery, based on lowess smooths of matrix spikes, were developed separately for groundwater and stream-water samples. The models of recovery can be used to adjust concentrations of pesticides measured in groundwater or stream-water samples to 100 percent recovery to compensate for temporal changes in the performance (bias) of the analytical method.

Detection of Bursts and Pauses in Spike Trains

PubMed Central

Ko, D.; Wilson, C. J.; Lobb, C. J.; Paladini, C. A.

2012-01-01

Midbrain dopaminergic neurons in vivo exhibit a wide range of firing patterns. They normally fire constantly at a low rate, and speed up, firing a phasic burst when reward exceeds prediction, or pause when an expected reward does not occur. Therefore, the detection of bursts and pauses from spike train data is a critical problem when studying the role of phasic dopamine (DA) in reward related learning, and other DA dependent behaviors. However, few statistical methods have been developed that can identify bursts and pauses simultaneously. We propose a new statistical method, the Robust Gaussian Surprise (RGS) method, which performs an exhaustive search of bursts and pauses in spike trains simultaneously. We found that the RGS method is adaptable to various patterns of spike trains recorded in vivo, and is not influenced by baseline firing rate, making it applicable to all in vivo spike trains where baseline firing rates vary over time. We compare the performance of the RGS method to other methods of detecting bursts, such as the Poisson Surprise (PS), Rank Surprise (RS), and Template methods. Analysis of data using the RGS method reveals potential mechanisms underlying how bursts and pauses are controlled in DA neurons. PMID:22939922

Analysis of the effects of periodic forcing in the spike rate and spike correlation's in semiconductor lasers with optical feedback

NASA Astrophysics Data System (ADS)

Quintero-Quiroz, C.; Sorrentino, Taciano; Torrent, M. C.; Masoller, Cristina

2016-04-01

We study the dynamics of semiconductor lasers with optical feedback and direct current modulation, operating in the regime of low frequency fluctuations (LFFs). In the LFF regime the laser intensity displays abrupt spikes: the intensity drops to zero and then gradually recovers. We focus on the inter-spike-intervals (ISIs) and use a method of symbolic time-series analysis, which is based on computing the probabilities of symbolic patterns. We show that the variation of the probabilities of the symbols with the modulation frequency and with the intrinsic spike rate of the laser allows to identify different regimes of noisy locking. Simulations of the Lang-Kobayashi model are in good qualitative agreement with experimental observations.

Neuromagnetic Evidence of Spatially Distributed Sources Underlying Epileptiform Spikes in the Human Brain

NASA Astrophysics Data System (ADS)

Barth, Daniel S.; Sutherling, William; Engle, Jerome; Beatty, Jackson

1984-01-01

Neuromagnetic measurements were performed on 17 subjects with focal seizure disorders. In all of the subjects, the interictal spike in the scalp electroencephalogram was associated with an orderly extracranial magnetic field pattern. In eight of these subjects, multiple current sources underlay the magnetic spike complex. The multiple sources within a given subject displayed a fixed chronological sequence of discharge, demonstrating a high degree of spatial and temporal organization within the interictal focus.

Cell assembly sequences arising from spike threshold adaptation keep track of time in the hippocampus

PubMed Central

Itskov, Vladimir; Curto, Carina; Pastalkova, Eva; Buzsáki, György

2011-01-01

Hippocampal neurons can display reliable and long-lasting sequences of transient firing patterns, even in the absence of changing external stimuli. We suggest that time-keeping is an important function of these sequences, and propose a network mechanism for their generation. We show that sequences of neuronal assemblies recorded from rat hippocampal CA1 pyramidal cells can reliably predict elapsed time (15-20 sec) during wheel running with a precision of 0.5sec. In addition, we demonstrate the generation of multiple reliable, long-lasting sequences in a recurrent network model. These sequences are generated in the presence of noisy, unstructured inputs to the network, mimicking stationary sensory input. Identical initial conditions generate similar sequences, whereas different initial conditions give rise to distinct sequences. The key ingredients responsible for sequence generation in the model are threshold-adaptation and a Mexican-hat-like pattern of connectivity among pyramidal cells. This pattern may arise from recurrent systems such as the hippocampal CA3 region or the entorhinal cortex. We hypothesize that mechanisms that evolved for spatial navigation also support tracking of elapsed time in behaviorally relevant contexts. PMID:21414904

Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity.

PubMed

Bichler, Olivier; Querlioz, Damien; Thorpe, Simon J; Bourgoin, Jean-Philippe; Gamrat, Christian

2012-08-01

A biologically inspired approach to learning temporally correlated patterns from a spiking silicon retina is presented. Spikes are generated from the retina in response to relative changes in illumination at the pixel level and transmitted to a feed-forward spiking neural network. Neurons become sensitive to patterns of pixels with correlated activation times, in a fully unsupervised scheme. This is achieved using a special form of Spike-Timing-Dependent Plasticity which depresses synapses that did not recently contribute to the post-synaptic spike activation, regardless of their activation time. Competitive learning is implemented with lateral inhibition. When tested with real-life data, the system is able to extract complex and overlapping temporally correlated features such as car trajectories on a freeway, after only 10 min of traffic learning. Complete trajectories can be learned with a 98% detection rate using a second layer, still with unsupervised learning, and the system may be used as a car counter. The proposed neural network is extremely robust to noise and it can tolerate a high degree of synaptic and neuronal variability with little impact on performance. Such results show that a simple biologically inspired unsupervised learning scheme is capable of generating selectivity to complex meaningful events on the basis of relatively little sensory experience. Copyright © 2012 Elsevier Ltd. All rights reserved.

POTENTIAL EMISSIONS OF HAZARDOUS ORGANIC COMPOUNDS FROM SEWAGE SLUDGE INCINERATION

EPA Science Inventory

Laboratory thermal decomposition studies were undertaken to evaluate potential organic emissions from sewage sludge incinerators. Precisely controlled thermal decomposition experiments were conducted on sludge spiked with mixtures of hazardous organic compounds, on the mixtures o...

Optimal firing rate estimation

NASA Technical Reports Server (NTRS)

Paulin, M. G.; Hoffman, L. F.

2001-01-01

We define a measure for evaluating the quality of a predictive model of the behavior of a spiking neuron. This measure, information gain per spike (Is), indicates how much more information is provided by the model than if the prediction were made by specifying the neuron's average firing rate over the same time period. We apply a maximum Is criterion to optimize the performance of Gaussian smoothing filters for estimating neural firing rates. With data from bullfrog vestibular semicircular canal neurons and data from simulated integrate-and-fire neurons, the optimal bandwidth for firing rate estimation is typically similar to the average firing rate. Precise timing and average rate models are limiting cases that perform poorly. We estimate that bullfrog semicircular canal sensory neurons transmit in the order of 1 bit of stimulus-related information per spike.

Controlling self-sustained spiking activity by adding or removing one network link

NASA Astrophysics Data System (ADS)

Xu, Kesheng; Huang, Wenwen; Li, Baowen; Dhamala, Mukesh; Liu, Zonghua

2013-06-01

Being able to control the neuronal spiking activity in specific brain regions is central to a treatment scheme in several brain disorders such as epileptic seizures, mental depression, and Parkinson's diseases. Here, we present an approach for controlling self-sustained oscillations by adding or removing one directed network link in coupled neuronal oscillators, in contrast to previous approaches of adding stimuli or noise. We find that such networks can exhibit a variety of activity patterns such as on-off switch, sustained spikes, and short-term spikes. We derive the condition for a specific link to be the controller of the on-off effect. A qualitative analysis is provided to facilitate the understanding of the mechanism for spiking activity by adding one link. Our findings represent the first report on generating spike activity with the addition of only one directed link to a network and provide a deeper understanding of the microscopic roots of self-sustained spiking.

The mechanisms of repetitive spike generation in an axonless retinal interneuron

PubMed Central

Cembrowski, Mark S.; Logan, Stephen M.; Tian, Miao; Jia, Li; Li, Wei; Kath, William L.; Riecke, Hermann; Singer, Joshua H.

2012-01-01

SUMMARY Several types of retinal interneurons exhibit spikes but lack axons. One such neuron is the AII amacrine cell, in which spikes recorded at the soma exhibit small amplitudes (<10 mV) and broad time courses (>5 ms). Here, we used electrophysiological recordings and computational analysis to examine the mechanisms underlying this atypical spiking. We found that somatic spikes likely represent large, brief action potential-like events initiated in a single, electrotonically-distal dendritic compartment. In this same compartment, spiking undergoes slow modulation, likely by an M-type K conductance. The structural correlate of this compartment is a thin neurite that extends from the primary dendritic tree: local application of TTX to this neurite, or excision of it, eliminates spiking. Thus, the physiology of the axonless AII is much more complex than would be anticipated from morphological descriptions and somatic recordings; in particular, the AII possesses a single dendritic structure that controls its firing pattern. PMID:22832164

Synaptic and Network Mechanisms of Sparse and Reliable Visual Cortical Activity during Nonclassical Receptive Field Stimulation

PubMed Central

Haider, Bilal; Krause, Matthew R.; Duque, Alvaro; Yu, Yuguo; Touryan, Jonathan; Mazer, James A.; McCormick, David A.

2011-01-01

SUMMARY During natural vision, the entire visual field is stimulated by images rich in spatiotemporal structure. Although many visual system studies restrict stimuli to the classical receptive field (CRF), it is known that costimulation of the CRF and the surrounding nonclassical receptive field (nCRF) increases neuronal response sparseness. The cellular and network mechanisms underlying increased response sparseness remain largely unexplored. Here we show that combined CRF + nCRF stimulation increases the sparseness, reliability, and precision of spiking and membrane potential responses in classical regular spiking (RSC) pyramidal neurons of cat primary visual cortex. Conversely, fast-spiking interneurons exhibit increased activity and decreased selectivity during CRF + nCRF stimulation. The increased sparseness and reliability of RSC neuron spiking is associated with increased inhibitory barrages and narrower visually evoked synaptic potentials. Our experimental observations were replicated with a simple computational model, suggesting that network interactions among neuronal subtypes ultimately sharpen recurrent excitation, producing specific and reliable visual responses. PMID:20152117

Optimal configurations of spatial scale for grid cell firing under noise and uncertainty

PubMed Central

Towse, Benjamin W.; Barry, Caswell; Bush, Daniel; Burgess, Neil

2014-01-01

We examined the accuracy with which the location of an agent moving within an environment could be decoded from the simulated firing of systems of grid cells. Grid cells were modelled with Poisson spiking dynamics and organized into multiple ‘modules’ of cells, with firing patterns of similar spatial scale within modules and a wide range of spatial scales across modules. The number of grid cells per module, the spatial scaling factor between modules and the size of the environment were varied. Errors in decoded location can take two forms: small errors of precision and larger errors resulting from ambiguity in decoding periodic firing patterns. With enough cells per module (e.g. eight modules of 100 cells each) grid systems are highly robust to ambiguity errors, even over ranges much larger than the largest grid scale (e.g. over a 500 m range when the maximum grid scale is 264 cm). Results did not depend strongly on the precise organization of scales across modules (geometric, co-prime or random). However, independent spatial noise across modules, which would occur if modules receive independent spatial inputs and might increase with spatial uncertainty, dramatically degrades the performance of the grid system. This effect of spatial uncertainty can be mitigated by uniform expansion of grid scales. Thus, in the realistic regimes simulated here, the optimal overall scale for a grid system represents a trade-off between minimizing spatial uncertainty (requiring large scales) and maximizing precision (requiring small scales). Within this view, the temporary expansion of grid scales observed in novel environments may be an optimal response to increased spatial uncertainty induced by the unfamiliarity of the available spatial cues. PMID:24366144

Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

NASA Astrophysics Data System (ADS)

Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

2016-12-01

Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

Femtosecond laser fabricated spike structures for selective control of cellular behavior.

PubMed

Schlie, Sabrina; Fadeeva, Elena; Koch, Jürgen; Ngezahayo, Anaclet; Chichkov, Boris N

2010-09-01

In this study we investigate the potential of femtosecond laser generated micrometer sized spike structures as functional surfaces for selective cell controlling. The spike dimensions as well as the average spike to spike distance can be easily tuned by varying the process parameters. Moreover, negative replications in soft materials such as silicone elastomer can be produced. This allows tailoring of wetting properties of the spike structures and their negative replicas representing a reduced surface contact area. Furthermore, we investigated material effects on cellular behavior. By comparing human fibroblasts and SH-SY5Y neuroblastoma cells we found that the influence of the material was cell specific. The cells not only changed their morphology, but also the cell growth was affected. Whereas, neuroblastoma cells proliferated at the same rate on the spike structures as on the control surfaces, the proliferation of fibroblasts was reduced by the spike structures. These effects can result from the cell specific adhesion patterns as shown in this work. These findings show a possibility to design defined surface microstructures, which could control cellular behavior in a cell specific manner.

CHRONIC DIETARY EXPOSURE WITH INTERMITTENT SPIKE DOSES OF CHLORPYRIFOS FAILS TO ALTER FLASH OR PATTERN REVERSAL EVOKED POTENTIALS IN RATS.

EPA Science Inventory

Human exposure to pesticides is often characterized by chronic low level exposure with intermittent spiked higher exposures. Visual disturbances are often reported following exposure to xenobiotics, and cholinesterase-inhibiting compounds have been reported to alter visual functi...

Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. I: Spike Generating Models on Converging Motifs

PubMed Central

Bi, Zedong; Zhou, Changsong

2016-01-01

In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to understand this influence under pair-wise additive spike-timing dependent plasticity (STDP) when the mean strength of plastic synapses into a neuron is bounded (synaptic homeostasis). Specifically, we systematically study, analytically and numerically, how four aspects of statistical features, i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations, as well as their interactions influence the efficacy variability in converging motifs (simple networks in which one neuron receives from many other neurons). Neurons (including the post-synaptic neuron) in a converging motif generate spikes according to statistical models with tunable parameters. In this way, we can explicitly control the statistics of the spike patterns, and investigate their influence onto the efficacy variability, without worrying about the feedback from synaptic changes onto the dynamics of the post-synaptic neuron. We separate efficacy variability into two parts: the drift part (DriftV) induced by the heterogeneity of change rates of different synapses, and the diffusion part (DiffV) induced by weight diffusion caused by stochasticity of spike trains. Our main findings are: (1) synchronous firing and burstiness tend to increase DiffV, (2) heterogeneity of rates induces DriftV when potentiation and depression in STDP are not balanced, and (3) heterogeneity of cross-correlations induces DriftV together with heterogeneity of rates. We anticipate our work important for understanding functional processes of neuronal networks (such as memory) and neural development. PMID:26941634

Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons

PubMed Central

Jeong, Jaeseung; Shi, Wei-Xing; Hoffman, Ralph; Oh, Jihoon; Gore, John C.; Bunney, Benjamin S.; Peterson, Bradley S.

2012-01-01

Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons. PMID:22831464

Integration of cortical and pallidal inputs in the basal ganglia-recipient thalamus of singing birds

PubMed Central

Goldberg, Jesse H.; Farries, Michael A.

2012-01-01

The basal ganglia-recipient thalamus receives inhibitory inputs from the pallidum and excitatory inputs from cortex, but it is unclear how these inputs interact during behavior. We recorded simultaneously from thalamic neurons and their putative synaptically connected pallidal inputs in singing zebra finches. We find, first, that each pallidal spike produces an extremely brief (∼5 ms) pulse of inhibition that completely suppresses thalamic spiking. As a result, thalamic spikes are entrained to pallidal spikes with submillisecond precision. Second, we find that the number of thalamic spikes that discharge within a single pallidal interspike interval (ISI) depends linearly on the duration of that interval but does not depend on pallidal activity prior to the interval. In a detailed biophysical model, our results were not easily explained by the postinhibitory “rebound” mechanism previously observed in anesthetized birds and in brain slices, nor could most of our data be characterized as “gating” of excitatory transmission by inhibitory pallidal input. Instead, we propose a novel “entrainment” mechanism of pallidothalamic transmission that highlights the importance of an excitatory conductance that drives spiking, interacting with brief pulses of pallidal inhibition. Building on our recent finding that cortical inputs can drive syllable-locked rate modulations in thalamic neurons during singing, we report here that excitatory inputs affect thalamic spiking in two ways: by shortening the latency of a thalamic spike after a pallidal spike and by increasing thalamic firing rates within individual pallidal ISIs. We present a unifying biophysical model that can reproduce all known modes of pallidothalamic transmission—rebound, gating, and entrainment—depending on the amount of excitation the thalamic neuron receives. PMID:22673333

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram

PubMed Central

Naud, Richard; Gerstner, Wulfram

2012-01-01

The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractoriness and adaptation. As a modeling framework, we use the spike response model, also known as the generalized linear neuron model. Because of refractoriness, the effect of the most recent spike on the spiking probability a few milliseconds later is very strong. The influence of the last spike needs therefore to be described with high precision, while the rest of the neuronal spiking history merely introduces an average self-inhibition or adaptation that depends on the expected number of past spikes but not on the exact spike timings. Based on these insights, we derive a ‘quasi-renewal equation’ which is shown to yield an excellent description of the firing rate of adapting neurons. We explore the domain of validity of the quasi-renewal equation and compare it with other rate equations for populations of spiking neurons. The problem of decoding the stimulus from the population response (or PSTH) is addressed analogously. We find that for small levels of activity and weak adaptation, a simple accumulator of the past activity is sufficient to decode the original input, but when refractory effects become large decoding becomes a non-linear function of the past activity. The results presented here can be applied to the mean-field analysis of coupled neuron networks, but also to arbitrary point processes with negative self-interaction. PMID:23055914

Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory; determination of low-level silver by graphite furnace atomic absorption spectrophotometry

USGS Publications Warehouse

Damrau, D.L.

1993-01-01

Increased awareness of the quality of water in the United States has led to the development of a method for determining low levels (0.2-5.0 microg/L) of silver in water samples. Use of graphite furnace atomic absorption spectrophotometry provides a sensitive, precise, and accurate method for determining low-level silver in samples of low ionic-strength water, precipitation water, and natural water. The minimum detection limit determined for low-level silver is 0.2 microg/L. Precision data were collected on natural-water samples and SRWS (Standard Reference Water Samples). The overall percent relative standard deviation for natural-water samples with silver concentrations more than 0.2 microg/L was less than 40 percent throughout the analytical range. For the SRWS with concentrations more than 0.2 microg/L, the overall percent relative standard deviation was less than 25 percent throughout the analytical range. The accuracy of the results was determined by spiking 6 natural-water samples with different known concentrations of the silver standard. The recoveries ranged from 61 to 119 percent at the 0.5-microg/L spike level. At the 1.25-microg/L spike level, the recoveries ranged from 92 to 106 percent. For the high spike level at 3.0 microg/L, the recoveries ranged from 65 to 113 percent. The measured concentrations of silver obtained from known samples were within the Branch of Quality Assurance accepted limits of 1 1/2 standard deviations on the basis of the SRWS program for Inter-Laboratory studies.

Entorhinal stellate cells show preferred spike phase-locking to theta inputs that is enhanced by correlations in synaptic activity

PubMed Central

Fernandez, Fernando R.; Malerba, Paola; Bressloff, Paul C.; White, John A.

2013-01-01

In active networks, excitatory and inhibitory synaptic inputs generate membrane voltage fluctuations that drive spike activity in a probabilistic manner. Despite this, some cells in vivo show a strong propensity to precisely lock to the local field potential and maintain a specific spike-phase relationship relative to other cells. In recordings from rat medial entorhinal cortical stellate cells, we measured spike phase-locking in response to sinusoidal “test” inputs in the presence of different forms of background membrane voltage fluctuations, generated via dynamic clamp. We find that stellate cells show strong and robust spike phase-locking to theta (4–12 Hz) inputs. This response occurs under a wide variety of background membrane voltage fluctuation conditions that include a substantial increase in overall membrane conductance. Furthermore, the IH current present in stellate cells is critical to the enhanced spike phase-locking response at theta. Finally, we show that correlations between inhibitory and excitatory conductance fluctuations, which can arise through feed-back and feed-forward inhibition, can substantially enhance the spike phase-locking response. The enhancement in locking is a result of a selective reduction in the size of low frequency membrane voltage fluctuations due to cancelation of inhibitory and excitatory current fluctuations with correlations. Hence, our results demonstrate that stellate cells have a strong preference for spike phase-locking to theta band inputs and that the absolute magnitude of locking to theta can be modulated by the properties of background membrane voltage fluctuations. PMID:23554484

On the robustness of EC-PC spike detection method for online neural recording.

PubMed

Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

2014-09-30

Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Exact distinction of excitatory and inhibitory neurons in neural networks: a study with GFP-GAD67 neurons optically and electrophysiologically recognized on multielectrode arrays

PubMed Central

Becchetti, Andrea; Gullo, Francesca; Bruno, Giuseppe; Dossi, Elena; Lecchi, Marzia; Wanke, Enzo

2012-01-01

Distinguishing excitatory from inhibitory neurons with multielectrode array (MEA) recordings is a serious experimental challenge. The current methods, developed in vitro, mostly rely on spike waveform analysis. These however often display poor resolution and may produce errors caused by the variability of spike amplitudes and neuron shapes. Recent recordings in human brain suggest that the spike waveform features correlate with time-domain statistics such as spiking rate, autocorrelation, and coefficient of variation. However, no precise criteria are available to exactly assign identified units to specific neuronal types, either in vivo or in vitro. To solve this problem, we combined MEA recording with fluorescence imaging of neocortical cultures from mice expressing green fluorescent protein (GFP) in GABAergic cells. In this way, we could sort out “authentic excitatory neurons” (AENs) and “authentic inhibitory neurons” (AINs). We thus characterized 1275 units (from 405 electrodes, n = 10 experiments), based on autocorrelation, burst length, spike number (SN), spiking rate, squared coefficient of variation, and Fano factor (FF) (the ratio between spike-count variance and mean). These metrics differed by about one order of magnitude between AINs and AENs. In particular, the FF turned out to provide a firing code which exactly (no overlap) recognizes excitatory and inhibitory units. The difference in FF between all of the identified AEN and AIN groups was highly significant (p < 10−8, ANOVA post-hoc Tukey test). Our results indicate a statistical metric-based approach to distinguish excitatory from inhibitory neurons independently from the spike width. PMID:22973197

Dual Roles for Spike Signaling in Cortical Neural Populations

PubMed Central

Ballard, Dana H.; Jehee, Janneke F. M.

2011-01-01

A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms. In addition, it makes testable predictions that follow from the γ latency coding. PMID:21687798

Locally induced neuronal synchrony precisely propagates to specific cortical areas without rhythm distortion.

PubMed

Toda, Haruo; Kawasaki, Keisuke; Sato, Sho; Horie, Masao; Nakahara, Kiyoshi; Bepari, Asim K; Sawahata, Hirohito; Suzuki, Takafumi; Okado, Haruo; Takebayashi, Hirohide; Hasegawa, Isao

2018-05-16

Propagation of oscillatory spike firing activity at specific frequencies plays an important role in distributed cortical networks. However, there is limited evidence for how such frequency-specific signals are induced or how the signal spectra of the propagating signals are modulated during across-layer (radial) and inter-areal (tangential) neuronal interactions. To directly evaluate the direction specificity of spectral changes in a spiking cortical network, we selectively photostimulated infragranular excitatory neurons in the rat primary visual cortex (V1) at a supra-threshold level with various frequencies, and recorded local field potentials (LFPs) at the infragranular stimulation site, the cortical surface site immediately above the stimulation site in V1, and cortical surface sites outside V1. We found a significant reduction of LFP powers during radial propagation, especially at high-frequency stimulation conditions. Moreover, low-gamma-band dominant rhythms were transiently induced during radial propagation. Contrastingly, inter-areal LFP propagation, directed to specific cortical sites, accompanied no significant signal reduction nor gamma-band power induction. We propose an anisotropic mechanism for signal processing in the spiking cortical network, in which the neuronal rhythms are locally induced/modulated along the radial direction, and then propagate without distortion via intrinsic horizontal connections for spatiotemporally precise, inter-areal communication.

Purkinje cells signal hand shape and grasp force during reach-to-grasp in the monkey.

PubMed

Mason, Carolyn R; Hendrix, Claudia M; Ebner, Timothy J

2006-01-01

The cerebellar cortex and nuclei play important roles in the learning, planning, and execution of reach-to-grasp and prehensile movements. However, few studies have investigated the signals carried by cerebellar neurons during reach-to-grasp, particularly signals relating to target object properties, hand shape, and grasp force. In this study, the simple spike discharge of 77 Purkinje cells was recorded as two rhesus monkeys reached and grasped 16 objects. The objects varied systematically in volume, shape, and orientation and each was grasped at five different force levels. Linear multiple regression analyses showed the simple spike discharge was significantly modulated in relation to objects and force levels. Object related modulation occurred preferentially during reach or early in the grasp and was linearly related to grasp aperture. The simple spike discharge was positively correlated with grasp force during both the reach and the grasp. There was no significant interaction between object and grasp force modulation, supporting previous kinematic findings that grasp kinematics and force are signaled independently. Singular value decomposition (SVD) was used to quantify the temporal patterns in the simple spike discharge. Most cells had a predominant discharge pattern that remained relatively constant across object grasp dimensions and force levels. A single predominant simple spike discharge pattern that spans reach and grasp and accounts for most of the variation (>60%) is consistent with the concept that the cerebellum is involved with synergies underlying prehension. Therefore Purkinje cells are involved with the signaling of prehension, providing independent signals for hand shaping and grasp force.

Neural Representation of Spatial Topology in the Rodent Hippocampus

PubMed Central

Chen, Zhe; Gomperts, Stephen N.; Yamamoto, Jun; Wilson, Matthew A.

2014-01-01

Pyramidal cells in the rodent hippocampus often exhibit clear spatial tuning in navigation. Although it has been long suggested that pyramidal cell activity may underlie a topological code rather than a topographic code, it remains unclear whether an abstract spatial topology can be encoded in the ensemble spiking activity of hippocampal place cells. Using a statistical approach developed previously, we investigate this question and related issues in greater details. We recorded ensembles of hippocampal neurons as rodents freely foraged in one and two-dimensional spatial environments, and we used a “decode-to-uncover” strategy to examine the temporally structured patterns embedded in the ensemble spiking activity in the absence of observed spatial correlates during periods of rodent navigation or awake immobility. Specifically, the spatial environment was represented by a finite discrete state space. Trajectories across spatial locations (“states”) were associated with consistent hippocampal ensemble spiking patterns, which were characterized by a state transition matrix. From this state transition matrix, we inferred a topology graph that defined the connectivity in the state space. In both one and two-dimensional environments, the extracted behavior patterns from the rodent hippocampal population codes were compared against randomly shuffled spike data. In contrast to a topographic code, our results support the efficiency of topological coding in the presence of sparse sample size and fuzzy space mapping. This computational approach allows us to quantify the variability of ensemble spiking activity, to examine hippocampal population codes during off-line states, and to quantify the topological complexity of the environment. PMID:24102128

Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits

PubMed Central

Hiratani, Naoki; Fukai, Tomoki

2015-01-01

The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP) is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory. PMID:25910189

Training Deep Spiking Neural Networks Using Backpropagation.

PubMed

Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

2016-01-01

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

Exact subthreshold integration with continuous spike times in discrete-time neural network simulations.

PubMed

Morrison, Abigail; Straube, Sirko; Plesser, Hans Ekkehard; Diesmann, Markus

2007-01-01

Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.

Evolving spiking neural networks: a novel growth algorithm exhibits unintelligent design

NASA Astrophysics Data System (ADS)

Schaffer, J. David

2015-06-01

Spiking neural networks (SNNs) have drawn considerable excitement because of their computational properties, believed to be superior to conventional von Neumann machines, and sharing properties with living brains. Yet progress building these systems has been limited because we lack a design methodology. We present a gene-driven network growth algorithm that enables a genetic algorithm (evolutionary computation) to generate and test SNNs. The genome for this algorithm grows O(n) where n is the number of neurons; n is also evolved. The genome not only specifies the network topology, but all its parameters as well. Experiments show the algorithm producing SNNs that effectively produce a robust spike bursting behavior given tonic inputs, an application suitable for central pattern generators. Even though evolution did not include perturbations of the input spike trains, the evolved networks showed remarkable robustness to such perturbations. In addition, the output spike patterns retain evidence of the specific perturbation of the inputs, a feature that could be exploited by network additions that could use this information for refined decision making if required. On a second task, a sequence detector, a discriminating design was found that might be considered an example of "unintelligent design"; extra non-functional neurons were included that, while inefficient, did not hamper its proper functioning.

The Characteristics of Binary Spike-Time-Dependent Plasticity in HfO2-Based RRAM and Applications for Pattern Recognition

NASA Astrophysics Data System (ADS)

Zhou, Zheng; Liu, Chen; Shen, Wensheng; Dong, Zhen; Chen, Zhe; Huang, Peng; Liu, Lifeng; Liu, Xiaoyan; Kang, Jinfeng

2017-04-01

A binary spike-time-dependent plasticity (STDP) protocol based on one resistive-switching random access memory (RRAM) device was proposed and experimentally demonstrated in the fabricated RRAM array. Based on the STDP protocol, a novel unsupervised online pattern recognition system including RRAM synapses and CMOS neurons is developed. Our simulations show that the system can efficiently compete the handwritten digits recognition task, which indicates the feasibility of using the RRAM-based binary STDP protocol in neuromorphic computing systems to obtain good performance.

Training Spiking Neural Models Using Artificial Bee Colony

PubMed Central

Vazquez, Roberto A.; Garro, Beatriz A.

2015-01-01

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

Calcium spikes, waves and oscillations in a large, patterned epithelial tissue

PubMed Central

Balaji, Ramya; Bielmeier, Christina; Harz, Hartmann; Bates, Jack; Stadler, Cornelia; Hildebrand, Alexander; Classen, Anne-Kathrin

2017-01-01

While calcium signaling in excitable cells, such as muscle or neurons, is extensively characterized, calcium signaling in epithelial tissues is little understood. Specifically, the range of intercellular calcium signaling patterns elicited by tightly coupled epithelial cells and their function in the regulation of epithelial characteristics are little explored. We found that in Drosophila imaginal discs, a widely studied epithelial model organ, complex spatiotemporal calcium dynamics occur. We describe patterns that include intercellular waves traversing large tissue domains in striking oscillatory patterns as well as spikes confined to local domains of neighboring cells. The spatiotemporal characteristics of intercellular waves and oscillations arise as emergent properties of calcium mobilization within a sheet of gap-junction coupled cells and are influenced by cell size and environmental history. While the in vivo function of spikes, waves and oscillations requires further characterization, our genetic experiments suggest that core calcium signaling components guide actomyosin organization. Our study thus suggests a possible role for calcium signaling in epithelia but importantly, introduces a model epithelium enabling the dissection of cellular mechanisms supporting the initiation, transmission and regeneration of long-range intercellular calcium waves and the emergence of oscillations in a highly coupled multicellular sheet. PMID:28218282

The Global Spike: Conserved Dendritic Properties Enable Unique Ca2+ Spike Generation in Low-Threshold Spiking Neurons.

PubMed

Connelly, William M; Crunelli, Vincenzo; Errington, Adam C

2015-11-25

Low-threshold Ca(2+) spikes (LTS) are an indispensible signaling mechanism for neurons in areas including the cortex, cerebellum, basal ganglia, and thalamus. They have critical physiological roles and have been strongly associated with disorders including epilepsy, Parkinson's disease, and schizophrenia. However, although dendritic T-type Ca(2+) channels have been implicated in LTS generation, because the properties of low-threshold spiking neuron dendrites are unknown, the precise mechanism has remained elusive. Here, combining data from fluorescence-targeted dendritic recordings and Ca(2+) imaging from low-threshold spiking cells in rat brain slices with computational modeling, the cellular mechanism responsible for LTS generation is established. Our data demonstrate that key somatodendritic electrical conduction properties are highly conserved between glutamatergic thalamocortical neurons and GABAergic thalamic reticular nucleus neurons and that these properties are critical for LTS generation. In particular, the efficiency of soma to dendrite voltage transfer is highly asymmetric in low-threshold spiking cells, and in the somatofugal direction, these neurons are particularly electrotonically compact. Our data demonstrate that LTS have remarkably similar amplitudes and occur synchronously throughout the dendritic tree. In fact, these Ca(2+) spikes cannot occur locally in any part of the cell, and hence we reveal that LTS are generated by a unique whole-cell mechanism that means they always occur as spatially global spikes. This all-or-none, global electrical and biochemical signaling mechanism clearly distinguishes LTS from other signals, including backpropagating action potentials and dendritic Ca(2+)/NMDA spikes, and has important consequences for dendritic function in low-threshold spiking neurons. Low-threshold Ca(2+) spikes (LTS) are critical for important physiological processes, including generation of sleep-related oscillations, and are implicated in disorders including epilepsy, Parkinson's disease, and schizophrenia. However, the mechanism underlying LTS generation in neurons, which is thought to involve dendritic T-type Ca(2+) channels, has remained elusive due to a lack of knowledge of the dendritic properties of low-threshold spiking cells. Combining dendritic recordings, two-photon Ca(2+) imaging, and computational modeling, this study reveals that dendritic properties are highly conserved between two prominent low-threshold spiking neurons and that these properties underpin a whole-cell somatodendritic spike generation mechanism that makes the LTS a unique global electrical and biochemical signal in neurons. Copyright © 2015 Connelly et al.

Solid matrix transformation and tracer addition using molten ammonium bifluoride salt as a sample preparation method for laser ablation inductively coupled plasma mass spectrometry.

PubMed

Grate, Jay W; Gonzalez, Jhanis J; O'Hara, Matthew J; Kellogg, Cynthia M; Morrison, Samuel S; Koppenaal, David W; Chan, George C-Y; Mao, Xianglei; Zorba, Vassilia; Russo, Richard E

2017-09-08

Solid sampling and analysis methods, such as laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), are challenged by matrix effects and calibration difficulties. Matrix-matched standards for external calibration are seldom available and it is difficult to distribute spikes evenly into a solid matrix as internal standards. While isotopic ratios of the same element can be measured to high precision, matrix-dependent effects in the sampling and analysis process frustrate accurate quantification and elemental ratio determinations. Here we introduce a potentially general solid matrix transformation approach entailing chemical reactions in molten ammonium bifluoride (ABF) salt that enables the introduction of spikes as tracers or internal standards. Proof of principle experiments show that the decomposition of uranium ore in sealed PFA fluoropolymer vials at 230 °C yields, after cooling, new solids suitable for direct solid sampling by LA. When spikes are included in the molten salt reaction, subsequent LA-ICP-MS sampling at several spots indicate that the spikes are evenly distributed, and that U-235 tracer dramatically improves reproducibility in U-238 analysis. Precisions improved from 17% relative standard deviation for U-238 signals to 0.1% for the ratio of sample U-238 to spiked U-235, a factor of over two orders of magnitude. These results introduce the concept of solid matrix transformation (SMT) using ABF, and provide proof of principle for a new method of incorporating internal standards into a solid for LA-ICP-MS. This new approach, SMT-LA-ICP-MS, provides opportunities to improve calibration and quantification in solids based analysis. Looking forward, tracer addition to transformed solids opens up LA-based methods to analytical methodologies such as standard addition, isotope dilution, preparation of matrix-matched solid standards, external calibration, and monitoring instrument drift against external calibration standards.

Spreading Photoparoxysmal EEG Response is Associated with an Abnormal Cortical Excitability Pattern

ERIC Educational Resources Information Center

Siniatchkin, Michael; Groppa, Sergey; Jerosch, Bettina; Muhle, Hiltrud; Kurth, Christoph; Shepherd, Alex J.; Siebner, Hartwig; Stephani, Ulrich

2007-01-01

Photosensitivity or photoparoxysmal response (PPR) is a highly heritable electroencephalographic trait characterized by an abnormal cortical response to intermittent photic stimulation (IPS). In PPR-positive individuals, IPS induces spikes, spike-waves or intermittent slow waves. The PPR may be restricted to posterior visual areas (i.e. local PPR…

Cortical pyramidal cells as non-linear oscillators: experiment and spike-generation theory.

PubMed

Brumberg, Joshua C; Gutkin, Boris S

2007-09-26

Cortical neurons are capable of generating trains of action potentials in response to current injections. These discharges can take different forms, e.g., repetitive firing that adapts during the period of current injection or bursting behaviors. We have used a combined experimental and computational approach to characterize the dynamics leading to action potential responses in single neurons. Specifically we investigated the origin of complex firing patterns in response to sinusoidal current injections. Using a reduced model, the theta-neuron, alongside recordings from cortical pyramidal cells we show that both real and simulated neurons show phase-locking to sine wave stimuli up to a critical frequency, above which period skipping and 1-to-x phase-locking occurs. The locking behavior follows a complex "devil's staircase" phenomena, where locked modes are interleaved with irregular firing. We further show that the critical frequency depends on the time scale of spike generation and on the level of spike frequency adaptation. These results suggest that phase-locking of neuronal responses to complex input patterns can be explained by basic properties of the spike-generating machinery.

A wireless neural recording system with a precision motorized microdrive for freely behaving animals

PubMed Central

Hasegawa, Taku; Fujimoto, Hisataka; Tashiro, Koichiro; Nonomura, Mayu; Tsuchiya, Akira; Watanabe, Dai

2015-01-01

The brain is composed of many different types of neurons. Therefore, analysis of brain activity with single-cell resolution could provide fundamental insights into brain mechanisms. However, the electrical signal of an individual neuron is very small, and precise isolation of single neuronal activity from moving subjects is still challenging. To measure single-unit signals in actively behaving states, establishment of technologies that enable fine control of electrode positioning and strict spike sorting is essential. To further apply such a single-cell recording approach to small brain areas in naturally behaving animals in large spaces or during social interaction, we developed a compact wireless recording system with a motorized microdrive. Wireless control of electrode placement facilitates the exploration of single neuronal activity without affecting animal behaviors. Because the system is equipped with a newly developed data-encoding program, the recorded data are readily compressed almost to theoretical limits and securely transmitted to a host computer. Brain activity can thereby be stably monitored in real time and further analyzed using online or offline spike sorting. Our wireless recording approach using a precision motorized microdrive will become a powerful tool for studying brain mechanisms underlying natural or social behaviors. PMID:25597933

Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons.

PubMed

Jeong, Jaeseung; Shi, Wei-Xing; Hoffman, Ralph; Oh, Jihoon; Gore, John C; Bunney, Benjamin S; Peterson, Bradley S

2012-11-01

Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

A generative spike train model with time-structured higher order correlations.

PubMed

Trousdale, James; Hu, Yu; Shea-Brown, Eric; Josić, Krešimir

2013-01-01

Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem. Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS) model creates marginally Poisson spike trains with diverse temporal correlation structures. We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs. We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics.

[Evaluation of the quality of three-dimensional data acquired by using two kinds of structure light intra-oral scanner to scan the crown preparation model].

PubMed

Zhang, X Y; Li, H; Zhao, Y J; Wang, Y; Sun, Y C

2016-07-01

To quantitatively evaluate the quality and accuracy of three-dimensional (3D) data acquired by using two kinds of structure intra-oral scanner to scan the typical teeth crown preparations. Eight typical teeth crown preparations model were scanned 3 times with two kinds of structured light intra-oral scanner(A, B), as test group. A high precision model scanner were used to scan the model as true value group. The data above the cervical margin was extracted. The indexes of quality including non-manifold edges, the self-intersections, highly-creased edges, spikes, small components, small tunnels, small holes and the anount of triangles were measured with the tool of mesh doctor in Geomagic studio 2012. The scanned data of test group were aligned to the data of true value group. 3D deviations of the test group compared with true value group were measured for each scanned point, each preparation and each group. Independent-samples Mann-Whitney U test was applied to analyze 3D deviations for each scanned point of A and B group. Correlation analysis was applied to index values and 3D deviation values. The total number of spikes in A group was 96, and that in B group and true value group were 5 and 0 respectively. Trueness: A group 8.0 (8.3) μm, B group 9.5 (11.5) μm(P>0.05). Correlation analysis of the number of spikes with data precision of A group was r=0.46. In the study, the qulity of the scanner B is better than scanner A, the difference of accuracy is not statistically significant. There is correlation between quality and data precision of the data scanned with scanner A.

Biophysical mechanism of spike threshold dependence on the rate of rise of the membrane potential by sodium channel inactivation or subthreshold axonal potassium current

PubMed Central

Wester, Jason C.

2013-01-01

Spike threshold filters incoming inputs and thus gates activity flow through neuronal networks. Threshold is variable, and in many types of neurons there is a relationship between the threshold voltage and the rate of rise of the membrane potential (dVm/dt) leading to the spike. In primary sensory cortex this relationship enhances the sensitivity of neurons to a particular stimulus feature. While Na+ channel inactivation may contribute to this relationship, recent evidence indicates that K+ currents located in the spike initiation zone are crucial. Here we used a simple Hodgkin-Huxley biophysical model to systematically investigate the role of K+ and Na+ current parameters (activation voltages and kinetics) in regulating spike threshold as a function of dVm/dt. Threshold was determined empirically and not estimated from the shape of the Vm prior to a spike. This allowed us to investigate intrinsic currents and values of gating variables at the precise voltage threshold. We found that Na+ inactivation is sufficient to produce the relationship provided it occurs at hyperpolarized voltages combined with slow kinetics. Alternatively, hyperpolarization of the K+ current activation voltage, even in the absence of Na+ inactivation, is also sufficient to produce the relationship. This hyperpolarized shift of K+ activation allows an outward current prior to spike initiation to antagonize the Na+ inward current such that it becomes self-sustaining at a more depolarized voltage. Our simulations demonstrate parameter constraints on Na+ inactivation and the biophysical mechanism by which an outward current regulates spike threshold as a function of dVm/dt. PMID:23344915

Swarm- Validation of Star Tracker and Accelerometer Data

NASA Astrophysics Data System (ADS)

Schack, Peter; Schlicht, Anja; Pail, Roland; Gruber, Thomas

2016-08-01

The ESA Swarm mission is designed to advance studies in the field of magnetosphere, thermosphere and gravity field. To be fortunate on this task precise knowledge of the orientation of the Swarm satellites is required together with knowledge about external forces acting on the satellites. The key sensors providing this information are the star trackers and the accelerometers. Based on star tracker studies conducted by the Denmark Technical University (DTU), we found interesting patterns in the interboresight angles on all three satellites, which are partly induced by temperature alterations. Additionally, structures of horizontal stripes seem to be caused by the unique distribution of observed stars on the charge-coupled device of the star trackers. Our accelerometer analyses focus on spikes and pulses in the observations. Those short term events on Swarm might originate from electrical processes introduced by sunlight illuminating the nadir foil. Comparisons to GOCE and GRACE are included.

Visual processing in the central bee brain.

PubMed

Paulk, Angelique C; Dacks, Andrew M; Phillips-Portillo, James; Fellous, Jean-Marc; Gronenberg, Wulfila

2009-08-12

Visual scenes comprise enormous amounts of information from which nervous systems extract behaviorally relevant cues. In most model systems, little is known about the transformation of visual information as it occurs along visual pathways. We examined how visual information is transformed physiologically as it is communicated from the eye to higher-order brain centers using bumblebees, which are known for their visual capabilities. We recorded intracellularly in vivo from 30 neurons in the central bumblebee brain (the lateral protocerebrum) and compared these neurons to 132 neurons from more distal areas along the visual pathway, namely the medulla and the lobula. In these three brain regions (medulla, lobula, and central brain), we examined correlations between the neurons' branching patterns and their responses primarily to color, but also to motion stimuli. Visual neurons projecting to the anterior central brain were generally color sensitive, while neurons projecting to the posterior central brain were predominantly motion sensitive. The temporal response properties differed significantly between these areas, with an increase in spike time precision across trials and a decrease in average reliable spiking as visual information processing progressed from the periphery to the central brain. These data suggest that neurons along the visual pathway to the central brain not only are segregated with regard to the physical features of the stimuli (e.g., color and motion), but also differ in the way they encode stimuli, possibly to allow for efficient parallel processing to occur.

The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.

PubMed

Kasi, Patrick; Wright, James; Khamis, Heba; Birznieks, Ingvars; van Schaik, André

2016-01-01

It is well known that signals encoded by mechanoreceptors facilitate precise object manipulation in humans. It is therefore of interest to study signals encoded by the mechanoreceptors because this will contribute further towards the understanding of fundamental sensory mechanisms that are responsible for coordinating force components during object manipulation. From a practical point of view, this may suggest strategies for designing sensory-controlled biomedical devices and robotic manipulators. We use a two-stage nonlinear decoding paradigm to reconstruct the force stimulus given signals from slowly adapting type one (SA-I) tactile afferents. First, we describe a nonhomogeneous Poisson encoding model which is a function of the force stimulus and the force's rate of change. In the decoding phase, we use a recursive nonlinear Bayesian filter to reconstruct the force profile, given the SA-I spike patterns and parameters described by the encoding model. Under the current encoding model, the mode ratio of force to its derivative is: 1.26 to 1.02. This indicates that the force derivative contributes significantly to the rate of change to the SA-I afferent spike modulation. Furthermore, using recursive Bayesian decoding algorithms is advantageous because it can incorporate past and current information in order to make predictions--consistent with neural systems--with little computational resources. This makes it suitable for interfacing with prostheses.

The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans

PubMed Central

Wright, James; Khamis, Heba; Birznieks, Ingvars; van Schaik, André

2016-01-01

It is well known that signals encoded by mechanoreceptors facilitate precise object manipulation in humans. It is therefore of interest to study signals encoded by the mechanoreceptors because this will contribute further towards the understanding of fundamental sensory mechanisms that are responsible for coordinating force components during object manipulation. From a practical point of view, this may suggest strategies for designing sensory-controlled biomedical devices and robotic manipulators. We use a two-stage nonlinear decoding paradigm to reconstruct the force stimulus given signals from slowly adapting type one (SA-I) tactile afferents. First, we describe a nonhomogeneous Poisson encoding model which is a function of the force stimulus and the force’s rate of change. In the decoding phase, we use a recursive nonlinear Bayesian filter to reconstruct the force profile, given the SA-I spike patterns and parameters described by the encoding model. Under the current encoding model, the mode ratio of force to its derivative is: 1.26 to 1.02. This indicates that the force derivative contributes significantly to the rate of change to the SA-I afferent spike modulation. Furthermore, using recursive Bayesian decoding algorithms is advantageous because it can incorporate past and current information in order to make predictions—consistent with neural systems—with little computational resources. This makes it suitable for interfacing with prostheses. PMID:27077750

A fast BK-type KCa current acts as a postsynaptic modulator of temporal selectivity for communication signals.

PubMed

Kohashi, Tsunehiko; Carlson, Bruce A

2014-01-01

Temporal patterns of spiking often convey behaviorally relevant information. Various synaptic mechanisms and intrinsic membrane properties can influence neuronal selectivity to temporal patterns of input. However, little is known about how synaptic mechanisms and intrinsic properties together determine the temporal selectivity of neuronal output. We tackled this question by recording from midbrain electrosensory neurons in mormyrid fish, in which the processing of temporal intervals between communication signals can be studied in a reduced in vitro preparation. Mormyrids communicate by varying interpulse intervals (IPIs) between electric pulses. Within the midbrain posterior exterolateral nucleus (ELp), the temporal patterns of afferent spike trains are filtered to establish single-neuron IPI tuning. We performed whole-cell recording from ELp neurons in a whole-brain preparation and examined the relationship between intrinsic excitability and IPI tuning. We found that spike frequency adaptation of ELp neurons was highly variable. Postsynaptic potentials (PSPs) of strongly adapting (phasic) neurons were more sharply tuned to IPIs than weakly adapting (tonic) neurons. Further, the synaptic filtering of IPIs by tonic neurons was more faithfully converted into variation in spiking output, particularly at short IPIs. Pharmacological manipulation under current- and voltage-clamp revealed that tonic firing is mediated by a fast, large-conductance Ca(2+)-activated K(+) (KCa) current (BK) that speeds up action potential repolarization. These results suggest that BK currents can shape the temporal filtering of sensory inputs by modifying both synaptic responses and PSP-to-spike conversion. Slow SK-type KCa currents have previously been implicated in temporal processing. Thus, both fast and slow KCa currents can fine-tune temporal selectivity.

Transcriptional expression analysis of genes involved in regulation of calcium translocation and storage in finger millet (Eleusine coracana L. Gartn.).

PubMed

Mirza, Neelofar; Taj, Gohar; Arora, Sandeep; Kumar, Anil

2014-10-25

Finger millet (Eleusine coracana) variably accumulates calcium in different tissues, due to differential expression of genes involved in uptake, translocation and accumulation of calcium. Ca(2+)/H(+) antiporter (CAX1), two pore channel (TPC1), CaM-stimulated type IIB Ca(2+) ATPase and two CaM dependent protein kinase (CaMK1 and 2) homologs were studied in finger millet. Two genotypes GP-45 and GP-1 (high and low calcium accumulating, respectively) were used to understand the role of these genes in differential calcium accumulation. For most of the genes higher expression was found in the high calcium accumulating genotype. CAX1 was strongly expressed in the late stages of spike development and could be responsible for accumulating high concentrations of calcium in seeds. TPC1 and Ca(2+) ATPase homologs recorded strong expression in the root, stem and developing spike and signify their role in calcium uptake and translocation, respectively. Calmodulin showed strong expression and a similar expression pattern to the type IIB ATPase in the developing spike only and indicating developing spike or even seed specific isoform of CaM affecting the activity of downstream target of calcium transportation. Interestingly, CaMK1 and CaMK2 had expression patterns similar to ATPase and TPC1 in various tissues raising a possibility of their respective regulation via CaM kinase. Expression pattern of 14-3-3 gene was observed to be similar to CAX1 gene in leaf and developing spike inferring a surprising possibility of CAX1 regulation through 14-3-3 protein. Our results provide a molecular insight for explaining the mechanism of calcium accumulation in finger millet. Copyright © 2014 Elsevier B.V. All rights reserved.

Results of quality-control sampling of water, bed sediment, and tissue in the Western Lake Michigan Drainages study unit of the National Water-Quality Assessment Program

USGS Publications Warehouse

Fitzgerald, S.A.

1997-01-01

This report contains the quality control results of the Western Lake Michigan Drainages study unit of the National Water Quality Assessment Program. Quality control samples were collected in the same manner and contemporaneously with environmental samples during the first highintensity study phase in the unit (1992 through 1995) and amounted to approximately 15 percent of all samples collected. The accuracy and precision of hundreds of chemical analyses of surface and ground-water, bed sediment, and tissue was determined through the collection and analysis of field blanks, field replicates and splits, matrix spikes, and surrogates. Despite the several detections of analytes in the field blanks, the concentrations of most constituents in the environmental samples will likely be an order of magnitude or higher than those in the blanks. However, frequent detections, and high concentrations, of dissolved organic carbon (DOC) in several surface and ground-water blanks are probably significant with respect to commonly measured environmental concentrations, and the environmental data will have to be qualified accordingly. The precision of sampling of water on a percent basis, as determined from replicates and splits, was generally proportional to the concentration of the constituents, with constituents present in relatively high concentrations generally having less sampling variability than those with relatively low concentrations. In general, analytes with relatively high variability between replicates were present at concentrations near the reporting limit or were associated with relatively small absolute concentration differences, or both. Precision of replicates compared to that for splits in bed sediment samples was similar, thus eliminating sampling as a major source of variability in analyte concentrations. In the case the phthalates in bed sediment, contamination in either the field or laboratory could have caused the relatively large variability between replicate samples and between split samples.Variability of analyte concentrations in tissue samples was relatively low, being 29 percent or less for all constituents. Recoveries of most laboratory schedule 2001/2010 pesticide spike compounds in surfacewater samples were reasonably good. Low intrinsic method recovery resulted in relatively low recovery forp,p'-DDE, metribuzin, and propargite. In the case of propargite, decomposition with the environmental sample matrices was also indicated. Recoveries of two compounds, cyanazine and thiobencarb, might have been biased high due to interferences. The one laboratory schedule 2050/2051 field matrix pesticide spike indicated numerous operational problems with this method that biased recoveries either low or high. Recoveries of pesticides from both pesticide schedules in field spikes of ground-water samples generally were similar to those of field matrix spikes of surface- water samples. High maximum recoveries were noted for tebuthiuron, disulfoton, DCPA, and permethrin, which indicates the possible presence of interferents in the matrices for these compounds. Problems in the recoveries of pesticides on schedule 2050/2051 from ground-water samples generally were the same as those for surfacewater samples. Recoveries of VOCs in field matrix spikes were reasonable when consideration was given for the use of the micropipettor that delivered only about 80 percent on average of the nominal mass of spiked analytes. Finally, the recoveries of most surrogate compounds in surface and ground-water samples were reasonable. Problems in sample handling (for example, spillage) were likely not the cause of any of the low recoveries of spiked compounds.

Firing patterns in the adaptive exponential integrate-and-fire model.

PubMed

Naud, Richard; Marcille, Nicolas; Clopath, Claudia; Gerstner, Wulfram

2008-11-01

For simulations of large spiking neuron networks, an accurate, simple and versatile single-neuron modeling framework is required. Here we explore the versatility of a simple two-equation model: the adaptive exponential integrate-and-fire neuron. We show that this model generates multiple firing patterns depending on the choice of parameter values, and present a phase diagram describing the transition from one firing type to another. We give an analytical criterion to distinguish between continuous adaption, initial bursting, regular bursting and two types of tonic spiking. Also, we report that the deterministic model is capable of producing irregular spiking when stimulated with constant current, indicating low-dimensional chaos. Lastly, the simple model is fitted to real experiments of cortical neurons under step current stimulation. The results provide support for the suitability of simple models such as the adaptive exponential integrate-and-fire neuron for large network simulations.

On a phase diagram for random neural networks with embedded spike timing dependent plasticity.

PubMed

Turova, Tatyana S; Villa, Alessandro E P

2007-01-01

This paper presents an original mathematical framework based on graph theory which is a first attempt to investigate the dynamics of a model of neural networks with embedded spike timing dependent plasticity. The neurons correspond to integrate-and-fire units located at the vertices of a finite subset of 2D lattice. There are two types of vertices, corresponding to the inhibitory and the excitatory neurons. The edges are directed and labelled by the discrete values of the synaptic strength. We assume that there is an initial firing pattern corresponding to a subset of units that generate a spike. The number of activated externally vertices is a small fraction of the entire network. The model presented here describes how such pattern propagates throughout the network as a random walk on graph. Several results are compared with computational simulations and new data are presented for identifying critical parameters of the model.

Decoding memory features from hippocampal spiking activities using sparse classification models.

PubMed

Dong Song; Hampson, Robert E; Robinson, Brian S; Marmarelis, Vasilis Z; Deadwyler, Sam A; Berger, Theodore W

2016-08-01

To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

ϒ Spike-Field Coherence in a Population of Olfactory Bulb Neurons Differentiates between Odors Irrespective of Associated Outcome

PubMed Central

Li, Anan; Gire, David H.

2015-01-01

Studies in different sensory systems indicate that short spike patterns within a spike train that carry items of sensory information can be extracted from the overall train by using field potential oscillations as a reference (Kayser et al., 2012; Panzeri et al., 2014). Here we test the hypothesis that the local field potential (LFP) provides the temporal reference frame needed to differentiate between odors regardless of associated outcome. Experiments were performed in the olfactory system of the mouse (Mus musculus) where the mitral/tufted (M/T) cell spike rate develops differential responses to rewarded and unrewarded odors as the animal learns to associate one of the odors with a reward in a go–no go behavioral task. We found that coherence of spiking in M/T cells with the ϒ LFP (65 to 95 Hz) differentiates between odors regardless of the associated behavioral outcome of odor presentation. PMID:25855190

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

Diverse spike-timing-dependent plasticity based on multilevel HfO x memristor for neuromorphic computing

NASA Astrophysics Data System (ADS)

Lu, Ke; Li, Yi; He, Wei-Fan; Chen, Jia; Zhou, Ya-Xiong; Duan, Nian; Jin, Miao-Miao; Gu, Wei; Xue, Kan-Hao; Sun, Hua-Jun; Miao, Xiang-Shui

2018-06-01

Memristors have emerged as promising candidates for artificial synaptic devices, serving as the building block of brain-inspired neuromorphic computing. In this letter, we developed a Pt/HfO x /Ti memristor with nonvolatile multilevel resistive switching behaviors due to the evolution of the conductive filaments and the variation in the Schottky barrier. Diverse state-dependent spike-timing-dependent-plasticity (STDP) functions were implemented with different initial resistance states. The measured STDP forms were adopted as the learning rule for a three-layer spiking neural network which achieves a 75.74% recognition accuracy for MNIST handwritten digit dataset. This work has shown the capability of memristive synapse in spiking neural networks for pattern recognition application.

A spiking neural integrator model of the adaptive control of action by the medial prefrontal cortex.

PubMed

Bekolay, Trevor; Laubach, Mark; Eliasmith, Chris

2014-01-29

Subjects performing simple reaction-time tasks can improve reaction times by learning the expected timing of action-imperative stimuli and preparing movements in advance. Success or failure on the previous trial is often an important factor for determining whether a subject will attempt to time the stimulus or wait for it to occur before initiating action. The medial prefrontal cortex (mPFC) has been implicated in enabling the top-down control of action depending on the outcome of the previous trial. Analysis of spike activity from the rat mPFC suggests that neural integration is a key mechanism for adaptive control in precisely timed tasks. We show through simulation that a spiking neural network consisting of coupled neural integrators captures the neural dynamics of the experimentally recorded mPFC. Errors lead to deviations in the normal dynamics of the system, a process that could enable learning from past mistakes. We expand on this coupled integrator network to construct a spiking neural network that performs a reaction-time task by following either a cue-response or timing strategy, and show that it performs the task with similar reaction times as experimental subjects while maintaining the same spiking dynamics as the experimentally recorded mPFC.

Dynamic spiking studies using the DNPH sampling train

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steger, J.L.; Knoll, J.E.

1996-12-31

The proposed aldehyde and ketone sampling method using aqueous 2,4-dinitrophenylhydrazine (DNPH) was evaluated in the laboratory and in the field. The sampling trains studied were based on the train described in SW 846 Method 0011. Nine compounds were evaluated: formaldehyde, acetaldehyde, quinone, acrolein, propionaldeyde, methyl isobutyl ketone, methyl ethyl ketone, acetophenone, and isophorone. In the laboratory, the trains were spiked both statistically and dynamically. Laboratory studies also investigated potential interferences to the method. Based on their potential to hydrolyze in acid solution to form formaldehyde, dimethylolurea, saligenin, s-trioxane, hexamethylenetetramine, and paraformaldehyde were investigated. Ten runs were performed using quadruplicate samplingmore » trains. Two of the four trains were dynamically spiked with the nine aldehydes and ketones. The test results were evaluated using the EPA method 301 criteria for method precision (< + pr - 50% relative standard deviation) and bias (correction factor of 1.00 + or - 0.30).« less

Role of GABAergic inhibition in hippocampal network oscillations.

PubMed

Mann, Edward O; Paulsen, Ole

2007-07-01

Physiological rhythmic activity in cortical circuits relies on GABAergic inhibition to balance excitation and control spike timing. With a focus on recent experimental progress in the hippocampus, here we review the mechanisms by which synaptic inhibition can control the precise timing of spike generation, by way of effects of GABAergic events on membrane conductance ('shunting' inhibition) and membrane potential ('hyperpolarizing' inhibition). Synaptic inhibition itself can be synchronized by way of interactions within networks of GABAergic neurons, and by excitatory neurons. The importance of GABAergic mechanisms for generation of cortical rhythms is now well established. What remains to be resolved is how such inhibitory control of spike timing can be harnessed for long-range fast synchronization, and the relevance of these mechanisms to network function. This review is part of the INMED/TINS special issue Physiogenic and pathogenic oscillations: the beauty and the beast, based on presentations at the annual INMED/TINS symposium (http://inmednet.com).

On dynamics of integrate-and-fire neural networks with conductance based synapses.

PubMed

Cessac, Bruno; Viéville, Thierry

2008-01-01

We present a mathematical analysis of networks with integrate-and-fire (IF) neurons with conductance based synapses. Taking into account the realistic fact that the spike time is only known within some finite precision, we propose a model where spikes are effective at times multiple of a characteristic time scale delta, where delta can be arbitrary small (in particular, well beyond the numerical precision). We make a complete mathematical characterization of the model-dynamics and obtain the following results. The asymptotic dynamics is composed by finitely many stable periodic orbits, whose number and period can be arbitrary large and can diverge in a region of the synaptic weights space, traditionally called the "edge of chaos", a notion mathematically well defined in the present paper. Furthermore, except at the edge of chaos, there is a one-to-one correspondence between the membrane potential trajectories and the raster plot. This shows that the neural code is entirely "in the spikes" in this case. As a key tool, we introduce an order parameter, easy to compute numerically, and closely related to a natural notion of entropy, providing a relevant characterization of the computational capabilities of the network. This allows us to compare the computational capabilities of leaky and IF models and conductance based models. The present study considers networks with constant input, and without time-dependent plasticity, but the framework has been designed for both extensions.

Segregated Excitatory–Inhibitory Recurrent Subnetworks in Layer 5 of the Rat Frontal Cortex

PubMed Central

Morishima, Mieko; Kobayashi, Kenta; Kato, Shigeki; Kobayashi, Kazuto; Kawaguchi, Yasuo

2017-01-01

Abstract A prominent feature of neocortical pyramidal cells (PCs) is their numerous projections to diverse brain areas. In layer 5 (L5) of the rat frontal cortex, there are 2 major subtypes of PCs that differ in their long-range axonal projections, corticopontine (CPn) cells and crossed corticostriatal (CCS) cells. The outputs of these L5 PCs can be regulated by feedback inhibition from neighboring cortical GABAergic cells. Two major subtypes of GABAergic cells are parvalbumin (PV)-positive and somatostatin (SOM)-positive cells. PV cells have a fast-spiking (FS) firing pattern, while SOM cells have a low threshold spike (LTS) and regular spiking. In this study, we found that the 2 PC subtypes in L5 selectively make recurrent connections with LTS cells. The connection patterns correlated with the morphological and physiological diversity of LTS cells. LTS cells with high input resistance (Ri) exhibited more compact dendrites and more rebound spikes than LTS cells with low Ri, which had vertically elongated dendrites. LTS subgroups differently inhibited the PC subtypes, although FS cells made nonselective connections with both projection subtypes. These results demonstrate a novel recurrent network of inhibitory and projection-specific excitatory neurons within the neocortex. PMID:29045559

A spiking neural network model of the midbrain superior colliculus that generates saccadic motor commands.

PubMed

Kasap, Bahadir; van Opstal, A John

2017-08-01

Single-unit recordings suggest that the midbrain superior colliculus (SC) acts as an optimal controller for saccadic gaze shifts. The SC is proposed to be the site within the visuomotor system where the nonlinear spatial-to-temporal transformation is carried out: the population encodes the intended saccade vector by its location in the motor map (spatial), and its trajectory and velocity by the distribution of firing rates (temporal). The neurons' burst profiles vary systematically with their anatomical positions and intended saccade vectors, to account for the nonlinear main-sequence kinematics of saccades. Yet, the underlying collicular mechanisms that could result in these firing patterns are inaccessible to current neurobiological techniques. Here, we propose a simple spiking neural network model that reproduces the spike trains of saccade-related cells in the intermediate and deep SC layers during saccades. The model assumes that SC neurons have distinct biophysical properties for spike generation that depend on their anatomical position in combination with a center-surround lateral connectivity. Both factors are needed to account for the observed firing patterns. Our model offers a basis for neuronal algorithms for spatiotemporal transformations and bio-inspired optimal controllers.

A spiking network model of cerebellar Purkinje cells and molecular layer interneurons exhibiting irregular firing

PubMed Central

Lennon, William; Hecht-Nielsen, Robert; Yamazaki, Tadashi

2014-01-01

While the anatomy of the cerebellar microcircuit is well-studied, how it implements cerebellar function is not understood. A number of models have been proposed to describe this mechanism but few emphasize the role of the vast network Purkinje cells (PKJs) form with the molecular layer interneurons (MLIs)—the stellate and basket cells. We propose a model of the MLI-PKJ network composed of simple spiking neurons incorporating the major anatomical and physiological features. In computer simulations, the model reproduces the irregular firing patterns observed in PKJs and MLIs in vitro and a shift toward faster, more regular firing patterns when inhibitory synaptic currents are blocked. In the model, the time between PKJ spikes is shown to be proportional to the amount of feedforward inhibition from an MLI on average. The two key elements of the model are: (1) spontaneously active PKJs and MLIs due to an endogenous depolarizing current, and (2) adherence to known anatomical connectivity along a parasagittal strip of cerebellar cortex. We propose this model to extend previous spiking network models of the cerebellum and for further computational investigation into the role of irregular firing and MLIs in cerebellar learning and function. PMID:25520646

Spiking and bursting patterns of fractional-order Izhikevich model

NASA Astrophysics Data System (ADS)

Teka, Wondimu W.; Upadhyay, Ranjit Kumar; Mondal, Argha

2018-03-01

Bursting and spiking oscillations play major roles in processing and transmitting information in the brain through cortical neurons that respond differently to the same signal. These oscillations display complex dynamics that might be produced by using neuronal models and varying many model parameters. Recent studies have shown that models with fractional order can produce several types of history-dependent neuronal activities without the adjustment of several parameters. We studied the fractional-order Izhikevich model and analyzed different kinds of oscillations that emerge from the fractional dynamics. The model produces a wide range of neuronal spike responses, including regular spiking, fast spiking, intrinsic bursting, mixed mode oscillations, regular bursting and chattering, by adjusting only the fractional order. Both the active and silent phase of the burst increase when the fractional-order model further deviates from the classical model. For smaller fractional order, the model produces memory dependent spiking activity after the pulse signal turned off. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. On the network level, the response of the neuronal network shifts from random to scale-free spiking. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.

Surfing a spike wave down the ventral stream.

PubMed

VanRullen, Rufin; Thorpe, Simon J

2002-10-01

Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the absolute or relative timing of spikes in spike trains. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of one such code and its relevance for visual processing. In a unified framework where the relation between stimulus saliency and spike relative timing plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure: the most salient information is represented by the first spikes over the population. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i). the selectivity of the cortical neurons, (ii). lateral interactions and (iii). top-down attentional influences from higher order cortical areas. The resulting model could account for the remarkable efficiency and rapidity of processing observed in the primate visual system.

Local Variation of Hashtag Spike Trains and Popularity in Twitter

PubMed Central

Sanlı, Ceyda; Lambiotte, Renaud

2015-01-01

We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media. PMID:26161650

Synchronised firing patterns in a random network of adaptive exponential integrate-and-fire neuron model.

PubMed

Borges, F S; Protachevicz, P R; Lameu, E L; Bonetti, R C; Iarosz, K C; Caldas, I L; Baptista, M S; Batista, A M

2017-06-01

We have studied neuronal synchronisation in a random network of adaptive exponential integrate-and-fire neurons. We study how spiking or bursting synchronous behaviour appears as a function of the coupling strength and the probability of connections, by constructing parameter spaces that identify these synchronous behaviours from measurements of the inter-spike interval and the calculation of the order parameter. Moreover, we verify the robustness of synchronisation by applying an external perturbation to each neuron. The simulations show that bursting synchronisation is more robust than spike synchronisation. Copyright © 2017 Elsevier Ltd. All rights reserved.

The introduction of dengue vaccine may temporarily cause large spikes in prevalence.

PubMed

Pandey, A; Medlock, J

2015-04-01

A dengue vaccine is expected to be available within a few years. Once vaccine is available, policy-makers will need to develop suitable policies to allocate the vaccine. Mathematical models of dengue transmission predict complex temporal patterns in prevalence, driven by seasonal oscillations in mosquito abundance. In particular, vaccine introduction may induce a transient period immediately after vaccine introduction where prevalence can spike higher than in the pre-vaccination period. These spikes in prevalence could lead to doubts about the vaccination programme among the public and even among decision-makers, possibly impeding the vaccination programme. Using simple dengue transmission models, we found that large transient spikes in prevalence are robust phenomena that occur when vaccine coverage and vaccine efficacy are not either both very high or both very low. Despite the presence of transient spikes in prevalence, the models predict that vaccination does always reduce the total number of infections in the 15 years after vaccine introduction. We conclude that policy-makers should prepare for spikes in prevalence after vaccine introduction to mitigate the burden of these spikes and to accurately measure the effectiveness of the vaccine programme.

Cortical activity patterns predict robust speech discrimination ability in noise

PubMed Central

Shetake, Jai A.; Wolf, Jordan T.; Cheung, Ryan J.; Engineer, Crystal T.; Ram, Satyananda K.; Kilgard, Michael P.

2012-01-01

The neural mechanisms that support speech discrimination in noisy conditions are poorly understood. In quiet conditions, spike timing information appears to be used in the discrimination of speech sounds. In this study, we evaluated the hypothesis that spike timing is also used to distinguish between speech sounds in noisy conditions that significantly degrade neural responses to speech sounds. We tested speech sound discrimination in rats and recorded primary auditory cortex (A1) responses to speech sounds in background noise of different intensities and spectral compositions. Our behavioral results indicate that rats, like humans, are able to accurately discriminate consonant sounds even in the presence of background noise that is as loud as the speech signal. Our neural recordings confirm that speech sounds evoke degraded but detectable responses in noise. Finally, we developed a novel neural classifier that mimics behavioral discrimination. The classifier discriminates between speech sounds by comparing the A1 spatiotemporal activity patterns evoked on single trials with the average spatiotemporal patterns evoked by known sounds. Unlike classifiers in most previous studies, this classifier is not provided with the stimulus onset time. Neural activity analyzed with the use of relative spike timing was well correlated with behavioral speech discrimination in quiet and in noise. Spike timing information integrated over longer intervals was required to accurately predict rat behavioral speech discrimination in noisy conditions. The similarity of neural and behavioral discrimination of speech in noise suggests that humans and rats may employ similar brain mechanisms to solve this problem. PMID:22098331

Validation of the World Health Organization Enzyme-Linked Immunosorbent Assay for the Quantitation of Immunoglobulin G Serotype-Specific Anti-Pneumococcal Antibodies in Human Serum

PubMed Central

2017-01-01

The World Health Organization (WHO) enzyme-linked immunosorbent assay (ELISA) guideline is currently accepted as the gold standard for the evaluation of immunoglobulin G (IgG) antibodies specific to pneumococcal capsular polysaccharide. We conducted validation of the WHO ELISA for 7 pneumococcal serotypes (4, 6B, 9V, 14, 18C, 19F, and 23F) by evaluating its specificity, precision (reproducibility and intermediate precision), accuracy, spiking recovery test, lower limit of quantification (LLOQ), and stability at the Ewha Center for Vaccine Evaluation and Study, Seoul, Korea. We found that the specificity, reproducibility, and intermediate precision were within acceptance ranges (reproducibility, coefficient of variability [CV] ≤ 15%; intermediate precision, CV ≤ 20%) for all serotypes. Comparisons between the provisional assignments of calibration sera and the results from this laboratory showed a high correlation > 94% for all 7 serotypes, supporting the accuracy of the ELISA. The spiking recovery test also fell within an acceptable range. The quantification limit, calculated using the LLOQ, for each of the serotypes was 0.05–0.093 μg/mL. The freeze-thaw stability and the short-term temperature stability were also within an acceptable range. In conclusion, we showed good performance using the standardized WHO ELISA for the evaluation of serotype-specific anti-pneumococcal IgG antibodies; the WHO ELISA can evaluate the immune response against pneumococcal vaccines with consistency and accuracy. PMID:28875600

Validation of the World Health Organization Enzyme-Linked Immunosorbent Assay for the Quantitation of Immunoglobulin G Serotype-Specific Anti-Pneumococcal Antibodies in Human Serum.

PubMed

Lee, Hyunju; Lim, Soo Young; Kim, Kyung Hyo

2017-10-01

The World Health Organization (WHO) enzyme-linked immunosorbent assay (ELISA) guideline is currently accepted as the gold standard for the evaluation of immunoglobulin G (IgG) antibodies specific to pneumococcal capsular polysaccharide. We conducted validation of the WHO ELISA for 7 pneumococcal serotypes (4, 6B, 9V, 14, 18C, 19F, and 23F) by evaluating its specificity, precision (reproducibility and intermediate precision), accuracy, spiking recovery test, lower limit of quantification (LLOQ), and stability at the Ewha Center for Vaccine Evaluation and Study, Seoul, Korea. We found that the specificity, reproducibility, and intermediate precision were within acceptance ranges (reproducibility, coefficient of variability [CV] ≤ 15%; intermediate precision, CV ≤ 20%) for all serotypes. Comparisons between the provisional assignments of calibration sera and the results from this laboratory showed a high correlation > 94% for all 7 serotypes, supporting the accuracy of the ELISA. The spiking recovery test also fell within an acceptable range. The quantification limit, calculated using the LLOQ, for each of the serotypes was 0.05-0.093 μg/mL. The freeze-thaw stability and the short-term temperature stability were also within an acceptable range. In conclusion, we showed good performance using the standardized WHO ELISA for the evaluation of serotype-specific anti-pneumococcal IgG antibodies; the WHO ELISA can evaluate the immune response against pneumococcal vaccines with consistency and accuracy. © 2017 The Korean Academy of Medical Sciences.

Highly sensitive spectrofluorimetric determination of lomefloxacin in spiked human plasma, urine and pharmaceutical preparations.

PubMed

Ulu, Sevgi Tatar

2009-09-01

A sensitive, simple and selective spectrofluorimetric method was developed for the determination of lomefloxacin in biological fluids and pharmaceutical preparations. The method is based on the reaction between the drug and 4-chloro-7-nitrobenzodioxazole in borate buffer of pH 8.5 to yield a highly fluorescent derivative that is measured at 533 nm after excitation at 433 nm. The calibration curves were linear over the concentration ranges of 12.5-625, 15-1500 and 20-2000 ng/mL for plasma, urine and standard solution, respectively. The limits of detection were 4.0 ng/mL in plasma, 5.0 ng/mL in urine and 7.0 ng/mL in standard solution. The intra-assay accuracy and precision in plasma ranged from 0.032 to 2.40% and 0.23 to 0.36%, respectively, while inter-assay accuracy and precision ranged from 0.45 to 2.10% and 0.25 to 0.38%, respectively. The intra-assay accuracy and precision estimated on spiked samples in urine ranged from 1.27 to 4.20% and 0.12 to 0.24%, respectively, while inter-assay accuracy and precision ranged from 1.60 to 4.00% and 0.14 to 0.25%, respectively. The mean recovery of lomefloxacin from plasma and urine was 98.34 and 98.43%, respectively. The method was successfully applied to the determination of lomefloxacin in pharmaceuticals and biological fluids.

Learning to Select Actions with Spiking Neurons in the Basal Ganglia

PubMed Central

Stewart, Terrence C.; Bekolay, Trevor; Eliasmith, Chris

2012-01-01

We expand our existing spiking neuron model of decision making in the cortex and basal ganglia to include local learning on the synaptic connections between the cortex and striatum, modulated by a dopaminergic reward signal. We then compare this model to animal data in the bandit task, which is used to test rodent learning in conditions involving forced choice under rewards. Our results indicate a good match in terms of both behavioral learning results and spike patterns in the ventral striatum. The model successfully generalizes to learning the utilities of multiple actions, and can learn to choose different actions in different states. The purpose of our model is to provide both high-level behavioral predictions and low-level spike timing predictions while respecting known neurophysiology and neuroanatomy. PMID:22319465

Long-Term Memory Stabilized by Noise-Induced Rehearsal

PubMed Central

Wei, Yi

2014-01-01

Cortical networks can maintain memories for decades despite the short lifetime of synaptic strengths. Can a neural network store long-lasting memories in unstable synapses? Here, we study the effects of ongoing spike-timing-dependent plasticity (STDP) on the stability of memory patterns stored in synapses of an attractor neural network. We show that certain classes of STDP rules can stabilize all stored memory patterns despite a short lifetime of synapses. In our model, unstructured neural noise, after passing through the recurrent network connections, carries the imprint of all memory patterns in temporal correlations. STDP, combined with these correlations, leads to reinforcement of all stored patterns, even those that are never explicitly visited. Our findings may provide the functional reason for irregular spiking displayed by cortical neurons and justify models of system memory consolidation. Therefore, we propose that irregular neural activity is the feature that helps cortical networks maintain stable connections. PMID:25411507

Optimization and application of ICPMS with dynamic reaction cell for precise determination of 44Ca/40Ca isotope ratios.

PubMed

Boulyga, Sergei F; Klötzli, Urs; Stingeder, Gerhard; Prohaska, Thomas

2007-10-15

An inductively coupled plasma mass spectrometer with dynamic reaction cell (ICP-DRC-MS) was optimized for determining (44)Ca/(40)Ca isotope ratios in aqueous solutions with respect to (i) repeatability, (ii) robustness, and (iii) stability. Ammonia as reaction gas allowed both the removal of (40)Ar+ interference on (40)Ca+ and collisional damping of ion density fluctuations of an ion beam extracted from an ICP. The effect of laboratory conditions as well as ICP-DRC-MS parameters such a nebulizer gas flow rate, rf power, lens potential, dwell time, or DRC parameters on precision and mass bias was studied. Precision (calculated using the "unbiased" or "n - 1" method) of a single isotope ratio measurement of a 60 ng g(-1) calcium solution (analysis time of 6 min) is routinely achievable in the range of 0.03-0.05%, which corresponded to the standard error of the mean value (n = 6) of 0.012-0.020%. These experimentally observed RSDs were close to theoretical precision values given by counting statistics. Accuracy of measured isotope ratios was assessed by comparative measurements of the same samples by ICP-DRC-MS and thermal ionization mass spectrometry (TIMS) by using isotope dilution with a (43)Ca-(48)Ca double spike. The analysis time in both cases was 1 h per analysis (10 blocks, each 6 min). The delta(44)Ca values measured by TIMS and ICP-DRC-MS with double-spike calibration in two samples (Ca ICP standard solution and digested NIST 1486 bone meal) coincided within the obtained precision. Although the applied isotope dilution with (43)Ca-(48)Ca double-spike compensates for time-dependent deviations of mass bias and allows achieving accurate results, this approach makes it necessary to measure an additional isotope pair, reducing the overall analysis time per isotope or increasing the total analysis time. Further development of external calibration by using a bracketing method would allow a wider use of ICP-DRC-MS for routine calcium isotopic measurements, but it still requires particular software or hardware improvements aimed at reliable control of environmental effects, which might influence signal stability in ICP-DRC-MS and serve as potential uncertainty sources in isotope ratio measurements.

A biophysical model examining the role of low-voltage-activated potassium currents in shaping the responses of vestibular ganglion neurons.

PubMed

Hight, Ariel E; Kalluri, Radha

2016-08-01

The vestibular nerve is characterized by two broad groups of neurons that differ in the timing of their interspike intervals; some fire at highly regular intervals, whereas others fire at highly irregular intervals. Heterogeneity in ion channel properties has been proposed as shaping these firing patterns (Highstein SM, Politoff AL. Brain Res 150: 182-187, 1978; Smith CE, Goldberg JM. Biol Cybern 54: 41-51, 1986). Kalluri et al. (J Neurophysiol 104: 2034-2051, 2010) proposed that regularity is controlled by the density of low-voltage-activated potassium currents (IKL). To examine the impact of IKL on spike timing regularity, we implemented a single-compartment model with three conductances known to be present in the vestibular ganglion: transient sodium (gNa), low-voltage-activated potassium (gKL), and high-voltage-activated potassium (gKH). Consistent with in vitro observations, removing gKL depolarized resting potential, increased input resistance and membrane time constant, and converted current step-evoked firing patterns from transient (1 spike at current onset) to sustained (many spikes). Modeled neurons were driven with a time-varying synaptic conductance that captured the random arrival times and amplitudes of glutamate-driven synaptic events. In the presence of gKL, spiking occurred only in response to large events with fast onsets. Models without gKL exhibited greater integration by responding to the superposition of rapidly arriving events. Three synaptic conductance were modeled, each with different kinetics to represent a variety of different synaptic processes. In response to all three types of synaptic conductance, models containing gKL produced spike trains with irregular interspike intervals. Only models lacking gKL when driven by rapidly arriving small excitatory postsynaptic currents were capable of generating regular spiking. Copyright © 2016 the American Physiological Society.

Characteristic microwave background distortions from collapsing domain wall bubbles

NASA Technical Reports Server (NTRS)

Goetz, Guenter; Noetzold, Dirk

1990-01-01

The magnitude and angular pattern of distortions of the microwave background are analyzed by collapsing spherical domain walls. A characteristic pattern of redshift distortions of red or blue spikes surrounded by blue discs was found. The width and height of a spike is related to the diameter and magnitude of the disc. A measurement of the relations between these quantities thus can serve as an unambiguous indicator for a collapsing spherical domain wall. From the redshift distortion in the blue discs an upper bound was found on the surface energy density of the walls sigma is less than or approximately 8 MeV cubed.

Precision and reliability of periodically and quasiperiodically driven integrate-and-fire neurons.

PubMed

Tiesinga, P H E

2002-04-01

Neurons in the brain communicate via trains of all-or-none electric events known as spikes. How the brain encodes information using spikes-the neural code-remains elusive. Here the robustness against noise of stimulus-induced neural spike trains is studied in terms of attractors and bifurcations. The dynamics of model neurons converges after a transient onto an attractor yielding a reproducible sequence of spike times. At a bifurcation point the spike times on the attractor change discontinuously when a parameter is varied. Reliability, the stability of the attractor against noise, is reduced when the neuron operates close to a bifurcation point. We determined using analytical spike-time maps the attractor and bifurcation structure of an integrate-and-fire model neuron driven by a periodic or a quasiperiodic piecewise constant current and investigated the stability of attractors against noise. The integrate-and-fire model neuron became mode locked to the periodic current with a rational winding number p/q and produced p spikes per q cycles. There were q attractors. p:q mode-locking regions formed Arnold tongues. In the model, reliability was the highest during 1:1 mode locking when there was only one attractor, as was also observed in recent experiments. The quasiperiodically driven neuron mode locked to either one of the two drive periods, or to a linear combination of both of them. Mode-locking regions were organized in Arnold tongues and reliability was again highest when there was only one attractor. These results show that neuronal reliability in response to the rhythmic drive generated by synchronized networks of neurons is profoundly influenced by the location of the Arnold tongues in parameter space.

Spiking in auditory cortex following thalamic stimulation is dominated by cortical network activity

PubMed Central

Krause, Bryan M.; Raz, Aeyal; Uhlrich, Daniel J.; Smith, Philip H.; Banks, Matthew I.

2014-01-01

The state of the sensory cortical network can have a profound impact on neural responses and perception. In rodent auditory cortex, sensory responses are reported to occur in the context of network events, similar to brief UP states, that produce “packets” of spikes and are associated with synchronized synaptic input (Bathellier et al., 2012; Hromadka et al., 2013; Luczak et al., 2013). However, traditional models based on data from visual and somatosensory cortex predict that ascending sensory thalamocortical (TC) pathways sequentially activate cells in layers 4 (L4), L2/3, and L5. The relationship between these two spatio-temporal activity patterns is unclear. Here, we used calcium imaging and electrophysiological recordings in murine auditory TC brain slices to investigate the laminar response pattern to stimulation of TC afferents. We show that although monosynaptically driven spiking in response to TC afferents occurs, the vast majority of spikes fired following TC stimulation occurs during brief UP states and outside the context of the L4>L2/3>L5 activation sequence. Specifically, monosynaptic subthreshold TC responses with similar latencies were observed throughout layers 2–6, presumably via synapses onto dendritic processes located in L3 and L4. However, monosynaptic spiking was rare, and occurred primarily in L4 and L5 non-pyramidal cells. By contrast, during brief, TC-induced UP states, spiking was dense and occurred primarily in pyramidal cells. These network events always involved infragranular layers, whereas involvement of supragranular layers was variable. During UP states, spike latencies were comparable between infragranular and supragranular cells. These data are consistent with a model in which activation of auditory cortex, especially supragranular layers, depends on internally generated network events that represent a non-linear amplification process, are initiated by infragranular cells and tightly regulated by feed-forward inhibitory cells. PMID:25285071

Definitions of state variables and state space for brain-computer interface : Part 2. Extraction and classification of feature vectors.

PubMed

Freeman, Walter J

2007-06-01

The hypothesis is proposed that the central dynamics of the action-perception cycle has five steps: emergence from an existing macroscopic brain state of a pattern that predicts a future goal state; selection of a mesoscopic frame for action control; execution of a limb trajectory by microscopic spike activity; modification of microscopic cortical spike activity by sensory inputs; construction of mesoscopic perceptual patterns; and integration of a new macroscopic brain state. The basis is the circular causality between microscopic entities (neurons) and the mesoscopic and macroscopic entities (populations) self-organized by axosynaptic interactions. Self-organization of neural activity is bidirectional in all cortices. Upwardly the organization of mesoscopic percepts from microscopic spike input predominates in primary sensory areas. Downwardly the organization of spike outputs that direct specific limb movements is by mesoscopic fields constituting plans to achieve predicted goals. The mesoscopic fields in sensory and motor cortices emerge as frames within macroscopic activity. Part 1 describes the action-perception cycle and its derivative reflex arc qualitatively. Part 2 describes the perceptual limb of the arc from microscopic MSA to mesoscopic wave packets, and from these to macroscopic EEG and global ECoG fields that express experience-dependent knowledge in successive states. These macroscopic states are conceived to embed and control mesoscopic frames in premotor and motor cortices that are observed in local ECoG and LFP of frontoparietal areas. The fields sampled by ECoG and LFP are conceived as local patterns of neural activity in which trajectories of multiple spike activities (MSA) emerge that control limb movements. Mesoscopic frames are located by use of the analytic signal from the Hilbert transform after band pass filtering. The state variables in frames are measured to construct feature vectors by which to describe and classify frame patterns. Evidence is cited to justify use of linear analysis. The aim of the review is to enable researchers to conceive and identify goal-oriented states in brain activity for use as commands, in order to relegate the details of execution to adaptive control devices outside the brain.

Awareness Becomes Necessary Between Adaptive Pattern Coding of Open and Closed Curvatures

PubMed Central

Sweeny, Timothy D.; Grabowecky, Marcia; Suzuki, Satoru

2012-01-01

Visual pattern processing becomes increasingly complex along the ventral pathway, from the low-level coding of local orientation in the primary visual cortex to the high-level coding of face identity in temporal visual areas. Previous research using pattern aftereffects as a psychophysical tool to measure activation of adaptive feature coding has suggested that awareness is relatively unimportant for the coding of orientation, but awareness is crucial for the coding of face identity. We investigated where along the ventral visual pathway awareness becomes crucial for pattern coding. Monoptic masking, which interferes with neural spiking activity in low-level processing while preserving awareness of the adaptor, eliminated open-curvature aftereffects but preserved closed-curvature aftereffects. In contrast, dichoptic masking, which spares spiking activity in low-level processing while wiping out awareness, preserved open-curvature aftereffects but eliminated closed-curvature aftereffects. This double dissociation suggests that adaptive coding of open and closed curvatures straddles the divide between weakly and strongly awareness-dependent pattern coding. PMID:21690314

Fate and degradation kinetics of nonylphenol compounds in aerobic batch digesters.

PubMed

Ömeroğlu, Seçil; Sanin, F Dilek

2014-11-01

Nonylphenol (NP) compounds are toxic and persistent chemicals that are not fully degraded either in natural or engineered systems. Current knowledge indicates that these compounds concentrate in sewage sludge. Therefore, investigating the degradation patterns and types of metabolites formed during sludge treatment are important for land application of sewage sludge. Unfortunately, the information on the fate of nonylphenol compounds in sludge treatment is very limited. This study aims to investigate the biodegradation patterns of nonylphenol diethoxylate (NP2EO) in aerobic batch digesters. For this purpose, two NP2EO spiked and two control laboratory aerobic batch digesters were operated. The spiked digester contained 3 mg/L NP2EO in the whole reactor content. The compounds of interest (parent compound and expected metabolites) were extracted with sonication and analyzed by gas chromatography-mass spectrometry (GC-MS) as a function of time. Results showed that, following the day of spike, NP2EO degraded rapidly. The metabolites observed were nonylphenol monoethoxylate (NP1EO), NP and dominantly, nonylphenoxy acetic acid (NP1EC). The mass balance over the reactors indicated that the total mass spiked was highly accounted for by the products analyzed. The time dependent analysis indicated that the parent compound degradation and daughter product formation followed first order kinetics. The digester performance parameters analyzed (VS and COD reduction) indicated that the spike of NP2EO did not affect the digester performance. Published by Elsevier Ltd.

Long-range synchrony and emergence of neural reentry

NASA Astrophysics Data System (ADS)

Keren, Hanna; Marom, Shimon

2016-11-01

Neural synchronization across long distances is a functionally important phenomenon in health and disease. In order to access the basis of different modes of long-range synchrony, we monitor spiking activities over centimetre scale in cortical networks and show that the mode of synchrony depends upon a length scale, λ, which is the minimal path that activity should propagate through to find its point of origin ready for reactivation. When λ is larger than the physical dimension of the network, distant neuronal populations operate synchronously, giving rise to irregularly occurring network-wide events that last hundreds of milliseconds to several seconds. In contrast, when λ approaches the dimension of the network, a continuous self-sustained reentry propagation emerges, a regular seizure-like mode that is marked by precise spatiotemporal patterns (‘synfire chains’) and may last many minutes. Termination of a reentry phase is preceded by a decrease of propagation speed to a halt. Stimulation decreases both propagation speed and λ values, which modifies the synchrony mode respectively. The results contribute to the understanding of the origin and termination of different modes of neural synchrony as well as their long-range spatial patterns, while hopefully catering to manipulation of the phenomena in pathological conditions.

Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity.

PubMed

Srinivasa, Narayan; Cho, Youngkwan

2014-01-01

A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns-both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

SPIKY: a graphical user interface for monitoring spike train synchrony

PubMed Central

Mulansky, Mario; Bozanic, Nebojsa

2015-01-01

Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. PMID:25744888

SPIKY: a graphical user interface for monitoring spike train synchrony.

PubMed

Kreuz, Thomas; Mulansky, Mario; Bozanic, Nebojsa

2015-05-01

Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. Copyright © 2015 the American Physiological Society.

Learning Universal Computations with Spikes

PubMed Central

Thalmeier, Dominik; Uhlmann, Marvin; Kappen, Hilbert J.; Memmesheimer, Raoul-Martin

2016-01-01

Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them. PMID:27309381

Bursting patterns and mixed-mode oscillations in reduced Purkinje model

NASA Astrophysics Data System (ADS)

Zhan, Feibiao; Liu, Shenquan; Wang, Jing; Lu, Bo

2018-02-01

Bursting discharge is a ubiquitous behavior in neurons, and abundant bursting patterns imply many physiological information. There exists a closely potential link between bifurcation phenomenon and the number of spikes per burst as well as mixed-mode oscillations (MMOs). In this paper, we have mainly explored the dynamical behavior of the reduced Purkinje cell and the existence of MMOs. First, we adopted the codimension-one bifurcation to illustrate the generation mechanism of bursting in the reduced Purkinje cell model via slow-fast dynamics analysis and demonstrate the process of spike-adding. Furthermore, we have computed the first Lyapunov coefficient of Hopf bifurcation to determine whether it is subcritical or supercritical and depicted the diagrams of inter-spike intervals (ISIs) to examine the chaos. Moreover, the bifurcation diagram near the cusp point is obtained by making the codimension-two bifurcation analysis for the fast subsystem. Finally, we have a discussion on mixed-mode oscillations and it is further investigated using the characteristic index that is Devil’s staircase.

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation

PubMed Central

Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André

2014-01-01

We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits. PMID:24672422

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation.

PubMed

Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan C; van Schaik, André

2014-01-01

We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits.

Spike-like solitary waves in incompressible boundary layers driven by a travelling wave.

PubMed

Feng, Peihua; Zhang, Jiazhong; Wang, Wei

2016-06-01

Nonlinear waves produced in an incompressible boundary layer driven by a travelling wave are investigated, with damping considered as well. As one of the typical nonlinear waves, the spike-like wave is governed by the driven-damped Benjamin-Ono equation. The wave field enters a completely irregular state beyond a critical time, increasing the amplitude of the driving wave continuously. On the other hand, the number of spikes of solitary waves increases through multiplication of the wave pattern. The wave energy grows in a sequence of sharp steps, and hysteresis loops are found in the system. The wave energy jumps to different levels with multiplication of the wave, which is described by winding number bifurcation of phase trajectories. Also, the phenomenon of multiplication and hysteresis steps is found when varying the speed of driving wave as well. Moreover, the nature of the change of wave pattern and its energy is the stability loss of the wave caused by saddle-node bifurcation.

Evolution of the cerebellum as a neuronal machine for Bayesian state estimation

NASA Astrophysics Data System (ADS)

Paulin, M. G.

2005-09-01

The cerebellum evolved in association with the electric sense and vestibular sense of the earliest vertebrates. Accurate information provided by these sensory systems would have been essential for precise control of orienting behavior in predation. A simple model shows that individual spikes in electrosensory primary afferent neurons can be interpreted as measurements of prey location. Using this result, I construct a computational neural model in which the spatial distribution of spikes in a secondary electrosensory map forms a Monte Carlo approximation to the Bayesian posterior distribution of prey locations given the sense data. The neural circuit that emerges naturally to perform this task resembles the cerebellar-like hindbrain electrosensory filtering circuitry of sharks and other electrosensory vertebrates. The optimal filtering mechanism can be extended to handle dynamical targets observed from a dynamical platform; that is, to construct an optimal dynamical state estimator using spiking neurons. This may provide a generic model of cerebellar computation. Vertebrate motion-sensing neurons have specific fractional-order dynamical characteristics that allow Bayesian state estimators to be implemented elegantly and efficiently, using simple operations with asynchronous pulses, i.e. spikes. The computational neural models described in this paper represent a novel kind of particle filter, using spikes as particles. The models are specific and make testable predictions about computational mechanisms in cerebellar circuitry, while providing a plausible explanation of cerebellar contributions to aspects of motor control, perception and cognition.

Action Potential Broadening in Capsaicin-Sensitive DRG Neurons from Frequency-Dependent Reduction of Kv3 Current

PubMed Central

Liu, Pin W.; Blair, Nathaniel T.

2017-01-01

Action potential (AP) shape is a key determinant of cellular electrophysiological behavior. We found that in small-diameter, capsaicin-sensitive dorsal root ganglia neurons corresponding to nociceptors (from rats of either sex), stimulation at frequencies as low as 1 Hz produced progressive broadening of the APs. Stimulation at 10 Hz for 3 s resulted in an increase in AP width by an average of 76 ± 7% at 22°C and by 38 ± 3% at 35°C. AP clamp experiments showed that spike broadening results from frequency-dependent reduction of potassium current during spike repolarization. The major current responsible for frequency-dependent reduction of overall spike-repolarizing potassium current was identified as Kv3 current by its sensitivity to low concentrations of 4-aminopyridine (IC50 <100 μm) and block by the peptide inhibitor blood depressing substance I (BDS-I). There was a small component of Kv1-mediated current during AP repolarization, but this current did not show frequency-dependent reduction. In a small fraction of cells, there was a component of calcium-dependent potassium current that showed frequency-dependent reduction, but the contribution to overall potassium current reduction was almost always much smaller than that of Kv3-mediated current. These results show that Kv3 channels make a major contribution to spike repolarization in small-diameter DRG neurons and undergo frequency-dependent reduction, leading to spike broadening at moderate firing frequencies. Spike broadening from frequency-dependent reduction in Kv3 current could mitigate the frequency-dependent decreases in conduction velocity typical of C-fiber axons. SIGNIFICANCE STATEMENT Small-diameter dorsal root ganglia (DRG) neurons mediating nociception and other sensory modalities express many types of potassium channels, but how they combine to control firing patterns and conduction is not well understood. We found that action potentials of small-diameter rat DRG neurons showed spike broadening at frequencies as low as 1 Hz and that spike broadening resulted predominantly from frequency-dependent inactivation of Kv3 channels. Spike width helps to control transmitter release, conduction velocity, and firing patterns and understanding the role of particular potassium channels can help to guide new pharmacological strategies for targeting pain-sensing neurons selectively. PMID:28877968

Action Potential Broadening in Capsaicin-Sensitive DRG Neurons from Frequency-Dependent Reduction of Kv3 Current.

PubMed

Liu, Pin W; Blair, Nathaniel T; Bean, Bruce P

2017-10-04

Action potential (AP) shape is a key determinant of cellular electrophysiological behavior. We found that in small-diameter, capsaicin-sensitive dorsal root ganglia neurons corresponding to nociceptors (from rats of either sex), stimulation at frequencies as low as 1 Hz produced progressive broadening of the APs. Stimulation at 10 Hz for 3 s resulted in an increase in AP width by an average of 76 ± 7% at 22°C and by 38 ± 3% at 35°C. AP clamp experiments showed that spike broadening results from frequency-dependent reduction of potassium current during spike repolarization. The major current responsible for frequency-dependent reduction of overall spike-repolarizing potassium current was identified as Kv3 current by its sensitivity to low concentrations of 4-aminopyridine (IC 50 <100 μm) and block by the peptide inhibitor blood depressing substance I (BDS-I). There was a small component of Kv1-mediated current during AP repolarization, but this current did not show frequency-dependent reduction. In a small fraction of cells, there was a component of calcium-dependent potassium current that showed frequency-dependent reduction, but the contribution to overall potassium current reduction was almost always much smaller than that of Kv3-mediated current. These results show that Kv3 channels make a major contribution to spike repolarization in small-diameter DRG neurons and undergo frequency-dependent reduction, leading to spike broadening at moderate firing frequencies. Spike broadening from frequency-dependent reduction in Kv3 current could mitigate the frequency-dependent decreases in conduction velocity typical of C-fiber axons. SIGNIFICANCE STATEMENT Small-diameter dorsal root ganglia (DRG) neurons mediating nociception and other sensory modalities express many types of potassium channels, but how they combine to control firing patterns and conduction is not well understood. We found that action potentials of small-diameter rat DRG neurons showed spike broadening at frequencies as low as 1 Hz and that spike broadening resulted predominantly from frequency-dependent inactivation of Kv3 channels. Spike width helps to control transmitter release, conduction velocity, and firing patterns and understanding the role of particular potassium channels can help to guide new pharmacological strategies for targeting pain-sensing neurons selectively. Copyright © 2017 the authors 0270-6474/17/379705-10$15.00/0.

GABAergic circuits control input-spike coupling in the piriform cortex.

PubMed

Luna, Victor M; Schoppa, Nathan E

2008-08-27

Odor coding in mammals is widely believed to involve synchronized gamma frequency (30-70 Hz) oscillations in the first processing structure, the olfactory bulb. How such inputs are read in downstream cortical structures however is not known. Here we used patch-clamp recordings in rat piriform cortex slices to examine cellular mechanisms that shape how the cortex integrates inputs from bulb mitral cells. Electrical stimulation of mitral cell axons in the lateral olfactory tract (LOT) resulted in excitation of pyramidal cells (PCs), which was followed approximately 10 ms later by inhibition that was highly reproducible between trials in its onset time. This inhibition was somatic in origin and appeared to be driven through a feedforward mechanism, wherein GABAergic interneurons were directly excited by mitral cell axons. The precise inhibition affected action potential firing in PCs in two distinct ways. First, by abruptly terminating PC excitation, it limited the PC response to each EPSP to exactly one, precisely timed action potential. In addition, inhibition limited the summation of EPSPs across time, such that PCs fired action potentials in strong preference for synchronized inputs arriving in a time window of <5 ms. Both mechanisms would help ensure that PCs respond faithfully and selectively to mitral cell inputs arriving as a synchronized gamma frequency pattern.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System.

PubMed

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L; Wennekers, Thomas; Chicca, Elisabetta

2012-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

PubMed Central

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L.; Wennekers, Thomas; Chicca, Elisabetta

2011-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems. PMID:22347163

Transfer of Timing Information from RGC to LGN Spike Trains

NASA Astrophysics Data System (ADS)

Teich, Malvin C.; Lowen, Steven B.; Saleh, Bahaa E. A.; Kaplan, Ehud

1998-03-01

We have studied the firing patterns of retinal ganglion cells (RGCs) and their target lateral geniculate nucleus (LGN) cells. We find that clusters of spikes in the RGC neural firing pattern appear at the LGN output essentially unchanged, while isolated RGC firing events are more likely to be eliminated; thus the LGN action-potential sequence is therefore not merely a randomly deleted version of the RGC spike train. Employing information-theoretic techniques we developed for point processes,(B. E. A. Saleh and M. C. Teich, Phys. Rev. Lett.) 58, 2656--2659 (1987). we are able to estimate the information efficiency of the LGN neuronal output --- the proportion of the variation in the LGN firing pattern that carries information about its associated RGC input. A suitably modified integrate-and-fire neural model reproduces both the enhanced clustering in the LGN data (which accounts for the increased coefficient of variation) and the measured value of information efficiency, as well as mimicking the results of other observed statistical measures. Reliable information transmission therefore coexists with fractal fluctuations, which appear in RGC and LGN firing patterns.(M. C. Teich, C. Heneghan, S. B. Lowen, T. Ozaki, and E. Kaplan, J. Opt. Soc. Am. A) 14, 529--546 (1997).

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

PubMed Central

Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.

2014-01-01

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

When do correlations increase with firing rates in recurrent networks?

PubMed Central

2017-01-01

A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate—a relationship previously explained in feedforward networks driven by correlated input—emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix. PMID:28448499

Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses.

PubMed

Sudhakar, Shyam Kumar; Torben-Nielsen, Benjamin; De Schutter, Erik

2015-12-01

Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it.

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

PubMed

Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

2014-01-01

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

In vivo mouse inferior olive neurons exhibit heterogeneous subthreshold oscillations and spiking patterns

PubMed Central

Khosrovani, S.; Van Der Giessen, R. S.; De Zeeuw, C. I.; De Jeu, M. T. G.

2007-01-01

In vitro whole-cell recordings of the inferior olive have demonstrated that its neurons are electrotonically coupled and have a tendency to oscillate. However, it remains to be shown to what extent subthreshold oscillations do indeed occur in the inferior olive in vivo and whether its spatiotemporal firing pattern may be dynamically generated by including or excluding different types of oscillatory neurons. Here, we did whole-cell recordings of olivary neurons in vivo to investigate the relation between their subthreshold activities and their spiking behavior in an intact brain. The vast majority of neurons (85%) showed subthreshold oscillatory activities. The frequencies of these subthreshold oscillations were used to distinguish four main olivary subtypes by statistical means. Type I showed both sinusoidal subthreshold oscillations (SSTOs) and low-threshold Ca2+ oscillations (LTOs) (16%); type II showed only sinusoidal subthreshold oscillations (13%); type III showed only low-threshold Ca2+ oscillations (56%); and type IV did not reveal any subthreshold oscillations (15%). These subthreshold oscillation frequencies were strongly correlated with the frequencies of preferred spiking. The frequency characteristics of the subthreshold oscillations and spiking behavior of virtually all olivary neurons were stable throughout the recordings. However, the occurrence of spontaneous or evoked action potentials modified the subthreshold oscillation by resetting the phase of its peak toward 90°. Together, these findings indicate that the inferior olive in intact mammals offers a rich repertoire of different neurons with relatively stable frequency settings, which can be used to generate and reset temporal firing patterns in a dynamically coupled ensemble. PMID:17895389

Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences

PubMed Central

Fetterhoff, Dustin; Kraft, Robert A.; Sandler, Roman A.; Opris, Ioan; Sexton, Cheryl A.; Marmarelis, Vasilis Z.; Hampson, Robert E.; Deadwyler, Sam A.

2015-01-01

Fractality, represented as self-similar repeating patterns, is ubiquitous in nature and the brain. Dynamic patterns of hippocampal spike trains are known to exhibit multifractal properties during working memory processing; however, it is unclear whether the multifractal properties inherent to hippocampal spike trains reflect active cognitive processing. To examine this possibility, hippocampal neuronal ensembles were recorded from rats before, during and after a spatial working memory task following administration of tetrahydrocannabinol (THC), a memory-impairing component of cannabis. Multifractal detrended fluctuation analysis was performed on hippocampal interspike interval sequences to determine characteristics of monofractal long-range temporal correlations (LRTCs), quantified by the Hurst exponent, and the degree/magnitude of multifractal complexity, quantified by the width of the singularity spectrum. Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses. Conversely, LRTCs are largest during resting state recordings, therefore reflecting different information compared to multifractality. In order to deepen conceptual understanding of multifractal complexity and LRTCs, these measures were compared to classical methods using hippocampal frequency content and firing variability measures. These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality. Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological, and pathological states. PMID:26441562

A simple method for the enrichment of bisphenols using boron nitride.

PubMed

Fischnaller, Martin; Bakry, Rania; Bonn, Günther K

2016-03-01

A simple solid-phase extraction method for the enrichment of 5 bisphenol derivatives using hexagonal boron nitride (BN) was developed. BN was applied to concentrate bisphenol derivatives in spiked water samples and the compounds were analyzed using HPLC coupled to fluorescence detection. The effect of pH and organic solvents on the extraction efficiency was investigated. An enrichment factor up to 100 was achieved without evaporation and reconstitution. The developed method was applied for the determination of bisphenol A migrated from some polycarbonate plastic products. Furthermore, bisphenol derivatives were analyzed in spiked and non-spiked canned food and beverages. None of the analyzed samples exceeded the migration limit set by the European Union of 0.6mg/kg food. The method showed good recovery rates ranging from 80% to 110%. Validation of the method was performed in terms of accuracy and precision. The applied method is robust, fast, efficient and easily adaptable to different analytical problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Video time encoding machines.

PubMed

Lazar, Aurel A; Pnevmatikakis, Eftychios A

2011-03-01

We investigate architectures for time encoding and time decoding of visual stimuli such as natural and synthetic video streams (movies, animation). The architecture for time encoding is akin to models of the early visual system. It consists of a bank of filters in cascade with single-input multi-output neural circuits. Neuron firing is based on either a threshold-and-fire or an integrate-and-fire spiking mechanism with feedback. We show that analog information is represented by the neural circuits as projections on a set of band-limited functions determined by the spike sequence. Under Nyquist-type and frame conditions, the encoded signal can be recovered from these projections with arbitrary precision. For the video time encoding machine architecture, we demonstrate that band-limited video streams of finite energy can be faithfully recovered from the spike trains and provide a stable algorithm for perfect recovery. The key condition for recovery calls for the number of neurons in the population to be above a threshold value.

Video Time Encoding Machines

PubMed Central

Lazar, Aurel A.; Pnevmatikakis, Eftychios A.

2013-01-01

We investigate architectures for time encoding and time decoding of visual stimuli such as natural and synthetic video streams (movies, animation). The architecture for time encoding is akin to models of the early visual system. It consists of a bank of filters in cascade with single-input multi-output neural circuits. Neuron firing is based on either a threshold-and-fire or an integrate-and-fire spiking mechanism with feedback. We show that analog information is represented by the neural circuits as projections on a set of band-limited functions determined by the spike sequence. Under Nyquist-type and frame conditions, the encoded signal can be recovered from these projections with arbitrary precision. For the video time encoding machine architecture, we demonstrate that band-limited video streams of finite energy can be faithfully recovered from the spike trains and provide a stable algorithm for perfect recovery. The key condition for recovery calls for the number of neurons in the population to be above a threshold value. PMID:21296708

Simultaneous extraction of polycyclic aromatic hydrocarbons through the complete dissolution of solid biological samples in sodium hydroxide/urea/thiourea aqueous solution.

PubMed

Mohammad, Sedigheh Ale; Ghanemi, Kamal; Larki, Arash

2016-12-09

In order to precisely and simultaneously extract polycyclic aromatic hydrocarbons (PAHs) for measurement using a high performance liquid chromatography-fluorescence detector (HPLC-FL), a novel sample preparation method was developed. This method is based on the complete and fast dissolution of biological samples in a new non-alcoholic alkaline medium. A solution composed of NaOH/urea/thiourea at an optimized ratio was used for complete dissolution of approximately 0.25g dried fish samples within 20min. The proposed method was conducted at 10°C and under atmospheric pressure to obtain a stable and highly homogeneous solution, without the need for microwaves or any other apparatus. This process operates at considerably lower temperature than conventional methods and provides an opportunity to simultaneously extract the target analytes from their matrices by adding the extracting solvent in the initial steps of the dissolution; this process greatly reduced the time of analysis and the loss of analytes via vaporization. Several key parameters were identified and their effects on precision and extraction recoveries were investigated. Linearity over a calibration range of 1.0-100 and 2.5-100ngg -1 was achieved, with high coefficients of determination (r 2 ) ranging between 0.9987 and 0.9998. Based on relative standard deviations (n=5), the intra-day and inter-day precisions of the spiked PAHs were found to be better than 3.1% and 3.2%, respectively, at a concentration level of 25ngg -1 . The recoveries of PAH from spiked marine fish tissues and shrimp samples were in the range of 90.6%-100.4%. The spiked samples were also treated with the alcoholic alkaline and Soxhlet extraction methods in order to provide a comparison. Copyright © 2016 Elsevier B.V. All rights reserved.

Long-Term Temporal Imprecision of Information Coding in the Anterior Cingulate Cortex of Mice with Peripheral Inflammation or Nerve Injury

PubMed Central

Li, Xiang-Yao; Wang, Ning; Wang, Yong-Jie; Zuo, Zhen-Xing; Koga, Kohei; Luo, Fei

2014-01-01

Temporal properties of spike firing in the central nervous system (CNS) are critical for neuronal coding and the precision of information storage. Chronic pain has been reported to affect cognitive and emotional functions, in addition to trigger long-term plasticity in sensory synapses and behavioral sensitization. Less is known about the possible changes in temporal precision of cortical neurons in chronic pain conditions. In the present study, we investigated the temporal precision of action potential firing in the anterior cingulate cortex (ACC) by using both in vivo and in vitro electrophysiological approaches. We found that peripheral inflammation caused by complete Freund's adjuvant (CFA) increased the standard deviation (SD) of spikes latency (also called jitter) of ∼51% of recorded neurons in the ACC of adult rats in vivo. Similar increases in jitter were found in ACC neurons using in vitro brain slices from adult mice with peripheral inflammation or nerve injury. Bath application of glutamate receptor antagonists CNQX and AP5 abolished the enhancement of jitter induced by CFA injection or nerve injury, suggesting that the increased jitter depends on the glutamatergic synaptic transmission. Activation of adenylyl cyclases (ACs) by bath application of forskolin increased jitter, whereas genetic deletion of AC1 abolished the change of jitter caused by CFA inflammation. Our study provides strong evidence for long-term changes of temporal precision of information coding in cortical neurons after peripheral injuries and explains neuronal mechanism for chronic pain caused cognitive and emotional impairment. PMID:25100600

Computational solution of spike overlapping using data-based subtraction algorithms to resolve synchronous sympathetic nerve discharge

PubMed Central

Su, Chun-Kuei; Chiang, Chia-Hsun; Lee, Chia-Ming; Fan, Yu-Pei; Ho, Chiu-Ming; Shyu, Liang-Yu

2013-01-01

Sympathetic nerves conveying central commands to regulate visceral functions often display activities in synchronous bursts. To understand how individual fibers fire synchronously, we establish “oligofiber recording techniques” to record “several” nerve fiber activities simultaneously, using in vitro splanchnic sympathetic nerve–thoracic spinal cord preparations of neonatal rats as experimental models. While distinct spike potentials were easily recorded from collagenase-dissociated sympathetic fibers, a problem arising from synchronous nerve discharges is a higher incidence of complex waveforms resulted from spike overlapping. Because commercial softwares do not provide an explicit solution for spike overlapping, a series of custom-made LabVIEW programs incorporated with MATLAB scripts was therefore written for spike sorting. Spikes were represented as data points after waveform feature extraction and automatically grouped by k-means clustering followed by principal component analysis (PCA) to verify their waveform homogeneity. For dissimilar waveforms with exceeding Hotelling's T2 distances from the cluster centroids, a unique data-based subtraction algorithm (SA) was used to determine if they were the complex waveforms resulted from superimposing a spike pattern close to the cluster centroid with the other signals that could be observed in original recordings. In comparisons with commercial software, higher accuracy was achieved by analyses using our algorithms for the synthetic data that contained synchronous spiking and complex waveforms. Moreover, both T2-selected and SA-retrieved spikes were combined as unit activities. Quantitative analyses were performed to evaluate if unit activities truly originated from single fibers. We conclude that applications of our programs can help to resolve synchronous sympathetic nerve discharges (SND). PMID:24198782

Neuronal Spike Timing Adaptation Described with a Fractional Leaky Integrate-and-Fire Model

PubMed Central

Teka, Wondimu; Marinov, Toma M.; Santamaria, Fidel

2014-01-01

The voltage trace of neuronal activities can follow multiple timescale dynamics that arise from correlated membrane conductances. Such processes can result in power-law behavior in which the membrane voltage cannot be characterized with a single time constant. The emergent effect of these membrane correlations is a non-Markovian process that can be modeled with a fractional derivative. A fractional derivative is a non-local process in which the value of the variable is determined by integrating a temporal weighted voltage trace, also called the memory trace. Here we developed and analyzed a fractional leaky integrate-and-fire model in which the exponent of the fractional derivative can vary from 0 to 1, with 1 representing the normal derivative. As the exponent of the fractional derivative decreases, the weights of the voltage trace increase. Thus, the value of the voltage is increasingly correlated with the trajectory of the voltage in the past. By varying only the fractional exponent, our model can reproduce upward and downward spike adaptations found experimentally in neocortical pyramidal cells and tectal neurons in vitro. The model also produces spikes with longer first-spike latency and high inter-spike variability with power-law distribution. We further analyze spike adaptation and the responses to noisy and oscillatory input. The fractional model generates reliable spike patterns in response to noisy input. Overall, the spiking activity of the fractional leaky integrate-and-fire model deviates from the spiking activity of the Markovian model and reflects the temporal accumulated intrinsic membrane dynamics that affect the response of the neuron to external stimulation. PMID:24675903

Differences in spike train variability in rat vasopressin and oxytocin neurons and their relationship to synaptic activity

PubMed Central

Li, Chunyan; Tripathi, Pradeep K; Armstrong, William E

2007-01-01

The firing pattern of magnocellular neurosecretory neurons is intimately related to hormone release, but the relative contribution of synaptic versus intrinsic factors to the temporal dispersion of spikes is unknown. In the present study, we examined the firing patterns of vasopressin (VP) and oxytocin (OT) supraoptic neurons in coronal slices from virgin female rats, with and without blockade of inhibitory and excitatory synaptic currents. Inhibitory postsynaptic currents (IPSCs) were twice as prevalent as their excitatory counterparts (EPSCs), and both were more prevalent in OT compared with VP neurons. Oxytocin neurons fired more slowly and irregularly than VP neurons near threshold. Blockade of Cl− currents (including tonic and synaptic currents) with picrotoxin reduced interspike interval (ISI) variability of continuously firing OT and VP neurons without altering input resistance or firing rate. Blockade of EPSCs did not affect firing pattern. Phasic bursting neurons (putative VP neurons) were inconsistently affected by broad synaptic blockade, suggesting that intrinsic factors may dominate the ISI distribution during this mode in the slice. Specific blockade of synaptic IPSCs with gabazine also reduced ISI variability, but only in OT neurons. In all cases, the effect of inhibitory blockade on firing pattern was independent of any consistent change in input resistance or firing rate. Since the great majority of IPSCs are randomly distributed, miniature events (mIPSCs) in the coronal slice, these findings imply that even mIPSCs can impart irregularity to the firing pattern of OT neurons in particular, and could be important in regulating spike patterning in vivo. For example, the increased firing variability that precedes bursting in OT neurons during lactation could be related to significant changes in synaptic activity. PMID:17332000

Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity

PubMed Central

Srinivasa, Narayan; Cho, Youngkwan

2014-01-01

A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns—both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity. PMID:25566045

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

PubMed

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

2018-02-01

Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

Spiking Neural Classifier with Lumped Dendritic Nonlinearity and Binary Synapses: A Current Mode VLSI Implementation and Analysis.

PubMed

Bhaduri, Aritra; Banerjee, Amitava; Roy, Subhrajit; Kar, Sougata; Basu, Arindam

2018-03-01

We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with fewer synaptic resources than conventional algorithms. We show that even in real analog systems with manufacturing imperfections (CV of 23.5% and 14.4% for dendritic branch gains and leaks respectively), this network is able to produce comparable results with fewer synaptic resources. The chip fabricated in [Formula: see text]m complementary metal oxide semiconductor has eight dendrites per cell and uses two opposing cells per class to cancel common-mode inputs. The chip can operate down to a [Formula: see text] V and dissipates 19 nW of static power per neuronal cell and [Formula: see text] 125 pJ/spike. For two-class classification problems of high-dimensional rate encoded binary patterns, the hardware achieves comparable performance as software implementation of the same with only about a 0.5% reduction in accuracy. On two UCI data sets, the IC integrated circuit has classification accuracy comparable to standard machine learners like support vector machines and extreme learning machines while using two to five times binary synapses. We also show that the system can operate on mean rate encoded spike patterns, as well as short bursts of spikes. To the best of our knowledge, this is the first attempt in hardware to perform classification exploiting dendritic properties and binary synapses.

Slow Cholinergic Modulation of Spike Probability in Ultra-Fast Time-Coding Sensory Neurons

PubMed Central

Goyer, David; Kurth, Stefanie; Rübsamen, Rudolf

2016-01-01

Abstract Sensory processing in the lower auditory pathway is generally considered to be rigid and thus less subject to modulation than central processing. However, in addition to the powerful bottom-up excitation by auditory nerve fibers, the ventral cochlear nucleus also receives efferent cholinergic innervation from both auditory and nonauditory top–down sources. We thus tested the influence of cholinergic modulation on highly precise time-coding neurons in the cochlear nucleus of the Mongolian gerbil. By combining electrophysiological recordings with pharmacological application in vitro and in vivo, we found 55–72% of spherical bushy cells (SBCs) to be depolarized by carbachol on two time scales, ranging from hundreds of milliseconds to minutes. These effects were mediated by nicotinic and muscarinic acetylcholine receptors, respectively. Pharmacological block of muscarinic receptors hyperpolarized the resting membrane potential, suggesting a novel mechanism of setting the resting membrane potential for SBC. The cholinergic depolarization led to an increase of spike probability in SBCs without compromising the temporal precision of the SBC output in vitro. In vivo, iontophoretic application of carbachol resulted in an increase in spontaneous SBC activity. The inclusion of cholinergic modulation in an SBC model predicted an expansion of the dynamic range of sound responses and increased temporal acuity. Our results thus suggest of a top–down modulatory system mediated by acetylcholine which influences temporally precise information processing in the lower auditory pathway. PMID:27699207

Comparative study of six sequential spectrophotometric methods for quantification and separation of ribavirin, sofosbuvir and daclatasvir: An application on Laboratory prepared mixture, pharmaceutical preparations, spiked human urine, spiked human plasma, and dissolution test.

PubMed

Hassan, Wafaa S; Elmasry, Manal S; Elsayed, Heba M; Zidan, Dalia W

2018-09-05

In accordance with International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) guidelines, six novel, simple and precise sequential spectrophotometric methods were developed and validated for the simultaneous analysis of Ribavirin (RIB), Sofosbuvir (SOF), and Daclatasvir (DAC) in their mixture without prior separation steps. These drugs are described as co-administered for treatment of Hepatitis C virus (HCV). HCV is the cause of hepatitis C and some cancers such as liver cancer (hepatocellular carcinoma) and lymphomas in humans. These techniques consisted of several sequential steps using zero, ratio and/or derivative spectra. DAC was first determined through direct spectrophotometry at 313.7 nm without any interference of the other two drugs while RIB and SOF can be determined after ratio subtraction through five methods; Ratio difference spectrophotometric method, successive derivative ratio method, constant center, isoabsorptive method at 238.8 nm, and mean centering of the ratio spectra (MCR) at 224 nm and 258 nm for RIB and SOF, respectively. The calibration curve is linear over the concentration ranges of (6-42), (10-70) and (4-16) μg/mL for RIB, SOF, and DAC, respectively. This method was successfully applied to commercial pharmaceutical preparation of the drugs, spiked human urine, and spiked human plasma. The above methods are very simple methods that were developed for the simultaneous determination of binary and ternary mixtures and so enhance signal-to-noise ratio. The method has been successfully applied to the simultaneous analysis of RIB, SOF, and DAC in laboratory prepared mixtures. The obtained results are statistically compared with those obtained by the official or reported methods, showing no significant difference with respect to accuracy and precision at p = 0.05. Copyright © 2018 Elsevier B.V. All rights reserved.

Interlaboratory evaluation of trace element determination in workplace air filter samples by inductively coupled plasma mass spectrometry†‡

PubMed Central

Shulman, Stanley A.; Brisson, Michael J.; Howe, Alan M.

2015-01-01

Inductively coupled plasma mass spectrometry (ICP-MS) is becoming more widely used for trace elemental analysis in the occupational hygiene field, and consequently new ICP-MS international standard procedures have been promulgated by ASTM International and ISO. However, there is a dearth of interlaboratory performance data for this analytical methodology. In an effort to fill this data void, an interlaboratory evaluation of ICP-MS for determining trace elements in workplace air samples was conducted, towards fulfillment of method validation requirements for international voluntary consensus standard test methods. The study was performed in accordance with applicable statistical procedures for investigating interlaboratory precision. The evaluation was carried out using certified 37-mm diameter mixed-cellulose ester (MCE) filters that were fortified with 21 elements of concern in occupational hygiene. Elements were spiked at levels ranging from 0.025 to 10 μg filter−1, with three different filter loadings denoted “Low”, “Medium” and “High”. Participating laboratories were recruited from a pool of over fifty invitees; ultimately twenty laboratories from Europe, North America and Asia submitted results. Triplicates of each certified filter with elemental contents at three different levels, plus media blanks spiked with reagent, were conveyed to each volunteer laboratory. Each participant was also provided a copy of the test method which each participant was asked to follow; spiking levels were unknown to the participants. The laboratories were requested to prepare the filters by one of three sample preparation procedures, i.e., hotplate digestion, microwave digestion or hot block extraction, which were described in the test method. Participants were then asked to analyze aliquots of the prepared samples by ICP-MS, and to report their data in units of μg filter−1. Most interlaboratory precision estimates were acceptable for medium- and high-level spikes (RSD <25%), but generally yielded greater uncertainties than were anticipated at the outset of the study. PMID:22038017

Uppermost synchronized generators of spike-wave activity are localized in limbic cortical areas in late-onset absence status epilepticus.

PubMed

Piros, Palma; Puskas, Szilvia; Emri, Miklos; Opposits, Gabor; Spisak, Tamas; Fekete, Istvan; Clemens, Bela

2014-03-01

Absence status (AS) epilepticus with generalized spike-wave pattern is frequently found in severely ill patients in whom several disease states co-exist. The cortical generators of the ictal EEG pattern and EEG functional connectivity (EEGfC) of this condition are unknown. The present study investigated the localization of the uppermost synchronized generators of spike-wave activity in AS. Seven patients with late-onset AS were investigated by EEG spectral analysis, LORETA (Low Resolution Electromagnetic Tomography) source imaging, and LSC (LORETA Source Correlation) analysis, which estimates cortico-cortical EEGfC among 23 ROIs (regions of interest) in each hemisphere. All the patients showed generalized ictal EEG activity. Maximum Z-scored spectral power was found in the 1-6 Hz and 12-14 Hz frequency bands. LORETA showed that the uppermost synchronized generators of 1-6 Hz band activity were localized in frontal and temporal cortical areas that are parts of the limbic system. For the 12-14 Hz band, abnormally synchronized generators were found in the antero-medial frontal cortex. Unlike the rather stereotyped spectral and LORETA findings, the individual EEGfC patterns were very dissimilar. The findings are discussed in the context of nonconvulsive seizure types and the role of the underlying cortical areas in late-onset AS. The diversity of the EEGfC patterns remains an enigma. Localizing the cortical generators of the EEG patterns contributes to understanding the neurophysiology of the condition. Copyright © 2013 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Dynamic interneuron-principal cell interplay leads to a specific pattern of in vitro ictogenesis.

PubMed

Lévesque, Maxime; Chen, Li-Yuan; Hamidi, Shabnam; Avoli, Massimo

2018-07-01

Ictal discharges induced by 4-aminopyridine in the in vitro rodent entorhinal cortex present with either low-voltage fast or sudden onset patterns. The role of interneurons in initiating low-voltage fast onset ictal discharges is well established but the processes leading to sudden onset ictal discharges remain unclear. We analysed here the participation of interneurons (n = 75) and principal cells (n = 13) in the sudden onset pattern by employing in vitro tetrode wire recordings in the entorhinal cortex of brain slices from Sprague-Dawley rats. Ictal discharges emerged from a background of frequently occurring interictal spikes that were associated to a specific interneuron/principal cell interplay. High rates of interneuron firing occurred 12 ms before interictal spike onset while principal cells fired later during low interneuron firing. In contrast, the onset of sudden ictal discharges was characterized by increased firing from principal cells 627 ms before ictal onset whereas interneurons increased their firing rates 161 ms before ictal onset. Our data show that sudden onset ictogenesis is associated with frequently occurring interictal spikes resting on the interplay between interneurons and principal cells while ictal discharges stem from enhanced principal cell firing leading to increased interneuron activity. These findings indicate that specific patterns of interactions between interneurons and principal cells shape interictal and ictal discharges with sudden onset in the rodent entorhinal cortex. We propose that specific neuronal interactions lead to the generation of distinct onset patterns in focal epileptic disorders. Copyright © 2018 Elsevier Inc. All rights reserved.

Control of Large Actuator Arrays Using Pattern-Forming Systems

DTIC Science & Technology

1998-01-01

carbon dioxide in motor vehicles [1, 3, 4, 5]. Physics examples include patterns observed in shaken collections of small spherical particles, gas...181 6.5 Narrow spike equilibrium shape as a function of β . . . . . . . . . . . . . . . . .182 7.1 One cycle of the...value after a cycle of the pattern solution has been excited as in figure 7.1

Changes in complex spike activity during classical conditioning

PubMed Central

Rasmussen, Anders; Jirenhed, Dan-Anders; Wetmore, Daniel Z.; Hesslow, Germund

2014-01-01

The cerebellar cortex is necessary for adaptively timed conditioned responses (CRs) in eyeblink conditioning. During conditioning, Purkinje cells acquire pause responses or “Purkinje cell CRs” to the conditioned stimuli (CS), resulting in disinhibition of the cerebellar nuclei (CN), allowing them to activate motor nuclei that control eyeblinks. This disinhibition also causes inhibition of the inferior olive (IO), via the nucleo-olivary pathway (N-O). Activation of the IO, which relays the unconditional stimulus (US) to the cortex, elicits characteristic complex spikes in Purkinje cells. Although Purkinje cell activity, as well as stimulation of the CN, is known to influence IO activity, much remains to be learned about the way that learned changes in simple spike firing affects the IO. In the present study, we analyzed changes in simple and complex spike firing, in extracellular Purkinje cell records, from the C3 zone, in decerebrate ferrets undergoing training in a conditioning paradigm. In agreement with the N-O feedback hypothesis, acquisition resulted in a gradual decrease in complex spike activity during the conditioned stimulus, with a delay that is consistent with the long N-O latency. Also supporting the feedback hypothesis, training with a short interstimulus interval (ISI), which does not lead to acquisition of a Purkinje cell CR, did not cause a suppression of complex spike activity. In contrast, observations that extinction did not lead to a recovery in complex spike activity and the irregular patterns of simple and complex spike activity after the conditioned stimulus are less conclusive. PMID:25140129

Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks.

PubMed

Sailamul, Pachaya; Jang, Jaeson; Paik, Se-Bum

2017-12-01

Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.

Parallel network simulations with NEURON.

PubMed

Migliore, M; Cannia, C; Lytton, W W; Markram, Henry; Hines, M L

2006-10-01

The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2,000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored.

Parallel Network Simulations with NEURON

PubMed Central

Migliore, M.; Cannia, C.; Lytton, W.W; Markram, Henry; Hines, M. L.

2009-01-01

The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored. PMID:16732488

Long-term memory stabilized by noise-induced rehearsal.

PubMed

Wei, Yi; Koulakov, Alexei A

2014-11-19

Cortical networks can maintain memories for decades despite the short lifetime of synaptic strengths. Can a neural network store long-lasting memories in unstable synapses? Here, we study the effects of ongoing spike-timing-dependent plasticity (STDP) on the stability of memory patterns stored in synapses of an attractor neural network. We show that certain classes of STDP rules can stabilize all stored memory patterns despite a short lifetime of synapses. In our model, unstructured neural noise, after passing through the recurrent network connections, carries the imprint of all memory patterns in temporal correlations. STDP, combined with these correlations, leads to reinforcement of all stored patterns, even those that are never explicitly visited. Our findings may provide the functional reason for irregular spiking displayed by cortical neurons and justify models of system memory consolidation. Therefore, we propose that irregular neural activity is the feature that helps cortical networks maintain stable connections. Copyright © 2014 the authors 0270-6474/14/3415804-12$15.00/0.

A thesaurus for a neural population code

PubMed Central

Ganmor, Elad; Segev, Ronen; Schneidman, Elad

2015-01-01

Information is carried in the brain by the joint spiking patterns of large groups of noisy, unreliable neurons. This noise limits the capacity of the neural code and determines how information can be transmitted and read-out. To accurately decode, the brain must overcome this noise and identify which patterns are semantically similar. We use models of network encoding noise to learn a thesaurus for populations of neurons in the vertebrate retina responding to artificial and natural videos, measuring the similarity between population responses to visual stimuli based on the information they carry. This thesaurus reveals that the code is organized in clusters of synonymous activity patterns that are similar in meaning but may differ considerably in their structure. This organization is highly reminiscent of the design of engineered codes. We suggest that the brain may use this structure and show how it allows accurate decoding of novel stimuli from novel spiking patterns. DOI: http://dx.doi.org/10.7554/eLife.06134.001 PMID:26347983

Activity Regulates the Incidence of Heteronymous Sensory-Motor Connections

PubMed Central

Mendelsohn, Alana I.; Simon, Christian M.; Abbott, L. F.; Mentis, George Z.; Jessell, Thomas M.

2015-01-01

Summary The construction of spinal sensory-motor circuits involves the selection of appropriate synaptic partners and the allocation of precise synaptic input densities. Many aspects of spinal sensory-motor selectivity appear to be preserved when peripheral sensory activation is blocked, which has led to a view that sensory-motor circuits are assembled in an activity-independent manner. Yet it remains unclear whether activity-dependent refinement has a role in the establishment of connections between sensory afferents and those motor pools that have synergistic biomechanical functions. We show here that genetically abolishing central sensory-motor neurotransmission leads to a selective enhancement in the number and density of such “heteronymous” connections, whereas other aspects of sensory-motor connectivity are preserved. Spike-timing dependent synaptic refinement represents one possible mechanism for the changes in connectivity observed after activity blockade. Our findings therefore reveal that sensory activity does have a limited and selective role in the establishment of patterned monosynaptic sensory-motor connections. PMID:26094608

Neurobehavioral consequences of continuous spike and waves during slow sleep (CSWS) in a pediatric population: A pattern of developmental hindrance.

PubMed

De Giorgis, Valentina; Filippini, Melissa; Macasaet, Joyce Ann; Masnada, Silvia; Veggiotti, Pierangelo

2017-09-01

Continuous spike and waves during slow sleep (CSWS) is a typical EEG pattern defined as diffuse, bilateral and recently also unilateral or focal localization spike-wave occurring in slow sleep or non-rapid eye movement sleep. Literature results so far point out a progressive deterioration and decline of intellectual functioning in CSWS patients, i.e. a loss of previously normally acquired skills, as well as persistent neurobehavioral disorders, beyond seizure and EEG control. The objective of this study was to shed light on the neurobehavioral impact of CSWS and to identify the potential clinical risk factors for development. We conducted a retrospective study involving a series of 16 CSWS idiopathic patients age 3-16years, considering the entire duration of epilepsy from the onset to the outcome, i.e. remission of CSWS pattern. All patients were longitudinally assessed taking into account clinical (sex, age at onset, lateralization and localization of epileptiform abnormalities, spike wave index, number of antiepileptic drugs) and behavioral features. Intelligent Quotient (IQ) was measured in the whole sample, whereas visuo-spatial attention, visuo-motor skills, short term memory and academic abilities (reading and writing) were tested in 6 out of 16 patients. Our results showed that the most vulnerable from an intellectual point of view were those children who had an early-onset of CSWS whereas those with later onset resulted less affected (p=0.004). Neuropsychological outcome was better than the behavioral one and the lexical-semantic route in reading and writing resulted more severely affected compared to the phonological route. Cognitive deterioration is one but not the only consequence of CSWS. Especially with respect to verbal skills, CSWS is responsible of a pattern of consequences in terms of developmental hindrance, including slowing of development and stagnation, whereas deterioration is rare. Behavioral and academic problems tend to persist beyond epilepsy resolution. Copyright © 2017 Elsevier Inc. All rights reserved.

Recurrent Coupling Improves Discrimination of Temporal Spike Patterns

PubMed Central

Yuan, Chun-Wei; Leibold, Christian

2012-01-01

Despite the ubiquitous presence of recurrent synaptic connections in sensory neuronal systems, their general functional purpose is not well understood. A recent conceptual advance has been achieved by theories of reservoir computing in which recurrent networks have been proposed to generate short-term memory as well as to improve neuronal representation of the sensory input for subsequent computations. Here, we present a numerical study on the distinct effects of inhibitory and excitatory recurrence in a canonical linear classification task. It is found that both types of coupling improve the ability to discriminate temporal spike patterns as compared to a purely feed-forward system, although in different ways. For a large class of inhibitory networks, the network’s performance is optimal as long as a fraction of roughly 50% of neurons per stimulus is active in the resulting population code. Thereby the contribution of inactive neurons to the neural code is found to be even more informative than that of the active neurons, generating an inherent robustness of classification performance against temporal jitter of the input spikes. Excitatory couplings are found to not only produce a short-term memory buffer but also to improve linear separability of the population patterns by evoking more irregular firing as compared to the purely inhibitory case. As the excitatory connectivity becomes more sparse, firing becomes more variable, and pattern separability improves. We argue that the proposed paradigm is particularly well-suited as a conceptual framework for processing of sensory information in the auditory pathway. PMID:22586392

Predicting the synaptic information efficacy in cortical layer 5 pyramidal neurons using a minimal integrate-and-fire model.

PubMed

London, Michael; Larkum, Matthew E; Häusser, Michael

2008-11-01

Synaptic information efficacy (SIE) is a statistical measure to quantify the efficacy of a synapse. It measures how much information is gained, on the average, about the output spike train of a postsynaptic neuron if the input spike train is known. It is a particularly appropriate measure for assessing the input-output relationship of neurons receiving dynamic stimuli. Here, we compare the SIE of simulated synaptic inputs measured experimentally in layer 5 cortical pyramidal neurons in vitro with the SIE computed from a minimal model constructed to fit the recorded data. We show that even with a simple model that is far from perfect in predicting the precise timing of the output spikes of the real neuron, the SIE can still be accurately predicted. This arises from the ability of the model to predict output spikes influenced by the input more accurately than those driven by the background current. This indicates that in this context, some spikes may be more important than others. Lastly we demonstrate another aspect where using mutual information could be beneficial in evaluating the quality of a model, by measuring the mutual information between the model's output and the neuron's output. The SIE, thus, could be a useful tool for assessing the quality of models of single neurons in preserving input-output relationship, a property that becomes crucial when we start connecting these reduced models to construct complex realistic neuronal networks.

SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.

PubMed

Pastore, Vito Paolo; Godjoski, Aleksandar; Martinoia, Sergio; Massobrio, Paolo

2018-01-01

We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

PubMed

Erfanian Saeedi, Nafise; Blamey, Peter J; Burkitt, Anthony N; Grayden, David B

2016-04-01

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV: structuring synaptic pathways among recurrent connections.

PubMed

Gilson, Matthieu; Burkitt, Anthony N; Grayden, David B; Thomas, Doreen A; van Hemmen, J Leo

2009-12-01

In neuronal networks, the changes of synaptic strength (or weight) performed by spike-timing-dependent plasticity (STDP) are hypothesized to give rise to functional network structure. This article investigates how this phenomenon occurs for the excitatory recurrent connections of a network with fixed input weights that is stimulated by external spike trains. We develop a theoretical framework based on the Poisson neuron model to analyze the interplay between the neuronal activity (firing rates and the spike-time correlations) and the learning dynamics, when the network is stimulated by correlated pools of homogeneous Poisson spike trains. STDP can lead to both a stabilization of all the neuron firing rates (homeostatic equilibrium) and a robust weight specialization. The pattern of specialization for the recurrent weights is determined by a relationship between the input firing-rate and correlation structures, the network topology, the STDP parameters and the synaptic response properties. We find conditions for feed-forward pathways or areas with strengthened self-feedback to emerge in an initially homogeneous recurrent network.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks

PubMed Central

Erfanian Saeedi, Nafise; Blamey, Peter J.; Burkitt, Anthony N.; Grayden, David B.

2016-01-01

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy. PMID:27049657

Pattern Formation in Keller-Segel Chemotaxis Models with Logistic Growth

NASA Astrophysics Data System (ADS)

Jin, Ling; Wang, Qi; Zhang, Zengyan

In this paper, we investigate pattern formation in Keller-Segel chemotaxis models over a multidimensional bounded domain subject to homogeneous Neumann boundary conditions. It is shown that the positive homogeneous steady state loses its stability as chemoattraction rate χ increases. Then using Crandall-Rabinowitz local theory with χ being the bifurcation parameter, we obtain the existence of nonhomogeneous steady states of the system which bifurcate from this homogeneous steady state. Stability of the bifurcating solutions is also established through rigorous and detailed calculations. Our results provide a selection mechanism of stable wavemode which states that the only stable bifurcation branch must have a wavemode number that minimizes the bifurcation value. Finally, we perform extensive numerical simulations on the formation of stable steady states with striking structures such as boundary spikes, interior spikes, stripes, etc. These nontrivial patterns can model cellular aggregation that develop through chemotactic movements in biological systems.

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

PubMed Central

Canova, Carlos; Denker, Michael; Gerstein, George; Helias, Moritz

2016-01-01

With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity. PMID:27420734

Mechanisms of Firing Patterns in Fast-Spiking Cortical Interneurons

PubMed Central

Golomb, David; Donner, Karnit; Shacham, Liron; Shlosberg, Dan; Amitai, Yael; Hansel, David

2007-01-01

Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. Their spike responses to step currents occur almost immediately following the step onset or after a substantial delay, during which subthreshold oscillations are frequently observed. Their firing patterns include high-frequency tonic firing and rhythmic or irregular bursting (stuttering). What is the origin of this variability? In the present paper, we hypothesize that it emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. To test this hypothesis, we construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na+, delayed-rectifier K+, and slowly inactivating d-type K+ conductances. The model is analyzed using nonlinear dynamical system theory. For small Na+ window current, the neuron exhibits high-frequency tonic firing. At current threshold, the spike response is almost instantaneous for small d-current conductance, g d, and it is delayed for larger g d. As g d further increases, the neuron stutters. Noise substantially reduces the delay duration and induces subthreshold oscillations. In contrast, when the Na+ window current is large, the neuron always fires tonically. Near threshold, the firing rates are low, and the delay to firing is only weakly sensitive to noise; subthreshold oscillations are not observed. We propose that the variability in the response of cortical FS neurons is a consequence of heterogeneities in their g d and in the strength of their Na+ window current. We predict the existence of two types of firing patterns in FS neurons, differing in the sensitivity of the delay duration to noise, in the minimal firing rate of the tonic discharge, and in the existence of subthreshold oscillations. We report experimental results from intracellular recordings supporting this prediction. PMID:17696606

Mechanisms of firing patterns in fast-spiking cortical interneurons.

PubMed

Golomb, David; Donner, Karnit; Shacham, Liron; Shlosberg, Dan; Amitai, Yael; Hansel, David

2007-08-01

Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. Their spike responses to step currents occur almost immediately following the step onset or after a substantial delay, during which subthreshold oscillations are frequently observed. Their firing patterns include high-frequency tonic firing and rhythmic or irregular bursting (stuttering). What is the origin of this variability? In the present paper, we hypothesize that it emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. To test this hypothesis, we construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na(+), delayed-rectifier K(+), and slowly inactivating d-type K(+) conductances. The model is analyzed using nonlinear dynamical system theory. For small Na(+) window current, the neuron exhibits high-frequency tonic firing. At current threshold, the spike response is almost instantaneous for small d-current conductance, gd, and it is delayed for larger gd. As gd further increases, the neuron stutters. Noise substantially reduces the delay duration and induces subthreshold oscillations. In contrast, when the Na(+) window current is large, the neuron always fires tonically. Near threshold, the firing rates are low, and the delay to firing is only weakly sensitive to noise; subthreshold oscillations are not observed. We propose that the variability in the response of cortical FS neurons is a consequence of heterogeneities in their gd and in the strength of their Na(+) window current. We predict the existence of two types of firing patterns in FS neurons, differing in the sensitivity of the delay duration to noise, in the minimal firing rate of the tonic discharge, and in the existence of subthreshold oscillations. We report experimental results from intracellular recordings supporting this prediction.

Different propagation speeds of recalled sequences in plastic spiking neural networks

NASA Astrophysics Data System (ADS)

Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

2015-03-01

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments.

Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone

PubMed Central

Gerhard, Felipe; Kispersky, Tilman; Gutierrez, Gabrielle J.; Marder, Eve; Kramer, Mark; Eden, Uri

2013-01-01

Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities. PMID:23874181

Comparison between McMaster and Mini-FLOTAC methods for the enumeration of Eimeria maxima oocysts in poultry excreta.

PubMed

Bortoluzzi, C; Paras, K L; Applegate, T J; Verocai, G G

2018-04-30

Monitoring Eimeria shedding has become more important due to the recent restrictions to the use of antibiotics within the poultry industry. Therefore, there is a need for the implementation of more precise and accurate quantitative diagnostic techniques. The objective of this study was to compare the precision and accuracy between the Mini-FLOTAC and the McMaster techniques for quantitative diagnosis of Eimeria maxima oocyst in poultry. Twelve pools of excreta samples of broiler chickens experimentally infected with E. maxima were analyzed for the comparison between Mini-FLOTAC and McMaster technique using, the detection limits (dl) of 23 and 25, respectively. Additionally, six excreta samples were used to compare the precision of different dl (5, 10, 23, and 46) using the Mini-FLOTAC technique. For precision comparisons, five technical replicates of each sample (five replicate slides on one excreta slurry) were read for calculating the mean oocyst per gram of excreta (OPG) count, standard deviation (SD), coefficient of variation (CV), and precision of both aforementioned comparisons. To compare accuracy between the methods (McMaster, and Mini-FLOTAC dl 5 and 23), excreta from uninfected chickens was spiked with 100, 500, 1,000, 5,000, or 10,000 OPG; additional samples remained unspiked (negative control). For each spiking level, three samples were read in triplicate, totaling nine reads per spiking level per technique. Data were transformed using log10 to obtain normality and homogeneity of variances. A significant correlation (R = 0.74; p = 0.006) was observed between the mean OPG of the McMaster dl 25 and the Mini-FLOTAC dl 23. Mean OPG, CV, SD, and precision were not statistically different between the McMaster dl 25 and Mini-FLOTAC dl 23. Despite the absence of statistical difference (p > 0.05), Mini-FLOTAC dl 5 showed a numerically lower SD and CV than Mini-FLOTAC dl 23. The Pearson correlation coefficient revealed significant and positive correlation among the four dl (p ≤ 0.05). In the accuracy study, it was observed that the Mini-FLOTAC dl 5 and 23 were more accurate than the McMaster for 100 OPG, and the Mini-FLOTAC dl 23 had the highest accuracy for 500 OPG. The McMaster and Mini-FLOTAC dl 23 techniques were more accurate than the Mini-FLOTAC dl 5 for 5,000 OPG, and both dl of the Mini-FLOTAC were less accurate for 10,000 OPG counts than the McMaster technique. However, the overall accuracy of the Mini-FLOTAC dl 23 was higher than the McMaster and Mini-FLOTAC dl 5 techniques. Copyright © 2018 Elsevier B.V. All rights reserved.

Thalamic inhibition: diverse sources, diverse scales

PubMed Central

Halassa, Michael M.; Acsády, László

2016-01-01

The thalamus is the major source of cortical inputs shaping sensation, action and cognition. Thalamic circuits are targeted by two major inhibitory systems: the thalamic reticular nucleus (TRN) and extra-thalamic inhibitory (ETI) inputs. A unifying framework of how these systems operate is currently lacking. Here, we propose that TRN circuits are specialized to exert thalamic control at different spatiotemporal scales. Local inhibition of thalamic spike rates prevails during attentional selection whereas global inhibition more likely during sleep. In contrast, the ETI (arising from basal ganglia, zona incerta, anterior pretectum and pontine reticular formation) provides temporally-precise and focal inhibition, impacting spike timing. Together, these inhibitory systems allow graded control of thalamic output, enabling thalamocortical operations to dynamically match ongoing behavioral demands. PMID:27589879

Nonlinear computations shaping temporal processing of precortical vision.

PubMed

Butts, Daniel A; Cui, Yuwei; Casti, Alexander R R

2016-09-01

Computations performed by the visual pathway are constructed by neural circuits distributed over multiple stages of processing, and thus it is challenging to determine how different stages contribute on the basis of recordings from single areas. In the current article, we address this problem in the lateral geniculate nucleus (LGN), using experiments combined with nonlinear modeling capable of isolating various circuit contributions. We recorded cat LGN neurons presented with temporally modulated spots of various sizes, which drove temporally precise LGN responses. We utilized simultaneously recorded S-potentials, corresponding to the primary retinal ganglion cell (RGC) input to each LGN cell, to distinguish the computations underlying temporal precision in the retina from those in the LGN. Nonlinear models with excitatory and delayed suppressive terms were sufficient to explain temporal precision in the LGN, and we found that models of the S-potentials were nearly identical, although with a lower threshold. To determine whether additional influences shaped the response at the level of the LGN, we extended this model to use the S-potential input in combination with stimulus-driven terms to predict the LGN response. We found that the S-potential input "explained away" the major excitatory and delayed suppressive terms responsible for temporal patterning of LGN spike trains but revealed additional contributions, largely PULL suppression, to the LGN response. Using this novel combination of recordings and modeling, we were thus able to dissect multiple circuit contributions to LGN temporal responses across retina and LGN, and set the foundation for targeted study of each stage. Copyright © 2016 the American Physiological Society.

Detailed Characterization of Local Field Potential Oscillations and Their Relationship to Spike Timing in the Antennal Lobe of the Moth Manduca sexta

PubMed Central

Daly, Kevin C.; Galán, Roberto F.; Peters, Oakland J.; Staudacher, Erich M.

2011-01-01

The transient oscillatory model of odor identity encoding seeks to explain how odorants with spatially overlapped patterns of input into primary olfactory networks can be discriminated. This model provides several testable predictions about the distributed nature of network oscillations and how they control spike timing. To test these predictions, 16 channel electrode arrays were placed within the antennal lobe (AL) of the moth Manduca sexta. Unitary spiking and multi site local field potential (LFP) recordings were made during spontaneous activity and in response to repeated presentations of an odor panel. We quantified oscillatory frequency, cross correlations between LFP recording sites, and spike–LFP phase relationships. We show that odor-driven AL oscillations in Manduca are frequency modulating (FM) from ∼100 to 30 Hz; this was odorant and stimulus duration dependent. FM oscillatory responses were localized to one or two recording sites suggesting a localized (perhaps glomerular) not distributed source. LFP cross correlations further demonstrated that only a small (r < 0.05) distributed and oscillatory component was present. Cross spectral density analysis demonstrated the frequency of these weakly distributed oscillations was state dependent (spontaneous activity = 25–55 Hz; odor-driven = 55–85 Hz). Surprisingly, vector strength analysis indicated that unitary phase locking of spikes to the LFP was strongest during spontaneous activity and dropped significantly during responses. Application of bicuculline, a GABAA receptor antagonist, significantly lowered the frequency content of odor-driven distributed oscillatory activity. Bicuculline significantly reduced spike phase locking generally, but the ubiquitous pattern of increased phase locking during spontaneous activity persisted. Collectively, these results indicate that oscillations perform poorly as a stimulus-mediated spike synchronizing mechanism for Manduca and hence are incongruent with the transient oscillatory model. PMID:22046161

Bursting synchronization dynamics of pancreatic β-cells with electrical and chemical coupling.

PubMed

Meng, Pan; Wang, Qingyun; Lu, Qishao

2013-06-01

Based on bifurcation analysis, the synchronization behaviors of two identical pancreatic β-cells connected by electrical and chemical coupling are investigated, respectively. Various firing patterns are produced in coupled cells when a single cell exhibits tonic spiking or square-wave bursting individually, irrespectively of what the cells are connected by electrical or chemical coupling. On the one hand, cells can burst synchronously for both weak electrical and chemical coupling when an isolated cell exhibits tonic spiking itself. In particular, for electrically coupled cells, under the variation of the coupling strength there exist complex transition processes of synchronous firing patterns such as "fold/limit cycle" type of bursting, then anti-phase continuous spiking, followed by the "fold/torus" type of bursting, and finally in-phase tonic spiking. On the other hand, it is shown that when the individual cell exhibits square-wave bursting, suitable coupling strength can make the electrically coupled system generate "fold/Hopf" bursting via "fold/fold" hysteresis loop; whereas, the chemically coupled cells generate "fold/subHopf" bursting. Especially, chemically coupled bursters can exhibit inverse period-adding bursting sequence. Fast-slow dynamics analysis is applied to explore the generation mechanism of these bursting oscillations. The above analysis of bursting types and the transition may provide us with better insight into understanding the role of coupling in the dynamic behaviors of pancreatic β-cells.

Mouse Visual Neocortex Supports Multiple Stereotyped Patterns of Microcircuit Activity

PubMed Central

Sadovsky, Alexander J.

2014-01-01

Spiking correlations between neocortical neurons provide insight into the underlying synaptic connectivity that defines cortical microcircuitry. Here, using two-photon calcium fluorescence imaging, we observed the simultaneous dynamics of hundreds of neurons in slices of mouse primary visual cortex (V1). Consistent with a balance of excitation and inhibition, V1 dynamics were characterized by a linear scaling between firing rate and circuit size. Using lagged firing correlations between neurons, we generated functional wiring diagrams to evaluate the topological features of V1 microcircuitry. We found that circuit connectivity exhibited both cyclic graph motifs, indicating recurrent wiring, and acyclic graph motifs, indicating feedforward wiring. After overlaying the functional wiring diagrams onto the imaged field of view, we found properties consistent with Rentian scaling: wiring diagrams were topologically efficient because they minimized wiring with a modular architecture. Within single imaged fields of view, V1 contained multiple discrete circuits that were overlapping and highly interdigitated but were still distinct from one another. The majority of neurons that were shared between circuits displayed peri-event spiking activity whose timing was specific to the active circuit, whereas spike times for a smaller percentage of neurons were invariant to circuit identity. These data provide evidence that V1 microcircuitry exhibits balanced dynamics, is efficiently arranged in anatomical space, and is capable of supporting a diversity of multineuron spike firing patterns from overlapping sets of neurons. PMID:24899701

Synchronization in Random Pulse Oscillator Networks

NASA Astrophysics Data System (ADS)

Brown, Kevin; Hermundstad, Ann

Motivated by synchronization phenomena in neural systems, we study synchronization of random networks of coupled pulse oscillators. We begin by considering binomial random networks whose nodes have intrinsic linear dynamics. We quantify order in the network spiking dynamics using a new measure: the normalized Lev-Zimpel complexity (LZC) of the nodes' spike trains. Starting from a globally-synchronized state, we see two broad classes of behaviors. In one (''temporally random''), the LZC is high and nodes spike independently with no coherent pattern. In another (''temporally regular''), the network does not globally synchronize but instead forms coherent, repeating population firing patterns with low LZC. No topological feature of the network reliably predicts whether an individual network will show temporally random or regular behavior; however, we find evidence that degree heterogeneity in binomial networks has a strong effect on the resulting state. To confirm these findings, we generate random networks with independently-adjustable degree mean and variance. We find that the likelihood of temporally-random behavior increases as degree variance increases. Our results indicate the subtle and complex relationship between network structure and dynamics.

Method for quantifying nitromethane in blood as a potential biomarker of halonitromethane exposure.

PubMed

Alwis, K Udeni; Blount, Benjamin C; Silva, Lalith K; Smith, Mitchell M; Loose, Karl-Hermann

2008-04-01

The cytotoxicity and genotoxicity of nitromethane and its halogenated analogues in mammals raise concerns about potential toxicity to humans. This study shows that halonitromethanes are not stable in human blood and undergo dehalogenation to form nitromethane. We quantified nitromethane in human blood using solid-phase microextraction (SPME) headspace sampling coupled with gas chromatography (GC) and high resolution mass spectrometry (HRMS). The limit of detection was 0.01 microg/L with a linear calibration curve spanning 3 orders of magnitude. This method employs isotope dilution to precisely quantify trace amounts of nitromethane (coefficient of variation <6%). At three spiked concentrations of nitromethane, method accuracy ranged from 88 to 99%. We applied this method to blood samples collected from 632 people with no known occupational exposure to nitromethane or halonitromethanes. Nitromethane was detected in all blood samples tested (range: 0.28-3.79 microg/L, median: 0.66 microg/L). Time-course experiments with trichloronitromethane- and tribromonitromethane-spiked blood showed that nitromethane was the major product formed (1 nmole tribromonitromethane formed 0.59 nmole of nitromethane, whereas 1 nmole trichloronitromethane formed 0.77 nmole nitromethane). Nitromethane may form endogenously from peroxynitrite: nitromethane concentrations increased proportionately in blood samples spiked with peroxynitrite. Blood nitromethane can be a biomarker of exposure to both nitromethane and halonitromethanes. This sensitive, accurate, and precise analytical method can be used to determine baseline blood nitromethane level in the general population. It can also be used to study the health impact from exposure to nitromethane and halonitromethanes in occupational environments and to assess trichloronitromethane (chloropicrin) exposure in chemical terrorism investigations.

Pattern reverberation in networks of excitable systems with connection delays

NASA Astrophysics Data System (ADS)

Lücken, Leonhard; Rosin, David P.; Worlitzer, Vasco M.; Yanchuk, Serhiy

2017-01-01

We consider the recurrent pulse-coupled networks of excitable elements with delayed connections, which are inspired by the biological neural networks. If the delays are tuned appropriately, the network can either stay in the steady resting state, or alternatively, exhibit a desired spiking pattern. It is shown that such a network can be used as a pattern-recognition system. More specifically, the application of the correct pattern as an external input to the network leads to a self-sustained reverberation of the encoded pattern. In terms of the coupling structure, the tolerance and the refractory time of the individual systems, we determine the conditions for the uniqueness of the sustained activity, i.e., for the functionality of the network as an unambiguous pattern detector. We point out the relation of the considered systems with cyclic polychronous groups and show how the assumed delay configurations may arise in a self-organized manner when a spike-time dependent plasticity of the connection delays is assumed. As excitable elements, we employ the simplistic coincidence detector models as well as the Hodgkin-Huxley neuron models. Moreover, the system is implemented experimentally on a Field-Programmable Gate Array.

Pattern formation and three-dimensional instability in rotating flows

NASA Astrophysics Data System (ADS)

Christensen, Erik A.; Aubry, Nadine; Sorensen, Jens N.

1997-03-01

A fluid flow enclosed in a cylindrical container where fluid motion is created by the rotation of one end wall as a centrifugal fan is studied. Direct numerical simulations and spatio-temporal analysis have been performed in the early transition scenario, which includes a steady-unsteady transition and a breakdown of axisymmetric to three-dimensional flow behavior. In the early unsteady regime of the flow, the central vortex undergoes a vertical beating motion, accompanied by axisymmetric spikes formation on the edge of the breakdown bubble. As traveling waves, the spikes move along the central vortex core toward the rotating end-wall. As the Reynolds number is increased further, the flow undergoes a three-dimensional instability. The influence of the latter on the previous patterns is studied.

Lowering the quantification limit of the QubitTM RNA HS assay using RNA spike-in.

PubMed

Li, Xin; Ben-Dov, Iddo Z; Mauro, Maurizio; Williams, Zev

2015-05-06

RNA quantification is often a prerequisite for most RNA analyses such as RNA sequencing. However, the relatively low sensitivity and large sample consumption of traditional RNA quantification methods such as UV spectrophotometry and even the much more sensitive fluorescence-based RNA quantification assays, such as the Qubit™ RNA HS Assay, are often inadequate for measuring minute levels of RNA isolated from limited cell and tissue samples and biofluids. Thus, there is a pressing need for a more sensitive method to reliably and robustly detect trace levels of RNA without interference from DNA. To improve the quantification limit of the Qubit™ RNA HS Assay, we spiked-in a known quantity of RNA to achieve the minimum reading required by the assay. Samples containing trace amounts of RNA were then added to the spike-in and measured as a reading increase over RNA spike-in baseline. We determined the accuracy and precision of reading increases between 1 and 20 pg/μL as well as RNA-specificity in this range, and compared to those of RiboGreen(®), another sensitive fluorescence-based RNA quantification assay. We then applied Qubit™ Assay with RNA spike-in to quantify plasma RNA samples. RNA spike-in improved the quantification limit of the Qubit™ RNA HS Assay 5-fold, from 25 pg/μL down to 5 pg/μL while maintaining high specificity to RNA. This enabled quantification of RNA with original concentration as low as 55.6 pg/μL compared to 250 pg/μL for the standard assay and decreased sample consumption from 5 to 1 ng. Plasma RNA samples that were not measurable by the Qubit™ RNA HS Assay were measurable by our modified method. The Qubit™ RNA HS Assay with RNA spike-in is able to quantify RNA with high specificity at 5-fold lower concentration and uses 5-fold less sample quantity than the standard Qubit™ Assay.

Synchronicity and Rhythmicity of Purkinje Cell Firing during Generalized Spike-and-Wave Discharges in a Natural Mouse Model of Absence Epilepsy

PubMed Central

Kros, Lieke; Lindeman, Sander; Eelkman Rooda, Oscar H. J.; Murugesan, Pavithra; Bina, Lorenzo; Bosman, Laurens W. J.; De Zeeuw, Chris I.; Hoebeek, Freek E.

2017-01-01

Absence epilepsy is characterized by the occurrence of generalized spike and wave discharges (GSWDs) in electrocorticographical (ECoG) recordings representing oscillatory activity in thalamocortical networks. The oscillatory nature of GSWDs has been shown to be reflected in the simple spike activity of cerebellar Purkinje cells and in the activity of their target neurons in the cerebellar nuclei, but it is unclear to what extent complex spike activity is implicated in generalized epilepsy. Purkinje cell complex spike firing is elicited by climbing fiber activation and reflects action potential firing in the inferior olive. Here, we investigated to what extent modulation of complex spike firing is reflected in the temporal patterns of seizures. Extracellular single-unit recordings in awake, head-restrained homozygous tottering mice, which suffer from a mutation in the voltage-gated CaV2.1 calcium channel, revealed that a substantial proportion of Purkinje cells (26%) showed increased complex spike activity and rhythmicity during GSWDs. Moreover, Purkinje cells, recorded either electrophysiologically or by using Ca2+-imaging, showed a significant increase in complex spike synchronicity for both adjacent and remote Purkinje cells during ictal events. These seizure-related changes in firing frequency, rhythmicity and synchronicity were most prominent in the lateral cerebellum, a region known to receive cerebral input via the inferior olive. These data indicate profound and widespread changes in olivary firing that are most likely induced by seizure-related activity changes in the thalamocortical network, thereby highlighting the possibility that olivary neurons can compensate for pathological brain-state changes by dampening oscillations. PMID:29163057

Heterogeneity induces spatiotemporal oscillations in reaction-diffusion systems

NASA Astrophysics Data System (ADS)

Krause, Andrew L.; Klika, Václav; Woolley, Thomas E.; Gaffney, Eamonn A.

2018-05-01

We report on an instability arising in activator-inhibitor reaction-diffusion (RD) systems with a simple spatial heterogeneity. This instability gives rise to periodic creation, translation, and destruction of spike solutions that are commonly formed due to Turing instabilities. While this behavior is oscillatory in nature, it occurs purely within the Turing space such that no region of the domain would give rise to a Hopf bifurcation for the homogeneous equilibrium. We use the shadow limit of the Gierer-Meinhardt system to show that the speed of spike movement can be predicted from well-known asymptotic theory, but that this theory is unable to explain the emergence of these spatiotemporal oscillations. Instead, we numerically explore this system and show that the oscillatory behavior is caused by the destabilization of a steady spike pattern due to the creation of a new spike arising from endogeneous activator production. We demonstrate that on the edge of this instability, the period of the oscillations goes to infinity, although it does not fit the profile of any well-known bifurcation of a limit cycle. We show that nearby stationary states are either Turing unstable or undergo saddle-node bifurcations near the onset of the oscillatory instability, suggesting that the periodic motion does not emerge from a local equilibrium. We demonstrate the robustness of this spatiotemporal oscillation by exploring small localized heterogeneity and showing that this behavior also occurs in the Schnakenberg RD model. Our results suggest that this phenomenon is ubiquitous in spatially heterogeneous RD systems, but that current tools, such as stability of spike solutions and shadow-limit asymptotics, do not elucidate understanding. This opens several avenues for further mathematical analysis and highlights difficulties in explaining how robust patterning emerges from Turing's mechanism in the presence of even small spatial heterogeneity.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

NASA Astrophysics Data System (ADS)

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

2018-02-01

Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

Nonlinear dynamics of a pulse-coupled neural oscillator model of orientation tuning in the visual cortex

NASA Astrophysics Data System (ADS)

Bressloff, P. C.; Bressloff, N. W.

2000-02-01

Orientation tuning in a ring of pulse-coupled integrate-and-fire (IF) neurons is analyzed in terms of spontaneous pattern formation. It is shown how the ring bifurcates from a synchronous state to a non-phase-locked state whose spike trains are characterized by quasiperiodic variations of the inter-spike intervals (ISIs) on closed invariant circles. The separation of these invariant circles in phase space results in a localized peak of activity as measured by the time-averaged firing rate of the neurons. This generates a sharp orientation tuning curve that can lock to a slowly rotating, weakly tuned external stimulus. For fast synapses, breakup of the quasiperiodic orbits occurs leading to high spike time variability suggestive of chaos.

An ultra-sparse code underliesthe generation of neural sequences in a songbird

NASA Astrophysics Data System (ADS)

Hahnloser, Richard H. R.; Kozhevnikov, Alexay A.; Fee, Michale S.

2002-09-01

Sequences of motor activity are encoded in many vertebrate brains by complex spatio-temporal patterns of neural activity; however, the neural circuit mechanisms underlying the generation of these pre-motor patterns are poorly understood. In songbirds, one prominent site of pre-motor activity is the forebrain robust nucleus of the archistriatum (RA), which generates stereotyped sequences of spike bursts during song and recapitulates these sequences during sleep. We show that the stereotyped sequences in RA are driven from nucleus HVC (high vocal centre), the principal pre-motor input to RA. Recordings of identified HVC neurons in sleeping and singing birds show that individual HVC neurons projecting onto RA neurons produce bursts sparsely, at a single, precise time during the RA sequence. These HVC neurons burst sequentially with respect to one another. We suggest that at each time in the RA sequence, the ensemble of active RA neurons is driven by a subpopulation of RA-projecting HVC neurons that is active only at that time. As a population, these HVC neurons may form an explicit representation of time in the sequence. Such a sparse representation, a temporal analogue of the `grandmother cell' concept for object recognition, eliminates the problem of temporal interference during sequence generation and learning attributed to more distributed representations.

The quantum needle of the avian magnetic compass

PubMed Central

Hiscock, Hamish G.; Worster, Susannah; Kattnig, Daniel R.; Steers, Charlotte; Jin, Ye; Manolopoulos, David E.; Mouritsen, Henrik; Hore, P. J.

2016-01-01

Migratory birds have a light-dependent magnetic compass, the mechanism of which is thought to involve radical pairs formed photochemically in cryptochrome proteins in the retina. Theoretical descriptions of this compass have thus far been unable to account for the high precision with which birds are able to detect the direction of the Earth's magnetic field. Here we use coherent spin dynamics simulations to explore the behavior of realistic models of cryptochrome-based radical pairs. We show that when the spin coherence persists for longer than a few microseconds, the output of the sensor contains a sharp feature, referred to as a spike. The spike arises from avoided crossings of the quantum mechanical spin energy-levels of radicals formed in cryptochromes. Such a feature could deliver a heading precision sufficient to explain the navigational behavior of migratory birds in the wild. Our results (i) afford new insights into radical pair magnetoreception, (ii) suggest ways in which the performance of the compass could have been optimized by evolution, (iii) may provide the beginnings of an explanation for the magnetic disorientation of migratory birds exposed to anthropogenic electromagnetic noise, and (iv) suggest that radical pair magnetoreception may be more of a quantum biology phenomenon than previously realized. PMID:27044102

Control of cerebellar granule cell output by sensory-evoked Golgi cell inhibition

PubMed Central

Duguid, Ian; Branco, Tiago; Chadderton, Paul; Arlt, Charlotte; Powell, Kate; Häusser, Michael

2015-01-01

Classical feed-forward inhibition involves an excitation–inhibition sequence that enhances the temporal precision of neuronal responses by narrowing the window for synaptic integration. In the input layer of the cerebellum, feed-forward inhibition is thought to preserve the temporal fidelity of granule cell spikes during mossy fiber stimulation. Although this classical feed-forward inhibitory circuit has been demonstrated in vitro, the extent to which inhibition shapes granule cell sensory responses in vivo remains unresolved. Here we combined whole-cell patch-clamp recordings in vivo and dynamic clamp recordings in vitro to directly assess the impact of Golgi cell inhibition on sensory information transmission in the granule cell layer of the cerebellum. We show that the majority of granule cells in Crus II of the cerebrocerebellum receive sensory-evoked phasic and spillover inhibition prior to mossy fiber excitation. This preceding inhibition reduces granule cell excitability and sensory-evoked spike precision, but enhances sensory response reproducibility across the granule cell population. Our findings suggest that neighboring granule cells and Golgi cells can receive segregated and functionally distinct mossy fiber inputs, enabling Golgi cells to regulate the size and reproducibility of sensory responses. PMID:26432880

Corollary discharge inhibition of wind-sensitive cercal giant interneurons in the singing field cricket

PubMed Central

Hedwig, Berthold

2014-01-01

Crickets carry wind-sensitive mechanoreceptors on their cerci, which, in response to the airflow produced by approaching predators, triggers escape reactions via ascending giant interneurons (GIs). Males also activate their cercal system by air currents generated due to the wing movements underlying sound production. Singing males still respond to external wind stimulation, but are not startled by the self-generated airflow. To investigate how the nervous system discriminates sensory responses to self-generated and external airflow, we intracellularly recorded wind-sensitive afferents and ventral GIs of the cercal escape pathway in fictively singing crickets, a situation lacking any self-stimulation. GI spiking was reduced whenever cercal wind stimulation coincided with singing motor activity. The axonal terminals of cercal afferents showed no indication of presynaptic inhibition during singing. In two ventral GIs, however, a corollary discharge inhibition occurred strictly in phase with the singing motor pattern. Paired intracellular recordings revealed that this inhibition was not mediated by the activity of the previously identified corollary discharge interneuron (CDI) that rhythmically inhibits the auditory pathway during singing. Cercal wind stimulation, however, reduced the spike activity of this CDI by postsynaptic inhibition. Our study reveals how precisely timed corollary discharge inhibition of ventral GIs can prevent self-generated airflow from triggering inadvertent escape responses in singing crickets. The results indicate that the responsiveness of the auditory and wind-sensitive pathway is modulated by distinct CDIs in singing crickets and that the corollary discharge inhibition in the auditory pathway can be attenuated by cercal wind stimulation. PMID:25318763

Locally Contractive Dynamics in Generalized Integrate-and-Fire Neurons*

PubMed Central

Jimenez, Nicolas D.; Mihalas, Stefan; Brown, Richard; Niebur, Ernst; Rubin, Jonathan

2013-01-01

Integrate-and-fire models of biological neurons combine differential equations with discrete spike events. In the simplest case, the reset of the neuronal voltage to its resting value is the only spike event. The response of such a model to constant input injection is limited to tonic spiking. We here study a generalized model in which two simple spike-induced currents are added. We show that this neuron exhibits not only tonic spiking at various frequencies but also the commonly observed neuronal bursting. Using analytical and numerical approaches, we show that this model can be reduced to a one-dimensional map of the adaptation variable and that this map is locally contractive over a broad set of parameter values. We derive a sufficient analytical condition on the parameters for the map to be globally contractive, in which case all orbits tend to a tonic spiking state determined by the fixed point of the return map. We then show that bursting is caused by a discontinuity in the return map, in which case the map is piecewise contractive. We perform a detailed analysis of a class of piecewise contractive maps that we call bursting maps and show that they robustly generate stable bursting behavior. To the best of our knowledge, this work is the first to point out the intimate connection between bursting dynamics and piecewise contractive maps. Finally, we discuss bifurcations in this return map, which cause transitions between spiking patterns. PMID:24489486

Noise-robust speech recognition through auditory feature detection and spike sequence decoding.

PubMed

Schafer, Phillip B; Jin, Dezhe Z

2014-03-01

Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.

Measurement of Sulfur Isotopic Composition (δ34S) by Multiple-Collector Thermal Ionization Mass Spectrometry (MC-TIMS) Using a 33S/36S Double Spike

NASA Astrophysics Data System (ADS)

Mann, J. L.; Kelly, W. R.

2006-05-01

A new analytical technique for the determination of δ34S will be described. The technique is based on the production of singularly charged arsenic sulfide molecular ions (AsS+) by thermal ionization using silica gel as an emitter and combines multiple-collector thermal ionization mass spectrometry (MC-TIMS) with a 33S/36S double spike to correct instrumental fractionation. Because the double spike is added to the sample before chemical processing, both the isotopic composition and sulfur concentration are measured simultaneously. The accuracy and precision of the double spike technique is comparable to or better than modern gas source mass spectrometry, but requires about a factor of 10 less sample. Δ33S effects can be determined directly in an unspiked sample without any assumptions about the value of k (mass dependent fractionation factor) which is currently required by gas source mass spectrometry. Three international sulfur standards (IAEA-S-1, IAEA-S-2, and IAEA-S-3) were measured to evaluate the precision and accuracy of the new technique and to evaluate the consensus values for these standards. Two different double spike preparations were used. The δ34S values (reported relative to Vienna Canyon Diablo Troilite (VCDT), (δ34S (‰) = 34S/32S)sample/(34S/32S)VCDT - 1) x 1000]), 34S/32SVCDT = 0.0441626) determined were -0.32‰ ± 0.04‰ (1σ, n=4) and -0.31‰ ± 0.13‰ (1σ, n=8) for IAEA-S-1, 22.65‰ ± 0.04‰ (1σ, n=7) and 22.60‰ ± 0.06‰ (1σ, n=5) for IAEA- S-2, and -32.47‰ ± 0.07‰ (1σ, n=8) for IAEA-S-3. The amount of natural sample used for these analyses ranged from 0.40 μmoles to 2.35 μmoles. Each standard showed less than 0.5‰ variability (IAEA-S-1 < 0.4‰, IAEA-S-2 < 0.2‰, and IAEA-S-3 < 0.2‰). Our values for S-1 and S-2 are in excellent agreement with the consensus values and the values reported by other laboratories using both SF6 and SO2. Our value for S-3 differs statistically from the Institute for Reference Materials and Measurement (IRMM) value and is slightly lower than the currently accepted consensus value (-32.3). Because the technique is based on thermal ionization of AsS+, and As is mononuclidic, corrections for interferences or for scale contraction/expansion are not required. The availability of MC-TIMS instruments in laboratories around the world makes this technique immediately available to a much larger scientific community who require highly accurate and precise measurements of sulfur.

Two Coincidence Detectors for Spike Timing-Dependent Plasticity in Somatosensory Cortex

PubMed Central

Bender, Vanessa A.; Bender, Kevin J.; Brasier, Daniel J.; Feldman, Daniel E.

2011-01-01

Many cortical synapses exhibit spike timing-dependent plasticity (STDP) in which the precise timing of presynaptic and postsynaptic spikes induces synaptic strengthening [long-term potentiation (LTP)] or weakening [long-term depression (LTD)]. Standard models posit a single, postsynaptic, NMDA receptor-based coincidence detector for LTP and LTD components of STDP. We show instead that STDP at layer 4 to layer 2/3 synapses in somatosensory (S1) cortex involves separate calcium sources and coincidence detection mechanisms for LTP and LTD. LTP showed classical NMDA receptor dependence. LTD was independent of postsynaptic NMDA receptors and instead required group I metabotropic glutamate receptors and calcium from voltage-sensitive channels and IP3 receptor-gated stores. Downstream of postsynaptic calcium, LTD required retrograde endocannabinoid signaling, leading to presynaptic LTD expression, and also required activation of apparently presynaptic NMDA receptors. These LTP and LTD mechanisms detected firing coincidence on ~25 and ~125 ms time scales, respectively, and combined to implement the overall STDP rule. These findings indicate that STDP is not a unitary process and suggest that endocannabinoid-dependent LTD may be relevant to cortical map plasticity. PMID:16624937

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems.

PubMed

Shehzad, Danish; Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

PubMed Central

Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models. PMID:27413363

Determination of methyl mercury by aqueous phase Eehylation, followed by gas chromatographic separation with cold vapor atomic fluorescence detection

USGS Publications Warehouse

De Wild, John F.; Olsen, Mark L.; Olund, Shane D.

2002-01-01

A recent national sampling of streams in the United States revealed low methyl mercury concentrations in surface waters. The resulting median and mean concentrations, calculated from 104 samples, were 0.06 nanograms per liter (ng/L) and 0.15 ng/L, respectively. This level of methyl mercury in surface water in the United States has created a need for analytical techniques capable of detecting sub-nanogram per liter concentrations. In an attempt to create a U.S. Geological Survey approved method, the Wisconsin District Mercury Laboratory has adapted a distillation/ethylation/ gas-phase separation method with cold vapor atomic fluorescence spectroscopy detection for the determination of methyl mercury in filtered and unfiltered waters. This method is described in this report. Based on multiple analyses of surface water and ground-water samples, a method detection limit of 0.04 ng/L was established. Precision and accuracy were evaluated for the method using both spiked and unspiked ground-water and surface-water samples. The percent relative standard deviations ranged from 10.2 to 15.6 for all analyses at all concentrations. Average recoveries obtained for the spiked matrices ranged from 88.8 to 117 percent. The precision and accuracy ranges are within the acceptable method-performance limits. Considering the demonstrated detection limit, precision, and accuracy, the method is an effective means to quantify methyl mercury in waters at or below environmentally relevant concentrations

Removal of spike frequency adaptation via neuromodulation intrinsic to the Tritonia escape swim central pattern generator.

PubMed

Katz, P S; Frost, W N

1997-10-15

For the mollusc Tritonia diomedea to generate its escape swim motor pattern, interneuron C2, a crucial member of the central pattern generator (CPG) for this rhythmic behavior, must fire repetitive bursts of action potentials. Yet, before swimming, repeated depolarizing current pulses injected into C2 at periods similar those in the swim motor program are incapable of mimicking the firing rate attained by C2 on each cycle of a swim motor program. This resting level of C2 inexcitability is attributable to its own inherent spike frequency adaptation (SFA). Clearly, this property must be altered for the swim behavior to occur. The pathway for initiation of the swimming behavior involves activation of the serotonergic dorsal swim interneurons (DSIs), which are also intrinsic members of the swim CPG. Physiologically appropriate DSI stimulation transiently decreases C2 SFA, allowing C2 to fire at higher rates even when repeatedly depolarized at short intervals. The increased C2 excitability caused by DSI stimulation is mimicked and occluded by serotonin application. Furthermore, the change in excitability is not caused by the depolarization associated with DSI stimulation or serotonin application but is correlated with a decrease in C2 spike afterhyperpolarization. This suggests that the DSIs use serotonin to evoke a neuromodulatory action on a conductance in C2 that regulates its firing rate. This modulatory action of one CPG neuron on another is likely to play a role in configuring the swim circuit into its rhythmic pattern-generating mode and maintaining it in that state.

Bioaccumulation and biotransformation of arsenic compounds in Hediste diversicolor (Muller 1776) after exposure to spiked sediments.

PubMed

Gaion, Andrea; Sartori, Davide; Scuderi, Alice; Fattorini, Daniele

2014-05-01

This study focused on the exposure of the common ragworm Hediste diversicolor (Müller 1776) to sediments enriched with different arsenic compounds, namely arsenate, dimethyl-arsinate, and arsenobetaine. Speciation analysis was carried out on both the spiked sediments and the exposed polychaetes in order to investigate H. diversicolor capability of arsenic bioaccumulation and biotransformation. Two levels of contamination (acute and moderate dose) were chosen for enriched sediments to investigate possible differences in the arsenic bioaccumulation patterns. The highest value of arsenic in tissues was reached after 15 days of exposure to dimethyl-arsinate (acute dose) spiked sediment (1,172 ± 176 μg/g). A significant increase was also obtained in worms exposed both to arsenate and arsenobetaine. Speciation analysis showed that trimethyl-arsine oxide was the predominant chemical form in tissues of H. diversicolor exposed to all the spiked sediments, confirming the importance of this intermediate in biological transformation of arsenic.

Sporting Miscues. Part Two.

ERIC Educational Resources Information Center

Adrian, Marlene; House, Gale

1987-01-01

Six common sporting miscues are examined and analyzed for their meanings and ramifications. The miscues involve accurate basketball and volleyball shots; overarm patterns; volleyball spikes; softball pitching; and basketball defense moves. (CB)

Irregular behavior in an excitatory-inhibitory neuronal network

NASA Astrophysics Data System (ADS)

Park, Choongseok; Terman, David

2010-06-01

Excitatory-inhibitory networks arise in many regions throughout the central nervous system and display complex spatiotemporal firing patterns. These neuronal activity patterns (of individual neurons and/or the whole network) are closely related to the functional status of the system and differ between normal and pathological states. For example, neurons within the basal ganglia, a group of subcortical nuclei that are responsible for the generation of movement, display a variety of dynamic behaviors such as correlated oscillatory activity and irregular, uncorrelated spiking. Neither the origins of these firing patterns nor the mechanisms that underlie the patterns are well understood. We consider a biophysical model of an excitatory-inhibitory network in the basal ganglia and explore how specific biophysical properties of the network contribute to the generation of irregular spiking. We use geometric dynamical systems and singular perturbation methods to systematically reduce the model to a simpler set of equations, which is suitable for analysis. The results specify the dependence on the strengths of synaptic connections and the intrinsic firing properties of the cells in the irregular regime when applied to the subthalamopallidal network of the basal ganglia.

New aspects of firing pattern autocontrol in oxytocin and vasopressin neurones.

PubMed

Moos, F; Gouzènes, L; Brown, D; Dayanithi, G; Sabatier, N; Boissin, L; Rabié, A; Richard, P

1998-01-01

In the rat, oxytocin (OT) and vasopressin (AVP) neurones exhibit specific electrical activities which are controlled by OT and AVP released from soma and dendrites within the magnocellular hypothalamic nuclei. OT enhances amplitude and frequency of suckling-induced bursts, and changes basal firing characteristics: spike patterning becomes very irregular (spike clusters separated by long silences), firing rate is highly variable, oscillating before facilitated bursts. This unstable behaviour which markedly decreases during hyperosmotic stimulation (interrupting bursting) could be a prerequisite for bursting. The effects of AVP depend on the initial phasic pattern of AVP neurones: AVP excites weakly active neurones (increasing burst duration, decreasing silences) and inhibits highly active neurones; neurones with intermediate phasic activity are unaffected. Thus, AVP ensures all AVP neurones discharge with moderate phasic activity (bursts and silences lasting 20-40 s), known to optimise systemic AVP release. V1a-type receptors are involved in AVP actions. In conclusion, OT and AVP control their respective neurones in a complex manner to favour the patterns of activity which are the best suited for an efficient systemic hormone release.

Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons

PubMed Central

Müller, Jan; Bakkum, Douglas J.; Hierlemann, Andreas

2012-01-01

We present a system to artificially correlate the spike timing between sets of arbitrary neurons that were interfaced to a complementary metal–oxide–semiconductor (CMOS) high-density microelectrode array (MEA). The system features a novel reprogrammable and flexible event engine unit to detect arbitrary spatio-temporal patterns of recorded action potentials and is capable of delivering sub-millisecond closed-loop feedback of electrical stimulation upon trigger events in real-time. The relative timing between action potentials of individual neurons as well as the temporal pattern among multiple neurons, or neuronal assemblies, is considered an important factor governing memory and learning in the brain. Artificially changing timings between arbitrary sets of spiking neurons with our system could provide a “knob” to tune information processing in the network. PMID:23335887

Analysis of spike-wave discharges in rats using discrete wavelet transform.

PubMed

Ubeyli, Elif Derya; Ilbay, Gül; Sahin, Deniz; Ateş, Nurbay

2009-03-01

A feature is a distinctive or characteristic measurement, transform, structural component extracted from a segment of a pattern. Features are used to represent patterns with the goal of minimizing the loss of important information. The discrete wavelet transform (DWT) as a feature extraction method was used in representing the spike-wave discharges (SWDs) records of Wistar Albino Glaxo/Rijswijk (WAG/Rij) rats. The SWD records of WAG/Rij rats were decomposed into time-frequency representations using the DWT and the statistical features were calculated to depict their distribution. The obtained wavelet coefficients were used to identify characteristics of the signal that were not apparent from the original time domain signal. The present study demonstrates that the wavelet coefficients are useful in determining the dynamics in the time-frequency domain of SWD records.

Genetic threshold hypothesis of neocortical spike-and-wave discharges in the rat: An animal model of petit mal epilepsy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vadasz, C.; Fleischer, A.; Carpi, D.

1995-02-27

Neocortical high-voltage spike-and-wave discharges (HVS) in the rat are an animal model of petit mal epilepsy. Genetic analysis of total duration of HVS (s/12 hr) in reciprocal F1 and F2 hybrids of F344 and BN rats indicated that the phenotypic variability of HVS cannot be explained by simple, monogenic Mendelian model. Biometrical analysis suggested the presence of additive, dominance, and sex-linked-epistatic effects, buffering maternal influence, and heterosis. High correlation was observed between average duration (s/episode) and frequency of occurrence of spike-and-wave episodes (n/12 hr) in parental and segregating generations, indicating that common genes affect both duration and frequency of themore » spike-and-wave pattern. We propose that both genetic and developmental - environmental factors control an underlying quantitative variable, which, above a certain threshold level, precipitates HVS discharges. These findings, together with the recent availability of rat DNA markers for total genome mapping, pave the way to the identification of genes that control the susceptibility of the brain to spike-and-wave discharges. 67 refs., 3 figs., 5 tabs.« less

Uncovering representations of sleep-associated hippocampal ensemble spike activity

NASA Astrophysics Data System (ADS)

Chen, Zhe; Grosmark, Andres D.; Penagos, Hector; Wilson, Matthew A.

2016-08-01

Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.

Comparing Realistic Subthalamic Nucleus Neuron Models

NASA Astrophysics Data System (ADS)

Njap, Felix; Claussen, Jens C.; Moser, Andreas; Hofmann, Ulrich G.

2011-06-01

The mechanism of action of clinically effective electrical high frequency stimulation is still under debate. However, recent evidence points at the specific activation of GABA-ergic ion channels. Using a computational approach, we analyze temporal properties of the spike trains emitted by biologically realistic neurons of the subthalamic nucleus (STN) as a function of GABA-ergic synaptic input conductances. Our contribution is based on a model proposed by Rubin and Terman and exhibits a wide variety of different firing patterns, silent, low spiking, moderate spiking and intense spiking activity. We observed that most of the cells in our network turn to silent mode when we increase the GABAA input conductance above the threshold of 3.75 mS/cm2. On the other hand, insignificant changes in firing activity are observed when the input conductance is low or close to zero. We thus reproduce Rubin's model with vanishing synaptic conductances. To quantitatively compare spike trains from the original model with the modified model at different conductance levels, we apply four different (dis)similarity measures between them. We observe that Mahalanobis distance, Victor-Purpura metric, and Interspike Interval distribution are sensitive to different firing regimes, whereas Mutual Information seems undiscriminative for these functional changes.

Calculation of precise firing statistics in a neural network model

NASA Astrophysics Data System (ADS)

Cho, Myoung Won

2017-08-01

A precise prediction of neural firing dynamics is requisite to understand the function of and the learning process in a biological neural network which works depending on exact spike timings. Basically, the prediction of firing statistics is a delicate manybody problem because the firing probability of a neuron at a time is determined by the summation over all effects from past firing states. A neural network model with the Feynman path integral formulation is recently introduced. In this paper, we present several methods to calculate firing statistics in the model. We apply the methods to some cases and compare the theoretical predictions with simulation results.

Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs.

PubMed

Jonke, Zeno; Legenstein, Robert; Habenschuss, Stefan; Maass, Wolfgang

2017-08-30

Cortical microcircuits are very complex networks, but they are composed of a relatively small number of stereotypical motifs. Hence, one strategy for throwing light on the computational function of cortical microcircuits is to analyze emergent computational properties of these stereotypical microcircuit motifs. We are addressing here the question how spike timing-dependent plasticity shapes the computational properties of one motif that has frequently been studied experimentally: interconnected populations of pyramidal cells and parvalbumin-positive inhibitory cells in layer 2/3. Experimental studies suggest that these inhibitory neurons exert some form of divisive inhibition on the pyramidal cells. We show that this data-based form of feedback inhibition, which is softer than that of winner-take-all models that are commonly considered in theoretical analyses, contributes to the emergence of an important computational function through spike timing-dependent plasticity: The capability to disentangle superimposed firing patterns in upstream networks, and to represent their information content through a sparse assembly code. SIGNIFICANCE STATEMENT We analyze emergent computational properties of a ubiquitous cortical microcircuit motif: populations of pyramidal cells that are densely interconnected with inhibitory neurons. Simulations of this model predict that sparse assembly codes emerge in this microcircuit motif under spike timing-dependent plasticity. Furthermore, we show that different assemblies will represent different hidden sources of upstream firing activity. Hence, we propose that spike timing-dependent plasticity enables this microcircuit motif to perform a fundamental computational operation on neural activity patterns. Copyright © 2017 the authors 0270-6474/17/378511-13$15.00/0.

Nonlinear Dynamic Modeling of Neuron Action Potential Threshold During Synaptically Driven Broadband Intracellular Activity

PubMed Central

Roach, Shane M.; Song, Dong; Berger, Theodore W.

2012-01-01

Activity-dependent variation of neuronal thresholds for action potential (AP) generation is one of the key determinants of spike-train temporal-pattern transformations from presynaptic to postsynaptic spike trains. In this study, we model the nonlinear dynamics of the threshold variation during synaptically driven broadband intracellular activity. First, membrane potentials of single CA1 pyramidal cells were recorded under physiologically plausible broadband stimulation conditions. Second, a method was developed to measure AP thresholds from the continuous recordings of membrane potentials. It involves measuring the turning points of APs by analyzing the third-order derivatives of the membrane potentials. Four stimulation paradigms with different temporal patterns were applied to validate this method by comparing the measured AP turning points and the actual AP thresholds estimated with varying stimulation intensities. Results show that the AP turning points provide consistent measurement of the AP thresholds, except for a constant offset. It indicates that 1) the variation of AP turning points represents the nonlinearities of threshold dynamics; and 2) an optimization of the constant offset is required to achieve accurate spike prediction. Third, a nonlinear dynamical third-order Volterra model was built to describe the relations between the threshold dynamics and the AP activities. Results show that the model can predict threshold accurately based on the preceding APs. Finally, the dynamic threshold model was integrated into a previously developed single neuron model and resulted in a 33% improvement in spike prediction. PMID:22156947

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

PubMed Central

Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

2016-01-01

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

Burst firing and modulation of functional connectivity in cat striate cortex.

PubMed

Snider, R K; Kabara, J F; Roig, B R; Bonds, A B

1998-08-01

We studied the influences of the temporal firing patterns of presynaptic cat visual cortical cells on spike generation by postsynaptic cells. Multiunit recordings were dissected into the activity of individual neurons within the recorded group. Cross-correlation analysis was then used to identify directly coupled neuron pairs. The 22 multiunit groups recorded typically showed activity from two to six neurons, each containing between 1 and 15 neuron pairs. From a total of 241 neuron pairs, 91 (38%) had a shifted cross-correlation peak, which indicated a possible direct connection. Only two multiunit groups contained no shifted peaks. Burst activity, defined by groups of two or more spikes with intervals of

Complex transitions between spike, burst or chaos synchronization states in coupled neurons with coexisting bursting patterns

NASA Astrophysics Data System (ADS)

Gu, Hua-Guang; Chen, Sheng-Gen; Li, Yu-Ye

2015-05-01

We investigated the synchronization dynamics of a coupled neuronal system composed of two identical Chay model neurons. The Chay model showed coexisting period-1 and period-2 bursting patterns as a parameter and initial values are varied. We simulated multiple periodic and chaotic bursting patterns with non-(NS), burst phase (BS), spike phase (SS), complete (CS), and lag synchronization states. When the coexisting behavior is near period-2 bursting, the transitions of synchronization states of the coupled system follows very complex transitions that begins with transitions between BS and SS, moves to transitions between CS and SS, and to CS. Most initial values lead to the CS state of period-2 bursting while only a few lead to the CS state of period-1 bursting. When the coexisting behavior is near period-1 bursting, the transitions begin with NS, move to transitions between SS and BS, to transitions between SS and CS, and then to CS. Most initial values lead to the CS state of period-1 bursting but a few lead to the CS state of period-2 bursting. The BS was identified as chaos synchronization. The patterns for NS and transitions between BS and SS are insensitive to initial values. The patterns for transitions between CS and SS and the CS state are sensitive to them. The number of spikes per burst of non-CS bursting increases with increasing coupling strength. These results not only reveal the initial value- and parameter-dependent synchronization transitions of coupled systems with coexisting behaviors, but also facilitate interpretation of various bursting patterns and synchronization transitions generated in the nervous system with weak coupling strength. Project supported by the National Natural Science Foundation of China (Grant Nos. 11372224 and 11402039) and the Fundamental Research Funds for Central Universities designated to Tongji University (Grant No. 1330219127).

A forecast-based STDP rule suitable for neuromorphic implementation.

PubMed

Davies, S; Galluppi, F; Rast, A D; Furber, S B

2012-08-01

Artificial neural networks increasingly involve spiking dynamics to permit greater computational efficiency. This becomes especially attractive for on-chip implementation using dedicated neuromorphic hardware. However, both spiking neural networks and neuromorphic hardware have historically found difficulties in implementing efficient, effective learning rules. The best-known spiking neural network learning paradigm is Spike Timing Dependent Plasticity (STDP) which adjusts the strength of a connection in response to the time difference between the pre- and post-synaptic spikes. Approaches that relate learning features to the membrane potential of the post-synaptic neuron have emerged as possible alternatives to the more common STDP rule, with various implementations and approximations. Here we use a new type of neuromorphic hardware, SpiNNaker, which represents the flexible "neuromimetic" architecture, to demonstrate a new approach to this problem. Based on the standard STDP algorithm with modifications and approximations, a new rule, called STDP TTS (Time-To-Spike) relates the membrane potential with the Long Term Potentiation (LTP) part of the basic STDP rule. Meanwhile, we use the standard STDP rule for the Long Term Depression (LTD) part of the algorithm. We show that on the basis of the membrane potential it is possible to make a statistical prediction of the time needed by the neuron to reach the threshold, and therefore the LTP part of the STDP algorithm can be triggered when the neuron receives a spike. In our system these approximations allow efficient memory access, reducing the overall computational time and the memory bandwidth required. The improvements here presented are significant for real-time applications such as the ones for which the SpiNNaker system has been designed. We present simulation results that show the efficacy of this algorithm using one or more input patterns repeated over the whole time of the simulation. On-chip results show that the STDP TTS algorithm allows the neural network to adapt and detect the incoming pattern with improvements both in the reliability of, and the time required for, consistent output. Through the approximations we suggest in this paper, we introduce a learning rule that is easy to implement both in event-driven simulators and in dedicated hardware, reducing computational complexity relative to the standard STDP rule. Such a rule offers a promising solution, complementary to standard STDP evaluation algorithms, for real-time learning using spiking neural networks in time-critical applications. Copyright © 2012 Elsevier Ltd. All rights reserved.

Small heat-shock proteins and leaf cooling capacity account for the unusual heat tolerance of the central spike leaves in Agave tequilana var. Weber.

PubMed

Luján, Rosario; Lledías, Fernando; Martínez, Luz María; Barreto, Rita; Cassab, Gladys I; Nieto-Sotelo, Jorge

2009-12-01

Agaves are perennial crassulacean acid metabolism (CAM) plants distributed in tropical and subtropical arid environments, features that are attractive for studying the heat-shock response. In agaves, the stress response can be analysed easily during leaf development, as they form a spirally shaped rosette, having the meristem surrounded by folded leaves in the centre (spike) and the unfolded and more mature leaves in the periphery. Here, we report that the spike of Agave tequilana is the most thermotolerant part of the rosette withstanding shocks of up to 55 degrees C. This finding was inconsistent with the patterns of heat-shock protein (Hsp) gene expression, as maximal accumulation of Hsp transcripts was at 44 degrees C in all sectors (spike, inner, middle and outer). However, levels of small HSP (sHSP)-CI and sHSP-CII proteins were conspicuously higher in spike leaves at all temperatures correlating with their thermotolerance. In addition, spike leaves showed a higher stomatal density and abated more efficiently their temperature several degrees below that of air. We propose that the greater capacity for leaf cooling during the day in response to heat stress, and the elevated levels of sHSPs, constitute part of a set of strategies that protect the SAM and folded leaves of A. tequilana from high temperatures.

Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study.

PubMed

Garcia-Garcia, Martha G; Bergquist, Austin J; Vargas-Perez, Hector; Nagai, Mary K; Zariffa, Jose; Marquez-Chin, Cesar; Popovic, Milos R

2017-11-01

Firing rates of single cortical neurons can be volitionally modulated through biofeedback (i.e. operant conditioning), and this information can be transformed to control external devices (i.e. brain-machine interfaces; BMIs). However, not all neurons respond to operant conditioning in BMI implementation. Establishing criteria that predict neuron utility will assist translation of BMI research to clinical applications. Single cortical neurons (n=7) were recorded extracellularly from primary motor cortex of a Long-Evans rat. Recordings were incorporated into a BMI involving up-regulation of firing rate to control the brightness of a light-emitting-diode and subsequent reward. Neurons were classified as 'fast-spiking', 'bursting' or 'regular-spiking' according to waveform-width and intrinsic firing patterns. Fast-spiking and bursting neurons were found to up-regulate firing rate by a factor of 2.43±1.16, demonstrating high utility, while regular-spiking neurons decreased firing rates on average by a factor of 0.73±0.23, demonstrating low utility. The ability to select neurons with high utility will be important to minimize training times and maximize information yield in future clinical BMI applications. The highly contrasting utility observed between fast-spiking and bursting neurons versus regular-spiking neurons allows for the hypothesis to be advanced that intrinsic electrophysiological properties may be useful criteria that predict neuron utility in BMI implementation.

Removal of polycyclic aromatic hydrocarbons in soil spiked with model mixtures of petroleum hydrocarbons and heterocycles using biosurfactants from Rhodococcus ruber IEGM 231.

PubMed

Ivshina, Irina; Kostina, Ludmila; Krivoruchko, Anastasiya; Kuyukina, Maria; Peshkur, Tatyana; Anderson, Peter; Cunningham, Colin

2016-07-15

Removal of polycyclic aromatic hydrocarbons (PAHs) in soil using biosurfactants (BS) produced by Rhodococcus ruber IEGM 231 was studied in soil columns spiked with model mixtures of major petroleum constituents. A crystalline mixture of single PAHs (0.63g/kg), a crystalline mixture of PAHs (0.63g/kg) and polycyclic aromatic sulfur heterocycles (PASHs), and an artificially synthesized non-aqueous phase liquid (NAPL) containing PAHs (3.00g/kg) dissolved in alkanes C10-C19 were used for spiking. Percentage of PAH removal with BS varied from 16 to 69%. Washing activities of BS were 2.5 times greater than those of synthetic surfactant Tween 60 in NAPL-spiked soil and similar to Tween 60 in crystalline-spiked soil. At the same time, amounts of removed PAHs were equal and consisted of 0.3-0.5g/kg dry soil regardless the chemical pattern of a model mixture of petroleum hydrocarbons and heterocycles used for spiking. UV spectra for soil before and after BS treatment were obtained and their applicability for differentiated analysis of PAH and PASH concentration changes in remediated soil was shown. The ratios A254nm/A288nm revealed that BS increased biotreatability of PAH-contaminated soils. Copyright © 2016 Elsevier B.V. All rights reserved.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule.

PubMed

Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

2015-11-01

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

Spatiotemporal Mapping of Interictal Spike Propagation: A Novel Methodology Applied to Pediatric Intracranial EEG Recordings

PubMed Central

Tomlinson, Samuel B.; Bermudez, Camilo; Conley, Chiara; Brown, Merritt W.; Porter, Brenda E.; Marsh, Eric D.

2016-01-01

Synchronized cortical activity is implicated in both normative cognitive functioning and many neurologic disorders. For epilepsy patients with intractable seizures, irregular synchronization within the epileptogenic zone (EZ) is believed to provide the network substrate through which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical for detecting seizure networks in order to achieve postsurgical seizure control. However, automated techniques for characterizing epileptic networks have yet to gain traction in the clinical setting. Recent advances in signal processing and spike detection have made it possible to examine the spatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, we present a novel methodology for detecting, extracting, and visualizing spike propagation and demonstrate its potential utility as a biomarker for the EZ. Eighteen presurgical intracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable (i.e., seizure-free, n = 9) or unfavorable (i.e., seizure-persistent, n = 9) surgical outcomes. Novel algorithms were applied to extract multichannel spike discharges and visualize their spatiotemporal propagation. Quantitative analysis of spike propagation was performed using trajectory clustering and spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed an increase in trajectory organization (i.e., spatial autocorrelation) among Sz-Free patients compared with Sz-Persist patients. The pathophysiological basis and clinical implications of these findings are considered. PMID:28066315

Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring.

PubMed

Zibrandtsen, I C; Kidmose, P; Christensen, C B; Kjaer, T W

2017-12-01

Ear-EEG is recording of electroencephalography from a small device in the ear. This is the first study to compare ictal and interictal abnormalities recorded with ear-EEG and simultaneous scalp-EEG in an epilepsy monitoring unit. We recorded and compared simultaneous ear-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal spikes. There were no differences in sensitivity or specificity for seizure detection. Mean correlation coefficient between ear-EEG and nearest scalp electrode was above 0.6 with a statistically significant decreasing trend with increasing distance away from the ear. Ictal morphology and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance. Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe seizures. Interictal spike morphology from sufficiently large temporal spike sources can be sampled using ear-EEG. Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Hui; Song, Yongduan; Xue, Fangzheng

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than themore » SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.« less

Method for the determination of catechin and epicatechin enantiomers in cocoa-based ingredients and products by high-performance liquid chromatography: single-laboratory validation.

PubMed

Machonis, Philip R; Jones, Matthew A; Schaneberg, Brian T; Kwik-Uribe, Catherine L

2012-01-01

A single-laboratory validation study was performed for an HPLC method to identify and quantify the flavanol enantiomers (+)- and (-)-epicatechin and (+)- and (-)-catechin in cocoa-based ingredients and products. These compounds were eluted isocratically with an ammonium acetate-methanol mobile phase applied to a modified beta-cyclodextrin chiral stationary phase and detected using fluorescence. Spike recovery experiments using appropriate matrix blanks, along with cocoa extract, cocoa powder, and dark chocolate, were used to evaluate accuracy, repeatability, specificity, LOD, LOQ, and linearity of the method as performed by a single analyst on multiple days. In all samples analyzed, (-)-epicatechin was the predominant flavanol and represented 68-91% of the total monomeric flavanols detected. For the cocoa-based products, within-day (intraday) precision for (-)-epicatechin was between 1.46-3.22%, for (+)-catechin between 3.66-6.90%, and for (-)-catechin between 1.69-6.89%; (+)-epicatechin was not detected in these samples. Recoveries for the three sample types investigated ranged from 82.2 to 102.1% at the 50% spiking level, 83.7 to 102.0% at the 100% spiking level, and 80.4 to 101.1% at the 200% spiking level. Based on performance results, this method may be suitable for routine laboratory use in analysis of cocoa-based ingredients and products.

Dynamical model of long-term synaptic plasticity

PubMed Central

Abarbanel, Henry D. I.; Huerta, R.; Rabinovich, M. I.

2002-01-01

Long-term synaptic plasticity leading to enhancement in synaptic efficacy (long-term potentiation, LTP) or decrease in synaptic efficacy (long-term depression, LTD) is widely regarded as underlying learning and memory in nervous systems. LTP and LTD at excitatory neuronal synapses are observed to be induced by precise timing of pre- and postsynaptic events. Modification of synaptic transmission in long-term plasticity is a complex process involving many pathways; for example, it is also known that both forms of synaptic plasticity can be induced by various time courses of Ca2+ introduction into the postsynaptic cell. We present a phenomenological description of a two-component process for synaptic plasticity. Our dynamical model reproduces the spike time-dependent plasticity of excitatory synapses as a function of relative timing between pre- and postsynaptic events, as observed in recent experiments. The model accounts for LTP and LTD when the postsynaptic cell is voltage clamped and depolarized (LTP) or hyperpolarized (LTD) and no postsynaptic action potentials are evoked. We are also able to connect our model with the Bienenstock, Cooper, and Munro rule. We give model predictions for changes in synaptic strength when periodic spike trains of varying frequency and Poisson distributed spike trains with varying average frequency are presented pre- and postsynaptically. When the frequency of spike presentation exceeds ≈30–40 Hz, only LTP is induced. PMID:12114531

Infinite Systems of Interacting Chains with Memory of Variable Length—A Stochastic Model for Biological Neural Nets

NASA Astrophysics Data System (ADS)

Galves, A.; Löcherbach, E.

2013-06-01

We consider a new class of non Markovian processes with a countable number of interacting components. At each time unit, each component can take two values, indicating if it has a spike or not at this precise moment. The system evolves as follows. For each component, the probability of having a spike at the next time unit depends on the entire time evolution of the system after the last spike time of the component. This class of systems extends in a non trivial way both the interacting particle systems, which are Markovian (Spitzer in Adv. Math. 5:246-290, 1970) and the stochastic chains with memory of variable length which have finite state space (Rissanen in IEEE Trans. Inf. Theory 29(5):656-664, 1983). These features make it suitable to describe the time evolution of biological neural systems. We construct a stationary version of the process by using a probabilistic tool which is a Kalikow-type decomposition either in random environment or in space-time. This construction implies uniqueness of the stationary process. Finally we consider the case where the interactions between components are given by a critical directed Erdös-Rényi-type random graph with a large but finite number of components. In this framework we obtain an explicit upper-bound for the correlation between successive inter-spike intervals which is compatible with previous empirical findings.

Enhanced polychronization in a spiking network with metaplasticity.

PubMed

Guise, Mira; Knott, Alistair; Benuskova, Lubica

2015-01-01

Computational models of metaplasticity have usually focused on the modeling of single synapses (Shouval et al., 2002). In this paper we study the effect of metaplasticity on network behavior. Our guiding assumption is that the primary purpose of metaplasticity is to regulate synaptic plasticity, by increasing it when input is low and decreasing it when input is high. For our experiments we adopt a model of metaplasticity that demonstrably has this effect for a single synapse; our primary interest is in how metaplasticity thus defined affects network-level phenomena. We focus on a network-level phenomenon called polychronicity, that has a potential role in representation and memory. A network with polychronicity has the ability to produce non-synchronous but precisely timed sequences of neural firing events that can arise from strongly connected groups of neurons called polychronous neural groups (Izhikevich et al., 2004). Polychronous groups (PNGs) develop readily when spiking networks are exposed to repeated spatio-temporal stimuli under the influence of spike-timing-dependent plasticity (STDP), but are sensitive to changes in synaptic weight distribution. We use a technique we have recently developed called Response Fingerprinting to show that PNGs formed in the presence of metaplasticity are significantly larger than those with no metaplasticity. A potential mechanism for this enhancement is proposed that links an inherent property of integrator type neurons called spike latency to an increase in the tolerance of PNG neurons to jitter in their inputs.

Rapid disinhibition by adjustment of PV intrinsic excitability during whisker map plasticity in mouse S1.

PubMed

Gainey, Melanie A; Aman, Joseph W; Feldman, Daniel E

2018-04-20

Rapid plasticity of layer (L) 2/3 inhibitory circuits is an early step in sensory cortical map plasticity, but its cellular basis is unclear. We show that, in mice of either sex, 1 day whisker deprivation drives rapid loss of L4-evoked feedforward inhibition and more modest loss of feedforward excitation in L2/3 pyramidal (PYR) cells, increasing E-I conductance ratio. Rapid disinhibition was due to reduced L4-evoked spiking by L2/3 parvalbumin (PV) interneurons, caused by reduced PV intrinsic excitability. This included elevated PV spike threshold, associated with an increase in low-threshold, voltage activated delayed rectifier (presumed Kv1) and A-type potassium currents. Excitatory synaptic input and unitary inhibitory output of PV cells were unaffected. Functionally, the loss of feedforward inhibition and excitation were precisely coordinated in L2/3 PYR cells, so that peak feedforward synaptic depolarization remained stable. Thus, rapid plasticity of PV intrinsic excitability offsets early weakening of excitatory circuits to homeostatically stabilize synaptic potentials in PYR cells of sensory cortex. SIGNIFICANCE STATEMENT Inhibitory circuits in cerebral cortex are highly plastic, but the cellular mechanisms and functional importance of this plasticity are incompletely understood. We show that brief (1-day) sensory deprivation rapidly weakens parvalbumin (PV) inhibitory circuits by reducing the intrinsic excitability of PV neurons. This involved a rapid increase in voltage-gated potassium conductances that control near-threshold spiking excitability. Functionally, the loss of PV-mediated feedforward inhibition in L2/3 pyramidal cells was precisely balanced with the separate loss of feedforward excitation, resulting in a net homeostatic stabilization of synaptic potentials. Thus, rapid plasticity of PV intrinsic excitability implements network-level homeostasis to stabilize synaptic potentials in sensory cortex. Copyright © 2018 the authors.

Bipolar volcanic events in ice cores and the Toba eruption at 74 ka BP (Invited)

NASA Astrophysics Data System (ADS)

Svensson, A.

2013-12-01

Acidity spikes in Greenland and Antarctic ice cores are applied as tracers of past volcanic activity. Besides providing information on the timing and magnitude of past eruptions, the acidity spikes are also widely used for synchronization of ice cores. All of the deep Greenland ice cores are thus synchronized throughout the last glacial cycle based on volcanic markers. Volcanic matching of ice cores from the two Hemispheres is much more challenging but it is feasible in periods of favourable conditions. Over the last two millennia, where ice cores are precisely dated, some 50 bipolar volcanic events have thus been identified. In order for an eruption to express a bipolar fingerprint it generally needs to be a low latitude eruption with stratospheric injection. Sometimes tephra is associated with the ice-core acidity spikes, but most often there is no tephra present in the ice. As yet, an unknown eruption occurring in 1259 AD is the only event reported to have deposited tephra in both Greenland and Antarctica. During the last glacial period bipolar volcanic matching is very challenging and very little work has been done, but recent high-resolution ice core records have the potential to provide bipolar ice core matching for some periods. Recently, Greenland and Antarctic ice cores have been linked by acidity spikes in the time window of the most recent eruption (the YTT eruption) of the Indonesian Toba volcano that is situated close to equator in Sumatra. Ash from this Toba event is widespread over large areas in Asia and has been identified as far west as Africa, but no corresponding tephra has been found in polar ice cores despite several attempts. The age of the YTT eruption is well constrained by recent Ar-Ar dating to have occurred some 74 ka ago close to the Marine Isotope Stage 4/5 boundary and close to the onset of the cold Greenland Stadial 20 and the corresponding mild Antarctic Isotopic Maxima 19 and 20. Surprisingly, no single outstanding acidity spike can be associated with the YTT Toba eruption in Greenland or Antarctica. Instead, several large bipolar ice cores acidity spikes are occurring within a couple of centuries at the time of the YTT eruption. To complicate matters, the intensity of those acidity spikes varies greatly from ice core to ice core. At this point, it is therefore impossible to relate the Toba eruption to a single event in the ice cores. Probably there have been several large low-latitude eruptions occurring close to the time of the YTT or the Toba volcano itself had multiple large eruptions within centuries. Bipolar volcanic matching allows for an estimation of the climatic impact of eruptions on a global scale. In the case of Toba, there must have been a global cooling following the enormous eruption, but unfortunately at this depth the resolution of the ice core temperature proxies does not allow for an identification of short term events (<100 yr). A significant warming event in Antarctica following the period associated with the YTT shows, however, that Toba did not initiate a long-term global cooling (>100 yr). At the time of YTT it appears that the inter-hemispheric climate variability is governed by the bipolar seesaw pattern that is active throughout most of the last glacial period. Still, it is intriguing that Toba occurs right at the time when Greenland and much of the northern Hemisphere enters its most extreme cold stadial of the last glacial period.

Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.

PubMed

Woodward, Alexander; Froese, Tom; Ikegami, Takashi

2015-02-01

The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains. Copyright © 2014 Elsevier Ltd. All rights reserved.

Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation

PubMed Central

Espinal, A.; Rostro-Gonzalez, H.; Carpio, M.; Guerra-Hernandez, E. I.; Ornelas-Rodriguez, M.; Puga-Soberanes, H. J.; Sotelo-Figueroa, M. A.; Melin, P.

2016-01-01

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases. PMID:27436997

Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation.

PubMed

Espinal, A; Rostro-Gonzalez, H; Carpio, M; Guerra-Hernandez, E I; Ornelas-Rodriguez, M; Puga-Soberanes, H J; Sotelo-Figueroa, M A; Melin, P

2016-01-01

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases.

Cerebral CBM1 neuron contributes to synaptic modulation appearing during rejection of seaweed in Aplysia kurodai.

PubMed

Narusuye, Kenji; Nagahama, Tatsumi

2002-11-01

The Japanese species Aplysia kurodai feeds well on Ulva but rejects Gelidium with distinctive rhythmic patterned movements of the jaws and radula. We have previously shown that the patterned jaw movements during the rejection of Gelidium might be caused by long-lasting suppression of the monosynaptic transmission from the multiaction MA neurons to the jaw-closing (JC) motor neurons in the buccal ganglia and that the modulation might be directly produced by some cerebral neurons. In the present paper, we have identified a pair of catecholaminergic neurons (CBM1) in bilateral cerebral M clusters. The CBM1, probably equivalent to CBI-1 in A. californica, simultaneously produced monosynaptic excitatory postsynaptic potentials (EPSPs) in the MA and JC neurons. Firing of the CBM1 reduced the size of the inhibitory postsynaptic currents (IPSCs) in the JC neuron, evoked by the MA spikes, for >100 s. Moreover, the application of dopamine mimicked the CBM1 modulatory effects and pretreatment with a D1 antagonist, SCH23390, blocked the modulatory effects induced by dopamine. It could also largely block the modulatory effects induced by the CBM1 firing. These results suggest that the CBM1 may directly modulate the synaptic transmission by releasing dopamine. Moreover, we explored the CBM1 spike activity induced by taste stimulation of the animal lips with seaweed extracts by the use of calcium imaging. The calcium-sensitive dye, Calcium Green-1, was iontophoretically loaded into a cell body of the CBM1 using a microelectrode. Application of either Ulva or Gelidium extract to the lips increased the fluorescence intensity, but the Gelidium extract always induced a larger change in fluorescence compared with the Ulva extract, although the solution used induced the maximum spike responses of the CBM1 for each of the seaweed extracts. When the firing frequency of the CBM1 activity after taste stimulation was estimated, the Gelidium extract induced a spike activity of ~30 spikes/s while the Ulva extract induced an activity of ~20 spikes/s, consistent with the effective firing frequency (>25 spikes/s) for the synaptic modulation. These results suggest that the CBM1 may be one of the cerebral neurons contributing to the modulation of the basic feeding circuits for rejection induced by the taste of seaweeds such as Gelidium.

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

PubMed Central

Shimamoto, Shoichi; Waldman, Zachary J.; Orosz, Iren; Song, Inkyung; Bragin, Anatol; Fried, Itzhak; Engel, Jerome; Staba, Richard; Sharan, Ashwini; Wu, Chengyuan; Sperling, Michael R.; Weiss, Shennan A.

2018-01-01

Objective To develop and validate a detector that identifies ripple (80–200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ). Methods iEEG recordings from 16 patients were first band-pass filtered (80–600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection. Results The signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ± 4.3%, and the sensitivity of the detector was 79.4 ± 3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p < .001). Conclusions Utilizing ICA to analyze iEEG recordings in referential montage provides many benefits to the study of high-frequency oscillations. The ripple rates and properties defined using this approach may accurately delineate the seizure onset zone. Significance Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of HFO biomarkers. PMID:29113719

Heterogeneity of Purkinje cell simple spike-complex spike interactions: zebrin- and non-zebrin-related variations.

PubMed

Tang, Tianyu; Xiao, Jianqiang; Suh, Colleen Y; Burroughs, Amelia; Cerminara, Nadia L; Jia, Linjia; Marshall, Sarah P; Wise, Andrew K; Apps, Richard; Sugihara, Izumi; Lang, Eric J

2017-08-01

Cerebellar Purkinje cells (PCs) generate two types of action potentials, simple and complex spikes. Although they are generated by distinct mechanisms, interactions between the two spike types exist. Zebrin staining produces alternating positive and negative stripes of PCs across most of the cerebellar cortex. Thus, here we compared simple spike-complex spike interactions both within and across zebrin populations. Simple spike activity undergoes a complex modulation preceding and following a complex spike. The amplitudes of the pre- and post-complex spike modulation phases were correlated across PCs. On average, the modulation was larger for PCs in zebrin positive regions. Correlations between aspects of the complex spike waveform and simple spike activity were found, some of which varied between zebrin positive and negative PCs. The implications of the results are discussed with regard to hypotheses that complex spikes are triggered by rises in simple spike activity for either motor learning or homeostatic functions. Purkinje cells (PCs) generate two types of action potentials, called simple and complex spikes (SSs and CSs). We first investigated the CS-associated modulation of SS activity and its relationship to the zebrin status of the PC. The modulation pattern consisted of a pre-CS rise in SS activity, and then, following the CS, a pause, a rebound, and finally a late inhibition of SS activity for both zebrin positive (Z+) and negative (Z-) cells, though the amplitudes of the phases were larger in Z+ cells. Moreover, the amplitudes of the pre-CS rise with the late inhibitory phase of the modulation were correlated across PCs. In contrast, correlations between modulation phases across CSs of individual PCs were generally weak. Next, the relationship between CS spikelets and SS activity was investigated. The number of spikelets/CS correlated with the average SS firing rate only for Z+ cells. In contrast, correlations across CSs between spikelet numbers and the amplitudes of the SS modulation phases were generally weak. Division of spikelets into likely axonally propagated and non-propagated groups (based on their interspikelet interval) showed that the correlation of spikelet number with SS firing rate primarily reflected a relationship with non-propagated spikelets. In sum, the results show both zebrin-related and non-zebrin-related physiological heterogeneity in SS-CS interactions among PCs, which suggests that the cerebellar cortex is more functionally diverse than is assumed by standard theories of cerebellar function. © 2017 The Authors. The Journal of Physiology © 2017 The Physiological Society.

Enhanced interlaminar excitation or reduced superficial layer inhibition in neocortex generates different spike-and-wave-like electrographic events in vitro

PubMed Central

Hall, Stephen P.; Traub, Roger D.; Adams, Natalie E.; Cunningham, Mark O.; Schofield, Ian; Jenkins, Alistair J.

2018-01-01

Acute in vitro models have revealed a great deal of information about mechanisms underlying many types of epileptiform activity. However, few examples exist that shed light on spike-and-wave (SpW) patterns of pathological activity. SpW are seen in many epilepsy syndromes, both generalized and focal, and manifest across the entire age spectrum. They are heterogeneous in terms of their severity, symptom burden, and apparent anatomical origin (thalamic, neocortical, or both), but any relationship between this heterogeneity and underlying pathology remains elusive. In this study we demonstrate that physiological delta-frequency rhythms act as an effective substrate to permit modeling of SpW of cortical origin and may help to address this issue. For a starting point of delta activity, multiple subtypes of SpW could be modeled computationally and experimentally by either enhancing the magnitude of excitatory synaptic events ascending from neocortical layer 5 to layers 2/3 or selectively modifying superficial layer GABAergic inhibition. The former generated SpW containing multiple field spikes with long interspike intervals, whereas the latter generated SpW with short-interval multiple field spikes. Both types had different laminar origins and each disrupted interlaminar cortical dynamics in a different manner. A small number of examples of human recordings from patients with different diagnoses revealed SpW subtypes with the same temporal signatures, suggesting that detailed quantification of the pattern of spikes in SpW discharges may be a useful indicator of disparate underlying epileptogenic pathologies. NEW & NOTEWORTHY Spike-and-wave-type discharges (SpW) are a common feature in many epilepsies. Their electrographic manifestation is highly varied, as are available genetic clues to associated underlying pathology. Using computational and in vitro models, we demonstrate that distinct subtypes of SpW are generated by lamina-selective disinhibition or enhanced interlaminar excitation. These subtypes could be detected in at least some noninvasive patient recordings, suggesting more detailed analysis of SpW may be useful in determining clinical pathology. PMID:28954894

Enhanced interlaminar excitation or reduced superficial layer inhibition in neocortex generates different spike-and-wave-like electrographic events in vitro.

PubMed

Hall, Stephen P; Traub, Roger D; Adams, Natalie E; Cunningham, Mark O; Schofield, Ian; Jenkins, Alistair J; Whittington, Miles A

2018-01-01

Acute in vitro models have revealed a great deal of information about mechanisms underlying many types of epileptiform activity. However, few examples exist that shed light on spike-and-wave (SpW) patterns of pathological activity. SpW are seen in many epilepsy syndromes, both generalized and focal, and manifest across the entire age spectrum. They are heterogeneous in terms of their severity, symptom burden, and apparent anatomical origin (thalamic, neocortical, or both), but any relationship between this heterogeneity and underlying pathology remains elusive. In this study we demonstrate that physiological delta-frequency rhythms act as an effective substrate to permit modeling of SpW of cortical origin and may help to address this issue. For a starting point of delta activity, multiple subtypes of SpW could be modeled computationally and experimentally by either enhancing the magnitude of excitatory synaptic events ascending from neocortical layer 5 to layers 2/3 or selectively modifying superficial layer GABAergic inhibition. The former generated SpW containing multiple field spikes with long interspike intervals, whereas the latter generated SpW with short-interval multiple field spikes. Both types had different laminar origins and each disrupted interlaminar cortical dynamics in a different manner. A small number of examples of human recordings from patients with different diagnoses revealed SpW subtypes with the same temporal signatures, suggesting that detailed quantification of the pattern of spikes in SpW discharges may be a useful indicator of disparate underlying epileptogenic pathologies. NEW & NOTEWORTHY Spike-and-wave-type discharges (SpW) are a common feature in many epilepsies. Their electrographic manifestation is highly varied, as are available genetic clues to associated underlying pathology. Using computational and in vitro models, we demonstrate that distinct subtypes of SpW are generated by lamina-selective disinhibition or enhanced interlaminar excitation. These subtypes could be detected in at least some noninvasive patient recordings, suggesting more detailed analysis of SpW may be useful in determining clinical pathology.

Thermal spike effect in sputtering of porous germanium to form surface pattern by high energy heavy ions irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hooda, Sonu; Khan, S. A.; Kanjilal, D.

2016-05-16

Germanium exhibits a remarkable effect when subjected to high energy heavy ions irradiation. A synergic effect of high electronic energy loss (S{sub e} = 16.4 keV nm{sup −1}) and nuclear energy loss (S{sub n} = 0.1 keV nm{sup −1}) of 100 MeV Ag ions irradiation in Ge is presented. The results show that crystalline Ge is insensitive to the ionizing part of energy loss whereas thermal spike generated in the damaged Ge leads to the formation of porous structure. Further, an unusual high sputtering of the porous structure opens up the sub-surface voids to show the surface pattern. We explore the role of electron and phonon confinement to explainmore » this effect.« less

A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

PubMed Central

Maass, Wolfgang

2008-01-01

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of spiking neurons could be achieved in a self-organizing manner through local synaptic plasticity. However, the capabilities and limitations of this learning rule could so far only be tested through computer simulations. This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. Our model for this experiment relies on a combination of reward-modulated STDP with variable spontaneous firing activity. Hence it also provides a possible functional explanation for trial-to-trial variability, which is characteristic for cortical networks of neurons but has no analogue in currently existing artificial computing systems. In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics. PMID:18846203

Python for large-scale electrophysiology.

PubMed

Spacek, Martin; Blanche, Tim; Swindale, Nicholas

2008-01-01

Electrophysiology is increasingly moving towards highly parallel recording techniques which generate large data sets. We record extracellularly in vivo in cat and rat visual cortex with 54-channel silicon polytrodes, under time-locked visual stimulation, from localized neuronal populations within a cortical column. To help deal with the complexity of generating and analysing these data, we used the Python programming language to develop three software projects: one for temporally precise visual stimulus generation ("dimstim"); one for electrophysiological waveform visualization and spike sorting ("spyke"); and one for spike train and stimulus analysis ("neuropy"). All three are open source and available for download (http://swindale.ecc.ubc.ca/code). The requirements and solutions for these projects differed greatly, yet we found Python to be well suited for all three. Here we present our software as a showcase of the extensive capabilities of Python in neuroscience.

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

PubMed Central

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-01-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity. PMID:23592970

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

PubMed

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-04-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Analysis of cocoa flavanols and procyanidins (DP 1-10) in cocoa-containing ingredients and products by rapid resolution liquid chromatography: single-laboratory validation.

PubMed

Machonis, Philip R; Jones, Matthew A; Kwik-Uribe, Catherine

2014-01-01

Recently, a multilaboratory validation (MLV) of AOAC Official Method 2012.24 for the determination of cocoa flavanols and procyanidins (CF-CP) in cocoa-based ingredients and products determined that the method was robust, reliable, and transferrable. Due to the complexity of the CF-CP molecules, this method required a run time exceeding 1 h to achieve acceptable separations. To address this issue, a rapid resolution normal phase LC method was developed, and a single-laboratory validation (SLV) study conducted. Flavanols and procyanidins with a degree of polymerization (DP) up to 10 were eluted in 15 min using a binary gradient applied to a diol stationary phase, detected using fluorescence detection, and reported as a total sum of DP 1-10. Quantification was achieved using (-)-epicatechin-based relative response factors for DP 2-10. Spike recovery samples and seven different types of cocoa-based samples were analyzed to evaluate the accuracy, precision, LOD, LOQ, and linearity of the method. The within-day precision of the reported content for the samples was 1.15-5.08%, and overall precision was 3.97-13.61%. Spike-recovery experiments demonstrated recoveries of over 98%. The results of this SLV were compared to those previously obtained in the MLV and found to be consistent. The translation to rapid resolution LC allowed for an 80% reduction in analysis time and solvent usage, while retaining the accuracy and reliability of the original method. The savings in both cost and time of this rapid method make it well-suited for routine laboratory use.

Evaluation of automated assays for immunoglobulin G, M, and A measurements in dog and cat serum.

PubMed

Tvarijonaviciute, Asta; Martínez-Subiela, Silvia; Caldin, Marco; Tecles, Fernando; Ceron, Jose J

2013-09-01

Measurements of immunoglobulins (Igs) in companion animals can be useful to detect deficiencies of the humoral immune system, that can be associated with opportunistic or chronic infections, or other immune-mediated disorders including B-cell neoplasms. The purpose of this study was to evaluate commercially available automated immunoturbidimetric assays designed for human IgG, M, and A measurements in canine and feline serum using species-specific calibrators. Canine and feline serum samples with different IgG, M, and A concentrations were used for the analytical validation of the assays. Intra- and inter-assay precision, linearity under dilution, spiking recovery, and limit of detection were determined. In addition, effects of lipemia, hemolysis, and bilirubinemia were evaluated. Finally, Ig concentrations were determined in small groups of diseased dogs and cats, and compared with healthy groups. Spiking recovery and linearity under dilution tests showed that the assays measured Igs in canine and feline serum samples precisely and accurately. Intra- and inter-assay imprecisions were lower than 15% in all cases. Significantly higher IgG, IgM, and IgA levels were observed in dogs with leishmaniasis, while dogs with pyometra showed a statistically significant increase in IgM and IgA concentrations in comparison with healthy dogs. Significantly higher IgG and IgM levels were observed in FIV-infected cats compared with healthy ones. The automated human Ig assays showed adequate precision and accuracy with serum samples from dogs and cats. Also, they were able to discriminate different concentrations of Igs in healthy and diseased animals. © 2013 American Society for Veterinary Clinical Pathology.

Method development for the determination of fluorine in toothpaste via molecular absorption of aluminum mono fluoride using a high-resolution continuum source nitrous oxide/acetylene flame atomic absorption spectrophotometer.

PubMed

Ozbek, Nil; Akman, Suleyman

2012-05-30

Fluorine was determined via the rotational molecular absorption line of aluminum mono fluoride (AlF) generated in C(2)H(2)/N(2)O flame at 227.4613 nm using a high-resolution continuum source flame atomic absorption spectrophotometer (HR-CS-FAAS). The effects of AlF wavelength, burner height, fuel rate (C(2)H(2)/N(2)O) and amount of Al on the accuracy, precision and sensitivity were investigated and optimized. The Al-F absorption band at 227.4613 nm was found to be the most suitable analytical line with respect to sensitivity and spectral interferences. Maximum sensitivity and a good linearity were obtained in acetylene-nitrous oxide flame at a flow rate of 210 L h(-1) and a burner height of 8mm using 3000 mg L(-1) of Al for 10-1000 mg L(-1)of F. The accuracy and precision of the method were tested by analyzing spiked samples and waste water certified reference material. The results were in good agreement with the certified and spiked amounts as well as the precision of several days during this study was satisfactory (RSD<10%). The limit of detection and characteristic concentration of the method were 5.5 mg L(-1) and 72.8 mg L(-1), respectively. Finally, the fluorine concentrations in several toothpaste samples were determined. The results found and given by the producers were not significantly different. The method was simple, fast, accurate and sensitive. Copyright © 2012 Elsevier B.V. All rights reserved.

Stable Sequential Activity Underlying the Maintenance of a Precisely Executed Skilled Behavior.

PubMed

Katlowitz, Kalman A; Picardo, Michel A; Long, Michael A

2018-05-21

A vast array of motor skills can be maintained throughout life. Do these behaviors require stability of individual neuron tuning or can the output of a given circuit remain constant despite fluctuations in single cells? This question is difficult to address due to the variability inherent in most motor actions studied in the laboratory. A notable exception, however, is the courtship song of the adult zebra finch, which is a learned, highly precise motor act mediated by orderly dynamics within premotor neurons of the forebrain. By longitudinally tracking the activity of excitatory projection neurons during singing using two-photon calcium imaging, we find that both the number and the precise timing of song-related spiking events remain nearly identical over the span of several weeks to months. These findings demonstrate that learned, complex behaviors can be stabilized by maintaining precise and invariant tuning at the level of single neurons. Copyright © 2018 Elsevier Inc. All rights reserved.

Conjunctive coding in an evolved spiking model of retrosplenial cortex.

PubMed

Rounds, Emily L; Alexander, Andrew S; Nitz, Douglas A; Krichmar, Jeffrey L

2018-06-04

Retrosplenial cortex (RSC) is an association cortex supporting spatial navigation and memory. However, critical issues remain concerning the forms by which its ensemble spiking patterns register spatial relationships that are difficult for experimental techniques to fully address. We therefore applied an evolutionary algorithmic optimization technique to create spiking neural network models that matched electrophysiologically observed spiking dynamics in rat RSC neuronal ensembles. Virtual experiments conducted on the evolved networks revealed a mixed selectivity coding capability that was not built into the optimization method, but instead emerged as a consequence of replicating biological firing patterns. The experiments reveal several important outcomes of mixed selectivity that may subserve flexible navigation and spatial representation: (a) robustness to loss of specific inputs, (b) immediate and stable encoding of novel routes and route locations, (c) automatic resolution of input variable conflicts, and (d) dynamic coding that allows rapid adaptation to changing task demands without retraining. These findings suggest that biological retrosplenial cortex can generate unique, first-trial, conjunctive encodings of spatial positions and actions that can be used by downstream brain regions for navigation and path integration. Moreover, these results are consistent with the proposed role for the RSC in the transformation of representations between reference frames and navigation strategy deployment. Finally, the specific modeling framework used for evolving synthetic retrosplenial networks represents an important advance for computational modeling by which synthetic neural networks can encapsulate, describe, and predict the behavior of neural circuits at multiple levels of function. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Oscillation patterns are enhanced and firing threshold is lowered in medullary respiratory neuron discharges by threshold doses of a μ-opioid receptor agonist

PubMed Central

Mifflin, Steve W.

2017-01-01

μ-Opioid receptors are distributed widely in the brain stem respiratory network, and opioids with selectivity for μ-type receptors slow in vivo respiratory rhythm in lowest effective doses. Several studies have reported μ-opioid receptor effects on the three-phase rhythm of respiratory neurons, but there are until now no reports of opioid effects on oscillatory activity within respiratory discharges. In this study, effects of the μ-opioid receptor agonist fentanyl on spike train discharge properties of several different types of rhythm-modulating medullary respiratory neuron discharges were analyzed. Doses of fentanyl that were just sufficient for prolongation of discharges and slowing of the three-phase respiratory rhythm also produced pronounced enhancement of spike train properties. Oscillation and burst patterns detected by autocorrelation measurements were greatly enhanced, and interspike intervals were prolonged. Spike train properties under control conditions and after fentanyl were uniform within each experiment, but varied considerably between experiments, which might be related to variability in acid-base balance in the brain stem extracellular fluid. Discharge threshold was shifted to more negative levels of membrane potential. The effects on threshold are postulated to result from opioid-mediated disinhibition and postsynaptic enhancement of N-methyl-d- aspartate receptor current. Lowering of firing threshold, enhancement of spike train oscillations and bursts and prolongation of discharges by lowest effective doses of fentanyl could represent compensatory adjustments in the brain stem respiratory network to override opioid blunting of CO2/pH chemosensitivity. PMID:28202437

Unsupervised learning of digit recognition using spike-timing-dependent plasticity

PubMed Central

Diehl, Peter U.; Cook, Matthew

2015-01-01

In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks. PMID:26941637

Oscillation patterns are enhanced and firing threshold is lowered in medullary respiratory neuron discharges by threshold doses of a μ-opioid receptor agonist.

PubMed

Lalley, Peter M; Mifflin, Steve W

2017-05-01

μ-Opioid receptors are distributed widely in the brain stem respiratory network, and opioids with selectivity for μ-type receptors slow in vivo respiratory rhythm in lowest effective doses. Several studies have reported μ-opioid receptor effects on the three-phase rhythm of respiratory neurons, but there are until now no reports of opioid effects on oscillatory activity within respiratory discharges. In this study, effects of the μ-opioid receptor agonist fentanyl on spike train discharge properties of several different types of rhythm-modulating medullary respiratory neuron discharges were analyzed. Doses of fentanyl that were just sufficient for prolongation of discharges and slowing of the three-phase respiratory rhythm also produced pronounced enhancement of spike train properties. Oscillation and burst patterns detected by autocorrelation measurements were greatly enhanced, and interspike intervals were prolonged. Spike train properties under control conditions and after fentanyl were uniform within each experiment, but varied considerably between experiments, which might be related to variability in acid-base balance in the brain stem extracellular fluid. Discharge threshold was shifted to more negative levels of membrane potential. The effects on threshold are postulated to result from opioid-mediated disinhibition and postsynaptic enhancement of N -methyl-d- aspartate receptor current. Lowering of firing threshold, enhancement of spike train oscillations and bursts and prolongation of discharges by lowest effective doses of fentanyl could represent compensatory adjustments in the brain stem respiratory network to override opioid blunting of CO 2 /pH chemosensitivity. Copyright © 2017 the American Physiological Society.

Enhanced Burst-Suppression and Disruption of Local Field Potential Synchrony in a Mouse Model of Focal Cortical Dysplasia Exhibiting Spike-Wave Seizures.

PubMed

Williams, Anthony J; Zhou, Chen; Sun, Qian-Quan

2016-01-01

Focal cortical dysplasias (FCDs) are a common cause of brain seizures and are often associated with intractable epilepsy. Here we evaluated aberrant brain neurophysiology in an in vivo mouse model of FCD induced by neonatal freeze lesions (FLs) to the right cortical hemisphere (near S1). Linear multi-electrode arrays were used to record extracellular potentials from cortical and subcortical brain regions near the FL in anesthetized mice (5-13 months old) followed by 24 h cortical electroencephalogram (EEG) recordings. Results indicated that FL animals exhibit a high prevalence of spontaneous spike-wave discharges (SWDs), predominately during sleep (EEG), and an increase in the incidence of hyper-excitable burst/suppression activity under general anesthesia (extracellular recordings, 0.5%-3.0% isoflurane). Brief periods of burst activity in the local field potential (LFP) typically presented as an arrhythmic pattern of increased theta-alpha spectral peaks (4-12 Hz) on a background of low-amplitude delta activity (1-4 Hz), were associated with an increase in spontaneous spiking of cortical neurons, and were highly synchronized in control animals across recording sites in both cortical and subcortical layers (average cross-correlation values ranging from +0.73 to +1.0) with minimal phase shift between electrodes. However, in FL animals, cortical vs. subcortical burst activity was strongly out of phase with significantly lower cross-correlation values compared to controls (average values of -0.1 to +0.5, P < 0.05 between groups). In particular, a marked reduction in the level of synchronous burst activity was observed, the closer the recording electrodes were to the malformation (Pearson's Correlation = 0.525, P < 0.05). In a subset of FL animals (3/9), burst activity also included a spike or spike-wave pattern similar to the SWDs observed in unanesthetized animals. In summary, neonatal FLs increased the hyperexcitable pattern of burst activity induced by anesthesia and disrupted field potential synchrony between cortical and subcortical brain regions near the site of the cortical malformation. Monitoring the altered electrophysiology of burst activity under general anesthesia with multi-dimensional micro-electrode arrays may serve to define distinct neurophysiological biomarkers of epileptogenesis in human brain and improve techniques for surgical resection of epileptogenic malformed brain tissue.

M-type potassium conductance controls the emergence of neural phase codes: a combined experimental and neuron modelling study

PubMed Central

Kwag, Jeehyun; Jang, Hyun Jae; Kim, Mincheol; Lee, Sujeong

2014-01-01

Rate and phase codes are believed to be important in neural information processing. Hippocampal place cells provide a good example where both coding schemes coexist during spatial information processing. Spike rate increases in the place field, whereas spike phase precesses relative to the ongoing theta oscillation. However, what intrinsic mechanism allows for a single neuron to generate spike output patterns that contain both neural codes is unknown. Using dynamic clamp, we simulate an in vivo-like subthreshold dynamics of place cells to in vitro CA1 pyramidal neurons to establish an in vitro model of spike phase precession. Using this in vitro model, we show that membrane potential oscillation (MPO) dynamics is important in the emergence of spike phase codes: blocking the slowly activating, non-inactivating K+ current (IM), which is known to control subthreshold MPO, disrupts MPO and abolishes spike phase precession. We verify the importance of adaptive IM in the generation of phase codes using both an adaptive integrate-and-fire and a Hodgkin–Huxley (HH) neuron model. Especially, using the HH model, we further show that it is the perisomatically located IM with slow activation kinetics that is crucial for the generation of phase codes. These results suggest an important functional role of IM in single neuron computation, where IM serves as an intrinsic mechanism allowing for dual rate and phase coding in single neurons. PMID:25100320

Realistic thermodynamic and statistical-mechanical measures for neural synchronization.

PubMed

Kim, Sang-Yoon; Lim, Woochang

2014-04-15

Synchronized brain rhythms, associated with diverse cognitive functions, have been observed in electrical recordings of brain activity. Neural synchronization may be well described by using the population-averaged global potential VG in computational neuroscience. The time-averaged fluctuation of VG plays the role of a "thermodynamic" order parameter O used for describing the synchrony-asynchrony transition in neural systems. Population spike synchronization may be well visualized in the raster plot of neural spikes. The degree of neural synchronization seen in the raster plot is well measured in terms of a "statistical-mechanical" spike-based measure Ms introduced by considering the occupation and the pacing patterns of spikes. The global potential VG is also used to give a reference global cycle for the calculation of Ms. Hence, VG becomes an important collective quantity because it is associated with calculation of both O and Ms. However, it is practically difficult to directly get VG in real experiments. To overcome this difficulty, instead of VG, we employ the instantaneous population spike rate (IPSR) which can be obtained in experiments, and develop realistic thermodynamic and statistical-mechanical measures, based on IPSR, to make practical characterization of the neural synchronization in both computational and experimental neuroscience. Particularly, more accurate characterization of weak sparse spike synchronization can be achieved in terms of realistic statistical-mechanical IPSR-based measure, in comparison with the conventional measure based on VG. Copyright © 2014. Published by Elsevier B.V.

Effect of marital status on death rates. Part 2: Transient mortality spikes

NASA Astrophysics Data System (ADS)

Richmond, Peter; Roehner, Bertrand M.

2016-05-01

We examine what happens in a population when it experiences an abrupt change in surrounding conditions. Several cases of such ;abrupt transitions; for both physical and living social systems are analyzed from which it can be seen that all share a common pattern. First, a steep rising death rate followed by a much slower relaxation process during which the death rate decreases as a power law. This leads us to propose a general principle which can be summarized as follows: ;Any abrupt change in living conditions generates a mortality spike which acts as a kind of selection process;. This we term the Transient Shock conjecture. It provides a qualitative model which leads to testable predictions. For example, marriage certainly brings about a major change in personal and social conditions and according to our conjecture one would expect a mortality spike in the months following marriage. At first sight this may seem an unlikely proposition but we demonstrate (by three different methods) that even here the existence of mortality spikes is supported by solid empirical evidence.

Spike processing with a graphene excitable laser

PubMed Central

Shastri, Bhavin J.; Nahmias, Mitchell A.; Tait, Alexander N.; Rodriguez, Alejandro W.; Wu, Ben; Prucnal, Paul R.

2016-01-01

Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved “spiking” of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate a unified platform for spike processing with a graphene-coupled laser system. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation—fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system and also propose and simulate an analogous integrated device. The addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms. PMID:26753897

Modeling and Identification of a Realistic Spiking Neural Network and Musculoskeletal Model of the Human Arm, and an Application to the Stretch Reflex.

PubMed

Sreenivasa, Manish; Ayusawa, Ko; Nakamura, Yoshihiko

2016-05-01

This study develops a multi-level neuromuscular model consisting of topological pools of spiking motor, sensory and interneurons controlling a bi-muscular model of the human arm. The spiking output of motor neuron pools were used to drive muscle actions and skeletal movement via neuromuscular junctions. Feedback information from muscle spindles were relayed via monosynaptic excitatory and disynaptic inhibitory connections, to simulate spinal afferent pathways. Subject-specific model parameters were identified from human experiments by using inverse dynamics computations and optimization methods. The identified neuromuscular model was used to simulate the biceps stretch reflex and the results were compared to an independent dataset. The proposed model was able to track the recorded data and produce dynamically consistent neural spiking patterns, muscle forces and movement kinematics under varying conditions of external forces and co-contraction levels. This additional layer of detail in neuromuscular models has important relevance to the research communities of rehabilitation and clinical movement analysis by providing a mathematical approach to studying neuromuscular pathology.

Continuous high-frequency activity in mesial temporal lobe structures

PubMed Central

Mari, Francesco; Zelmann, Rina; Andrade-Valenca, Luciana; Dubeau, Francois; Gotman, Jean

2013-01-01

Summary Purpose Many recent studies have reported the importance of high-frequency oscillations (HFOs) in the intracerebral electroencephalography (EEG) of patients with epilepsy. These HFOs have been defined as events that stand out from the background. We have noticed that this background often consists itself of high-frequency rhythmic activity. The purpose of this study is to perform a first evaluation of the characteristics of high-frequency continuous or semicontinuous background activity. Methods Because the continuous high-frequency pattern was noted mainly in mesial temporal structures, we reviewed the EEG studies from these structures in 24 unselected patients with electrodes implanted in these regions. Sections of background away from interictal spikes were marked visually during periods of slow-wave sleep and wakefulness. They were then high-passed filtered at 80 Hz and categorized as having high-frequency rhythmic activity in one of three patterns: continuous/semicontinuous, irregular, sporadic. Wavelet entropy, which measures the degree of rhythmicity of a signal, was calculated for the marked background sections. Key Findings Ninety-six bipolar channels were analyzed. The continuous/semicontinuous pattern was found frequently (29/96 channels during wake and 34/96 during sleep). The different patterns were consistent between sleep and wakefulness. The continuous/semicontinuous pattern was found significantly more often in the hippocampus than in the parahippocampal gyrus and was rarely found in the amygdala. The types of pattern were not influenced by whether a channel was within the seizure-onset zone, or whether it was a lesional channel. The continuous/semicontinuous pattern was associated with a higher frequency of spikes and with high rates of ripples and fast ripples. Significance It appears that high-frequency activity (above 80 Hz) does not appear only in the form of brief paroxysmal events but also in the form of continuous rhythmic activity or very long bursts. In this study limited to mesial temporal structures, we found a clear anatomic preference for the hippocampus. Although associated with spikes and with distinct HFOs, this pattern was not clearly associated with the seizure-onset zone. Future studies will need to evaluate systematically the presence of this pattern, as it may have a pathophysiologic significance and it will also have an important influence on the very definition of HFOs. PMID:22416973

Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons.

PubMed

Oddo, Calogero M; Mazzoni, Alberto; Spanne, Anton; Enander, Jonas M D; Mogensen, Hannes; Bengtsson, Fredrik; Camboni, Domenico; Micera, Silvestro; Jörntell, Henrik

2017-04-04

Investigations of the mechanisms of touch perception and decoding has been hampered by difficulties in achieving invariant patterns of skin sensor activation. To obtain reproducible spatiotemporal patterns of activation of sensory afferents, we used an artificial fingertip equipped with an array of neuromorphic sensors. The artificial fingertip was used to transduce real-world haptic stimuli into spatiotemporal patterns of spikes. These spike patterns were delivered to the skin afferents of the second digit of rats via an array of stimulation electrodes. Combined with low-noise intra- and extracellular recordings from neocortical neurons in vivo, this approach provided a previously inaccessible high resolution analysis of the representation of tactile information in the neocortical neuronal circuitry. The results indicate high information content in individual neurons and reveal multiple novel neuronal tactile coding features such as heterogeneous and complementary spatiotemporal input selectivity also between neighboring neurons. Such neuronal heterogeneity and complementariness can potentially support a very high decoding capacity in a limited population of neurons. Our results also indicate a potential neuroprosthetic approach to communicate with the brain at a very high resolution and provide a potential novel solution for evaluating the degree or state of neurological disease in animal models.

Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons

PubMed Central

Oddo, Calogero M.; Mazzoni, Alberto; Spanne, Anton; Enander, Jonas M. D.; Mogensen, Hannes; Bengtsson, Fredrik; Camboni, Domenico; Micera, Silvestro; Jörntell, Henrik

2017-01-01

Investigations of the mechanisms of touch perception and decoding has been hampered by difficulties in achieving invariant patterns of skin sensor activation. To obtain reproducible spatiotemporal patterns of activation of sensory afferents, we used an artificial fingertip equipped with an array of neuromorphic sensors. The artificial fingertip was used to transduce real-world haptic stimuli into spatiotemporal patterns of spikes. These spike patterns were delivered to the skin afferents of the second digit of rats via an array of stimulation electrodes. Combined with low-noise intra- and extracellular recordings from neocortical neurons in vivo, this approach provided a previously inaccessible high resolution analysis of the representation of tactile information in the neocortical neuronal circuitry. The results indicate high information content in individual neurons and reveal multiple novel neuronal tactile coding features such as heterogeneous and complementary spatiotemporal input selectivity also between neighboring neurons. Such neuronal heterogeneity and complementariness can potentially support a very high decoding capacity in a limited population of neurons. Our results also indicate a potential neuroprosthetic approach to communicate with the brain at a very high resolution and provide a potential novel solution for evaluating the degree or state of neurological disease in animal models. PMID:28374841

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

PubMed Central

Dura-Bernal, Salvador; Li, Kan; Neymotin, Samuel A.; Francis, Joseph T.; Principe, Jose C.; Lytton, William W.

2016-01-01

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors. PMID:26903796

Investigation of mercury exchange between forest canopy vegetation and the atmosphere using a new dynamic chamber

USGS Publications Warehouse

Graydon, J.A.; St. Louis, V.L.; Lindberg, S.E.; Hintelmann, H.; Krabbenhoft, D.P.

2006-01-01

This paper presents the design of a dynamic chamber system that allows full transmission of PAR and UV radiation and permits enclosed intact foliage to maintain normal physiological function while Hg(0) flux rates are quantified in the field. Black spruce and jack pine foliage both emitted and absorbed Hg(0), exhibiting compensation points near atmospheric Hg(0) concentrations of ???2-3 ng m-3. Using enriched stable Hg isotope spikes, patterns of spike Hg(II) retention on foliage were investigated. Hg(0) evasion rates from foliage were simultaneously measured using the chamber to determine if the decline of foliar spike Hg(II) concentrations overtime could be explained by the photoreduction and re-emission of spike Hg to the atmosphere. This mass balance approach suggested that spike Hg(0) fluxes alone could not account for the measured decrease in spike Hg(II) on foliage following application, implying that either the chamber underestimates the true photoreduction of Hg(II) to Hg(0) on foliage, or other mechanisms of Hg(II) loss from foliage, such as cuticle weathering, are in effect. The radiation spectrum responsible for the photoreduction of newly deposited Hg(II) on foliage was also investigated. Our spike experiments suggest that some of the Hg(II) in wet deposition retained by the forest canopy may be rapidly photoreduced to Hg(0) and re-emitted back to the atmosphere, while another portion may be retained by foliage at the end of the growing season, with some being deposited in litterfall. This finding has implications for the estimation of Hg dry deposition based on throughfall and litterfall fluxes. ?? 2006 American Chemical Society.

Changes in auditory nerve responses across the duration of sinusoidally amplitude-modulated electric pulse-train stimuli.

PubMed

Hu, Ning; Miller, Charles A; Abbas, Paul J; Robinson, Barbara K; Woo, Jihwan

2010-12-01

Response rates of auditory nerve fibers (ANFs) to electric pulse trains change over time, reflecting substantial spike-rate adaptation that depends on stimulus parameters. We hypothesize that adaptation affects the representation of amplitude-modulated pulse trains used by cochlear prostheses to transmit speech information to the auditory system. We recorded cat ANF responses to sinusoidally amplitude-modulated (SAM) trains with 5,000 pulse/s carriers. Stimuli delivered by a monopolar intracochlear electrode had fixed modulation frequency (100 Hz) and depth (10%). ANF responses were assessed by spike-rate measures, while representation of modulation was evaluated by vector strength (VS) and the fundamental component of the fast Fourier transform (F(0) amplitude). These measures were assessed across the 400 ms duration of pulse-train stimuli, a duration relevant to speech stimuli. Different stimulus levels were explored and responses were categorized into four spike-rate groups to assess level effects across ANFs. The temporal pattern of rate adaptation to modulated trains was similar to that of unmodulated trains, but with less rate adaptation. VS to the modulator increased over time and tended to saturate at lower spike rates, while F(0) amplitude typically decreased over time for low driven rates and increased for higher driven rates. VS at moderate and high spike rates and degree of F(0) amplitude temporal changes at low and moderate spike rates were positively correlated with the degree of rate adaptation. Thus, high-rate carriers will modify the ANF representation of the modulator over time. As the VS and F(0) measures were sensitive to adaptation-related changes over different spike-rate ranges, there is value in assessing both measures.

Defects formation and spiral waves in a network of neurons in presence of electromagnetic induction.

PubMed

Rostami, Zahra; Jafari, Sajad

2018-04-01

Complex anatomical and physiological structure of an excitable tissue (e.g., cardiac tissue) in the body can represent different electrical activities through normal or abnormal behavior. Abnormalities of the excitable tissue coming from different biological reasons can lead to formation of some defects. Such defects can cause some successive waves that may end up to some additional reorganizing beating behaviors like spiral waves or target waves. In this study, formation of defects and the resulting emitted waves in an excitable tissue are investigated. We have considered a square array network of neurons with nearest-neighbor connections to describe the excitable tissue. Fundamentally, electrophysiological properties of ion currents in the body are responsible for exhibition of electrical spatiotemporal patterns. More precisely, fluctuation of accumulated ions inside and outside of cell causes variable electrical and magnetic field. Considering undeniable mutual effects of electrical field and magnetic field, we have proposed the new Hindmarsh-Rose (HR) neuronal model for the local dynamics of each individual neuron in the network. In this new neuronal model, the influence of magnetic flow on membrane potential is defined. This improved model holds more bifurcation parameters. Moreover, the dynamical behavior of the tissue is investigated in different states of quiescent, spiking, bursting and even chaotic state. The resulting spatiotemporal patterns are represented and the time series of some sampled neurons are displayed, as well.

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity

PubMed Central

Lin, I-Chun; Xing, Dajun; Shapley, Robert

2014-01-01

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes. PMID:22684587

Effects of calcium (Ca(2+)) extrusion mechanisms on electrophysiological properties in a hypoglossal motoneuron: insight from a mathematical model.

PubMed

Horn, Kyle G; Solomon, Irene C

2014-01-01

Spike-frequency dynamics and spike shape can provide insight into the types of ion channels present in any given neuron and give a sense for the precise response any neuron may have to a given input stimulus. Motoneuron firing frequency over time is especially important due to its direct effect on motor output. Of particular interest is intracellular Ca(2+), which exerts a powerful influence on both firing properties over time and spike shape. In order to better understand the cellular mechanisms for the regulation of intracellular Ca(2+) and their effect on spiking behavior, we have modified a computational model of an HM to include a variety of Ca(2+) handling processes. For the current study, a series of HM models that include Ca(2+) pumps, Na(+)/Ca(2+) exchangers, and a generic exponential decay of excess Ca(2+) were generated. Simulations from these models indicate that although each extrusion mechanism exerts a similar effect on voltage, the firing properties change distinctly with the inclusion of additional Ca(2+)-related mechanisms: BK channels, Ca(2+) buffering, and diffusion of [Ca(2+)]i modeled via a linear diffusion partial differential equation. While an exponential decay of Ca(2+) seems to adequately capture short-term changes in firing frequency seen in biological data, internal diffusion of Ca(2+) appears to be necessary for capturing longer term frequency changes. © 2014 Elsevier B.V. All rights reserved.

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity.

PubMed

Lin, I-Chun; Xing, Dajun; Shapley, Robert

2012-12-01

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.

Importance of the cutoff value in the quadratic adaptive integrate-and-fire model.

PubMed

Touboul, Jonathan

2009-08-01

The quadratic adaptive integrate-and-fire model (Izhikevich, 2003 , 2007 ) is able to reproduce various firing patterns of cortical neurons and is widely used in large-scale simulations of neural networks. This model describes the dynamics of the membrane potential by a differential equation that is quadratic in the voltage, coupled to a second equation for adaptation. Integration is stopped during the rise phase of a spike at a voltage cutoff value V(c) or when it blows up. Subsequently the membrane potential is reset, and the adaptation variable is increased by a fixed amount. We show in this note that in the absence of a cutoff value, not only the voltage but also the adaptation variable diverges in finite time during spike generation in the quadratic model. The divergence of the adaptation variable makes the system very sensitive to the cutoff: changing V(c) can dramatically alter the spike patterns. Furthermore, from a computational viewpoint, the divergence of the adaptation variable implies that the time steps for numerical simulation need to be small and adaptive. However, divergence of the adaptation variable does not occur for the quartic model (Touboul, 2008 ) and the adaptive exponential integrate-and-fire model (Brette & Gerstner, 2005 ). Hence, these models are robust to changes in the cutoff value.

Oscillation-Induced Signal Transmission and Gating in Neural Circuits

PubMed Central

Jahnke, Sven; Memmesheimer, Raoul-Martin; Timme, Marc

2014-01-01

Reliable signal transmission constitutes a key requirement for neural circuit function. The propagation of synchronous pulse packets through recurrent circuits is hypothesized to be one robust form of signal transmission and has been extensively studied in computational and theoretical works. Yet, although external or internally generated oscillations are ubiquitous across neural systems, their influence on such signal propagation is unclear. Here we systematically investigate the impact of oscillations on propagating synchrony. We find that for standard, additive couplings and a net excitatory effect of oscillations, robust propagation of synchrony is enabled in less prominent feed-forward structures than in systems without oscillations. In the presence of non-additive coupling (as mediated by fast dendritic spikes), even balanced oscillatory inputs may enable robust propagation. Here, emerging resonances create complex locking patterns between oscillations and spike synchrony. Interestingly, these resonances make the circuits capable of selecting specific pathways for signal transmission. Oscillations may thus promote reliable transmission and, in co-action with dendritic nonlinearities, provide a mechanism for information processing by selectively gating and routing of signals. Our results are of particular interest for the interpretation of sharp wave/ripple complexes in the hippocampus, where previously learned spike patterns are replayed in conjunction with global high-frequency oscillations. We suggest that the oscillations may serve to stabilize the replay. PMID:25503492

NeuroCa: integrated framework for systematic analysis of spatiotemporal neuronal activity patterns from large-scale optical recording data

PubMed Central

Jang, Min Jee; Nam, Yoonkey

2015-01-01

Abstract. Optical recording facilitates monitoring the activity of a large neural network at the cellular scale, but the analysis and interpretation of the collected data remain challenging. Here, we present a MATLAB-based toolbox, named NeuroCa, for the automated processing and quantitative analysis of large-scale calcium imaging data. Our tool includes several computational algorithms to extract the calcium spike trains of individual neurons from the calcium imaging data in an automatic fashion. Two algorithms were developed to decompose the imaging data into the activity of individual cells and subsequently detect calcium spikes from each neuronal signal. Applying our method to dense networks in dissociated cultures, we were able to obtain the calcium spike trains of ∼1000 neurons in a few minutes. Further analyses using these data permitted the quantification of neuronal responses to chemical stimuli as well as functional mapping of spatiotemporal patterns in neuronal firing within the spontaneous, synchronous activity of a large network. These results demonstrate that our method not only automates time-consuming, labor-intensive tasks in the analysis of neural data obtained using optical recording techniques but also provides a systematic way to visualize and quantify the collective dynamics of a network in terms of its cellular elements. PMID:26229973

Stimulus-induced dissociation of neuronal firing rates and local field potential gamma power and its relationship to the blood oxygen level-dependent signal in macaque primary visual cortex

PubMed Central

Bartolo, M J; Gieselmann, M A; Vuksanovic, V; Hunter, D; Sun, L; Chen, X; Delicato, L S; Thiele, A

2011-01-01

The functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) signal is regularly used to assign neuronal activity to cognitive function. Recent analyses have shown that the local field potential (LFP) gamma power is a better predictor of the fMRI BOLD signal than spiking activity. However, LFP gamma power and spiking activity are usually correlated, clouding the analysis of the neural basis of the BOLD signal. We show that changes in LFP gamma power and spiking activity in the primary visual cortex (V1) of the awake primate can be dissociated by using grating and plaid pattern stimuli, which differentially engage surround suppression and cross-orientation inhibition/facilitation within and between cortical columns. Grating presentation yielded substantial V1 LFP gamma frequency oscillations and significant multi-unit activity. Plaid pattern presentation significantly reduced the LFP gamma power while increasing population multi-unit activity. The fMRI BOLD activity followed the LFP gamma power changes, not the multi-unit activity. Inference of neuronal activity from the fMRI BOLD signal thus requires detailed a priori knowledge of how different stimuli or tasks activate the cortical network. PMID:22081989

Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture.

PubMed

Gholami Doborjeh, Zohreh; Kasabov, Nikola; Gholami Doborjeh, Maryam; Sumich, Alexander

2018-06-11

Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network (SNN) approach that reveals the complexity of peri-perceptual processes of familiarity. The method is applied to data from 20 participants viewing familiar and unfamiliar logos. The results support the potential of SNN models as novel tools in the exploration of peri-perceptual mechanisms that respond differentially to familiar and unfamiliar stimuli. Specifically, the activation pattern of the time-locked response identified by the proposed SNN model at approximately 200 milliseconds post-stimulus suggests greater connectivity and more widespread dynamic spatio-temporal patterns for familiar than unfamiliar logos. The proposed SNN approach can be applied to study other peri-perceptual or perceptual brain processes in cognitive and computational neuroscience.

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

PubMed

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-12-24

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

[Determination of four bisphenolic compounds in drinking water by liquid chromatography-tandem mass spectrometry].

PubMed

Hu, Xiaojian; Zhang, Haijing; Wang, Xiaohong; Ding, Changming; Lin, Shaobin

2015-05-01

To simultaneously determine the four bisphenolic compounds (bisphenol F, bisphenol A, tetrachlorobisphenol A and tetrabromobisphenol A) in drinking water by liquid chromatography tandem mass spectrometry. 200 ml water sample was extracted by solid-phase extraction, eluted with methanol and analyzed by liquid chromatography tandem mass spectrometry under the MRM mode. The separation was carried out on a T3 column (2.1 mm x 150 mm, 3 μm). The limits of detection for the four bisphenolic compounds were in the range of 0.20 - 5.5 ng/L. The mean recoveries at the two spiked levels were 87.1% - 109.0% with the intra-day precision between 6.3% - 12.4% and inter-day precision between 4.5% - 15.4%. The method was applied for determination of 15 water samples. The method was sensitive, precise and accurate.

Google Searches for "Cheap Cigarettes" Spike at Tax Increases: Evidence from an Algorithm to Detect Spikes in Time Series Data.

PubMed

Caputi, Theodore L

2018-05-03

Online cigarette dealers have lower prices than brick-and-mortar retailers and advertise tax-free status.1-8 Previous studies show smokers search out these online alternatives at the time of a cigarette tax increase.9,10 However, these studies rely upon researchers' decision to consider a specific date and preclude the possibility that researchers focus on the wrong date. The purpose of this study is to introduce an unbiased methodology to the field of observing search patterns and to use this methodology to determine whether smokers search Google for "cheap cigarettes" at cigarette tax increases and, if so, whether the increased level of searches persists. Publicly available data from Google Trends is used to observe standardized search volumes for the term, "cheap cigarettes". Seasonal Hybrid Extreme Studentized Deviate and E-Divisive with Means tests were performed to observe spikes and mean level shifts in search volume. Of the twelve cigarette tax increases studied, ten showed spikes in searches for "cheap cigarettes" within two weeks of the tax increase. However, the mean level shifts did not occur for any cigarette tax increase. Searches for "cheap cigarettes" spike around the time of a cigarette tax increase, but the mean level of searches does not shift in response to a tax increase. The SHESD and EDM tests are unbiased methodologies that can be used to identify spikes and mean level shifts in time series data without an a priori date to be studied. SHESD and EDM affirm spikes in interest are related to tax increases. • Applies improved statistical techniques (SHESD and EDM) to Google search data related to cigarettes, reducing bias and increasing power • Contributes to the body of evidence that state and federal tax increases are associated with spikes in searches for cheap cigarettes and may be good dates for increased online health messaging related to tobacco.

Spike Train Similarity Space (SSIMS) Method Detects Effects of Obstacle Proximity and Experience on Temporal Patterning of Bat Biosonar

PubMed Central

Accomando, Alyssa W.; Vargas-Irwin, Carlos E.; Simmons, James A.

2018-01-01

Bats emit biosonar pulses in complex temporal patterns that change to accommodate dynamic surroundings. Efforts to quantify these patterns have included analyses of inter-pulse intervals, sonar sound groups, and changes in individual signal parameters such as duration or frequency. Here, the similarity in temporal structure between trains of biosonar pulses is assessed. The spike train similarity space (SSIMS) algorithm, originally designed for neural activity pattern analysis, was applied to determine which features of the environment influence temporal patterning of pulses emitted by flying big brown bats, Eptesicus fuscus. In these laboratory experiments, bats flew down a flight corridor through an obstacle array. The corridor varied in width (100, 70, or 40 cm) and shape (straight or curved). Using a relational point-process framework, SSIMS was able to discriminate between echolocation call sequences recorded from flights in each of the corridor widths. SSIMS was also able to tell the difference between pulse trains recorded during flights where corridor shape through the obstacle array matched the previous trials (fixed, or expected) as opposed to those recorded from flights with randomized corridor shape (variable, or unexpected), but only for the flight path shape in which the bats had previous training. The results show that experience influences the temporal patterns with which bats emit their echolocation calls. It is demonstrated that obstacle proximity to the bat affects call patterns more dramatically than flight path shape. PMID:29472848

Spike Train Similarity Space (SSIMS) Method Detects Effects of Obstacle Proximity and Experience on Temporal Patterning of Bat Biosonar.

PubMed

Accomando, Alyssa W; Vargas-Irwin, Carlos E; Simmons, James A

2018-01-01

Bats emit biosonar pulses in complex temporal patterns that change to accommodate dynamic surroundings. Efforts to quantify these patterns have included analyses of inter-pulse intervals, sonar sound groups, and changes in individual signal parameters such as duration or frequency. Here, the similarity in temporal structure between trains of biosonar pulses is assessed. The spike train similarity space (SSIMS) algorithm, originally designed for neural activity pattern analysis, was applied to determine which features of the environment influence temporal patterning of pulses emitted by flying big brown bats, Eptesicus fuscus . In these laboratory experiments, bats flew down a flight corridor through an obstacle array. The corridor varied in width (100, 70, or 40 cm) and shape (straight or curved). Using a relational point-process framework, SSIMS was able to discriminate between echolocation call sequences recorded from flights in each of the corridor widths. SSIMS was also able to tell the difference between pulse trains recorded during flights where corridor shape through the obstacle array matched the previous trials (fixed, or expected) as opposed to those recorded from flights with randomized corridor shape (variable, or unexpected), but only for the flight path shape in which the bats had previous training. The results show that experience influences the temporal patterns with which bats emit their echolocation calls. It is demonstrated that obstacle proximity to the bat affects call patterns more dramatically than flight path shape.

Discrimination of communication vocalizations by single neurons and groups of neurons in the auditory midbrain.

PubMed

Schneider, David M; Woolley, Sarah M N

2010-06-01

Many social animals including songbirds use communication vocalizations for individual recognition. The perception of vocalizations depends on the encoding of complex sounds by neurons in the ascending auditory system, each of which is tuned to a particular subset of acoustic features. Here, we examined how well the responses of single auditory neurons could be used to discriminate among bird songs and we compared discriminability to spectrotemporal tuning. We then used biologically realistic models of pooled neural responses to test whether the responses of groups of neurons discriminated among songs better than the responses of single neurons and whether discrimination by groups of neurons was related to spectrotemporal tuning and trial-to-trial response variability. The responses of single auditory midbrain neurons could be used to discriminate among vocalizations with a wide range of abilities, ranging from chance to 100%. The ability to discriminate among songs using single neuron responses was not correlated with spectrotemporal tuning. Pooling the responses of pairs of neurons generally led to better discrimination than the average of the two inputs and the most discriminating input. Pooling the responses of three to five single neurons continued to improve neural discrimination. The increase in discriminability was largest for groups of neurons with similar spectrotemporal tuning. Further, we found that groups of neurons with correlated spike trains achieved the largest gains in discriminability. We simulated neurons with varying levels of temporal precision and measured the discriminability of responses from single simulated neurons and groups of simulated neurons. Simulated neurons with biologically observed levels of temporal precision benefited more from pooling correlated inputs than did neurons with highly precise or imprecise spike trains. These findings suggest that pooling correlated neural responses with the levels of precision observed in the auditory midbrain increases neural discrimination of complex vocalizations.

Supercritical fluid extraction of 13-cis retinoic acid and its photoisomers from selected pharmaceutical dosage forms.

PubMed

Simmons, B R; Chukwumerije, O; Stewart, J T

1997-11-01

13-Cis retinoic acid (Accutane) was extracted from a cream, gel, capsule and beadlet dosage from using supercritical carbon dioxide modified with 5% methanol as the mobile phase. The pump pressure and the extraction chamber and restrictor temperature were experimentally optimized at 325 atm and 45 degrees C, respectively. A 2.5-min static and 5-min dynamic extraction time were used. The supercritical fluid extraction (SFE) eluent was trapped in methanol, injected into the high-performance liquid chromatographic (HPLC) system, and quantitated by ultraviolet detection at 360 nm. Application of the SFE method to spiked placebo dosage forms gave 13-cis retinoic acid recoveries of 98.8, 98.9, 98.8 and 100% for the cream, gel, capsule and beadlet, respectively, with R.S.D.s in the range 0.6-0.9% (n = 4). Inter-day percent error and precision of the extraction were 1.1-2.0 and 0.2-2.4% (n = 3), respectively, and intra-day percent error and precision were 1.0-3.0 and 0.3-2.1% (n = 8), respectively. Percent error and precision data for spiked celite samples in the 0.05-1.0 microgram ml-1 range were 0.59-4.75 and 1.8-2.1% (n = 3), respectively. The extraction method was applied to commercial 13-cis retinoic acid dosage forms and the results compared to unextracted samples. Linear regression analysis of concentration versus peak height gave a correlation coefficient of 0.9991 with a slope of 7.468 and a y-intercept of 0.1923. The percent error and precision data were 1.3-5.3 and 0.2-1.5% (n = 4), respectively. The photoisomers of 13-cis retinoic acid were also extracted with the method and recoveries of 90.4-92.4% with R.S.D.s of 1.5-3.4% were obtained (n = 4).

A pseudo-equilibrium thermodynamic model of information processing in nonlinear brain dynamics.

PubMed

Freeman, Walter J

2008-01-01

Computational models of brain dynamics fall short of performance in speed and robustness of pattern recognition in detecting minute but highly significant pattern fragments. A novel model employs the properties of thermodynamic systems operating far from equilibrium, which is analyzed by linearization near adaptive operating points using root locus techniques. Such systems construct order by dissipating energy. Reinforcement learning of conditioned stimuli creates a landscape of attractors and their basins in each sensory cortex by forming nerve cell assemblies in cortical connectivity. Retrieval of a selected category of stored knowledge is by a phase transition that is induced by a conditioned stimulus, and that leads to pattern self-organization. Near self-regulated criticality the cortical background activity displays aperiodic null spikes at which analytic amplitude nears zero, and which constitute a form of Rayleigh noise. Phase transitions in recognition and recall are initiated at null spikes in the presence of an input signal, owing to the high signal-to-noise ratio that facilitates capture of cortex by an attractor, even by very weak activity that is typically evoked by a conditioned stimulus.

Coordinated prefrontal-hippocampal activity and navigation strategy-related prefrontal firing during spatial memory formation.

PubMed

Negrón-Oyarzo, Ignacio; Espinosa, Nelson; Aguilar, Marcelo; Fuenzalida, Marco; Aboitiz, Francisco; Fuentealba, Pablo

2018-06-18

Learning the location of relevant places in the environment is crucial for survival. Such capacity is supported by a distributed network comprising the prefrontal cortex and hippocampus, yet it is not fully understood how these structures cooperate during spatial reference memory formation. Hence, we examined neural activity in the prefrontal-hippocampal circuit in mice during acquisition of spatial reference memory. We found that interregional oscillatory coupling increased with learning, specifically in the slow-gamma frequency (20 to 40 Hz) band during spatial navigation. In addition, mice used both spatial and nonspatial strategies to navigate and solve the task, yet prefrontal neuronal spiking and oscillatory phase coupling were selectively enhanced in the spatial navigation strategy. Lastly, a representation of the behavioral goal emerged in prefrontal spiking patterns exclusively in the spatial navigation strategy. These results suggest that reference memory formation is supported by enhanced cortical connectivity and evolving prefrontal spiking representations of behavioral goals.

Neural signal registration and analysis of axons grown in microchannels

NASA Astrophysics Data System (ADS)

Pigareva, Y.; Malishev, E.; Gladkov, A.; Kolpakov, V.; Bukatin, A.; Mukhina, I.; Kazantsev, V.; Pimashkin, A.

2016-08-01

Registration of neuronal bioelectrical signals remains one of the main physical tools to study fundamental mechanisms of signal processing in the brain. Neurons generate spiking patterns which propagate through complex map of neural network connectivity. Extracellular recording of isolated axons grown in microchannels provides amplification of the signal for detailed study of spike propagation. In this study we used neuronal hippocampal cultures grown in microfluidic devices combined with microelectrode arrays to investigate a changes of electrical activity during neural network development. We found that after 5 days in vitro after culture plating the spiking activity appears first in microchannels and on the next 2-3 days appears on the electrodes of overall neural network. We conclude that such approach provides a convenient method to study neural signal processing and functional structure development on a single cell and network level of the neuronal culture.

A Neural Code That Is Isometric to Vocal Output and Correlates with Its Sensory Consequences

PubMed Central

Vyssotski, Alexei L.; Stepien, Anna E.; Keller, Georg B.; Hahnloser, Richard H. R.

2016-01-01

What cortical inputs are provided to motor control areas while they drive complex learned behaviors? We study this question in the nucleus interface of the nidopallium (NIf), which is required for normal birdsong production and provides the main source of auditory input to HVC, the driver of adult song. In juvenile and adult zebra finches, we find that spikes in NIf projection neurons precede vocalizations by several tens of milliseconds and are insensitive to distortions of auditory feedback. We identify a local isometry between NIf output and vocalizations: quasi-identical notes produced in different syllables are preceded by highly similar NIf spike patterns. NIf multiunit firing during song precedes responses in auditory cortical neurons by about 50 ms, revealing delayed congruence between NIf spiking and a neural representation of auditory feedback. Our findings suggest that NIf codes for imminent acoustic events within vocal performance. PMID:27723764

Biomorphic networks: approach to invariant feature extraction and segmentation for ATR

NASA Astrophysics Data System (ADS)

Baek, Andrew; Farhat, Nabil H.

1998-10-01

Invariant features in two dimensional binary images are extracted in a single layer network of locally coupled spiking (pulsating) model neurons with prescribed synapto-dendritic response. The feature vector for an image is represented as invariant structure in the aggregate histogram of interspike intervals obtained by computing time intervals between successive spikes produced from each neuron over a given period of time and combining such intervals from all neurons in the network into a histogram. Simulation results show that the feature vectors are more pattern-specific and invariant under translation, rotation, and change in scale or intensity than achieved in earlier work. We also describe an application of such networks to segmentation of line (edge-enhanced or silhouette) images. The biomorphic spiking network's capabilities in segmentation and invariant feature extraction may prove to be, when they are combined, valuable in Automated Target Recognition (ATR) and other automated object recognition systems.

Low excitatory innervation balances high intrinsic excitability of immature dentate neurons

PubMed Central

Dieni, Cristina V.; Panichi, Roberto; Aimone, James B.; Kuo, Chay T.; Wadiche, Jacques I.; Overstreet-Wadiche, Linda

2016-01-01

Persistent neurogenesis in the dentate gyrus produces immature neurons with high intrinsic excitability and low levels of inhibition that are predicted to be more broadly responsive to afferent activity than mature neurons. Mounting evidence suggests that these immature neurons are necessary for generating distinct neural representations of similar contexts, but it is unclear how broadly responsive neurons help distinguish between similar patterns of afferent activity. Here we show that stimulation of the entorhinal cortex in mouse brain slices paradoxically generates spiking of mature neurons in the absence of immature neuron spiking. Immature neurons with high intrinsic excitability fail to spike due to insufficient excitatory drive that results from low innervation rather than silent synapses or low release probability. Our results suggest that low synaptic connectivity prevents immature neurons from responding broadly to cortical activity, potentially enabling excitable immature neurons to contribute to sparse and orthogonal dentate representations. PMID:27095423

Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

PubMed

Sarkar, Sankho Turjo; Bhondekar, Amol P; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod

2015-11-01

The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Accuracy in Rietveld quantitative phase analysis: a comparative study of strictly monochromatic Mo and Cu radiations.

PubMed

León-Reina, L; García-Maté, M; Álvarez-Pinazo, G; Santacruz, I; Vallcorba, O; De la Torre, A G; Aranda, M A G

2016-06-01

This study reports 78 Rietveld quantitative phase analyses using Cu  K α 1 , Mo  K α 1 and synchrotron radiations. Synchrotron powder diffraction has been used to validate the most challenging analyses. From the results for three series with increasing contents of an analyte (an inorganic crystalline phase, an organic crystalline phase and a glass), it is inferred that Rietveld analyses from high-energy Mo  K α 1 radiation have slightly better accuracies than those obtained from Cu  K α 1 radiation. This behaviour has been established from the results of the calibration graphics obtained through the spiking method and also from Kullback-Leibler distance statistic studies. This outcome is explained, in spite of the lower diffraction power for Mo radiation when compared to Cu radiation, as arising because of the larger volume tested with Mo and also because higher energy allows one to record patterns with fewer systematic errors. The limit of detection (LoD) and limit of quantification (LoQ) have also been established for the studied series. For similar recording times, the LoDs in Cu patterns, ∼0.2 wt%, are slightly lower than those derived from Mo patterns, ∼0.3 wt%. The LoQ for a well crystallized inorganic phase using laboratory powder diffraction was established to be close to 0.10 wt% in stable fits with good precision. However, the accuracy of these analyses was poor with relative errors near to 100%. Only contents higher than 1.0 wt% yielded analyses with relative errors lower than 20%.

Selective and Efficient Neural Coding of Communication Signals Depends on Early Acoustic and Social Environment

PubMed Central

Amin, Noopur; Gastpar, Michael; Theunissen, Frédéric E.

2013-01-01

Previous research has shown that postnatal exposure to simple, synthetic sounds can affect the sound representation in the auditory cortex as reflected by changes in the tonotopic map or other relatively simple tuning properties, such as AM tuning. However, their functional implications for neural processing in the generation of ethologically-based perception remain unexplored. Here we examined the effects of noise-rearing and social isolation on the neural processing of communication sounds such as species-specific song, in the primary auditory cortex analog of adult zebra finches. Our electrophysiological recordings reveal that neural tuning to simple frequency-based synthetic sounds is initially established in all the laminae independent of patterned acoustic experience; however, we provide the first evidence that early exposure to patterned sound statistics, such as those found in native sounds, is required for the subsequent emergence of neural selectivity for complex vocalizations and for shaping neural spiking precision in superficial and deep cortical laminae, and for creating efficient neural representations of song and a less redundant ensemble code in all the laminae. Our study also provides the first causal evidence for ‘sparse coding’, such that when the statistics of the stimuli were changed during rearing, as in noise-rearing, that the sparse or optimal representation for species-specific vocalizations disappeared. Taken together, these results imply that a layer-specific differential development of the auditory cortex requires patterned acoustic input, and a specialized and robust sensory representation of complex communication sounds in the auditory cortex requires a rich acoustic and social environment. PMID:23630587

Changes in the neural control of a complex motor sequence during learning

PubMed Central

Otchy, Timothy M.; Goldberg, Jesse H.; Aronov, Dmitriy; Fee, Michale S.

2011-01-01

The acquisition of complex motor sequences often proceeds through trial-and-error learning, requiring the deliberate exploration of motor actions and the concomitant evaluation of the resulting performance. Songbirds learn their song in this manner, producing highly variable vocalizations as juveniles. As the song improves, vocal variability is gradually reduced until it is all but eliminated in adult birds. In the present study we examine how the motor program underlying such a complex motor behavior evolves during learning by recording from the robust nucleus of the arcopallium (RA), a motor cortex analog brain region. In young birds, neurons in RA exhibited highly variable firing patterns that throughout development became more precise, sparse, and bursty. We further explored how the developing motor program in RA is shaped by its two main inputs: LMAN, the output nucleus of a basal ganglia-forebrain circuit, and HVC, a premotor nucleus. Pharmacological inactivation of LMAN during singing made the song-aligned firing patterns of RA neurons adultlike in their stereotypy without dramatically affecting the spike statistics or the overall firing patterns. Removing the input from HVC, on the other hand, resulted in a complete loss of stereotypy of both the song and the underlying motor program. Thus our results show that a basal ganglia-forebrain circuit drives motor exploration required for trial-and-error learning by adding variability to the developing motor program. As learning proceeds and the motor circuits mature, the relative contribution of LMAN is reduced, allowing the premotor input from HVC to drive an increasingly stereotyped song. PMID:21543758

Python for Large-Scale Electrophysiology

PubMed Central

Spacek, Martin; Blanche, Tim; Swindale, Nicholas

2008-01-01

Electrophysiology is increasingly moving towards highly parallel recording techniques which generate large data sets. We record extracellularly in vivo in cat and rat visual cortex with 54-channel silicon polytrodes, under time-locked visual stimulation, from localized neuronal populations within a cortical column. To help deal with the complexity of generating and analysing these data, we used the Python programming language to develop three software projects: one for temporally precise visual stimulus generation (“dimstim”); one for electrophysiological waveform visualization and spike sorting (“spyke”); and one for spike train and stimulus analysis (“neuropy”). All three are open source and available for download (http://swindale.ecc.ubc.ca/code). The requirements and solutions for these projects differed greatly, yet we found Python to be well suited for all three. Here we present our software as a showcase of the extensive capabilities of Python in neuroscience. PMID:19198646

Ultrafast Synaptic Events in a Chalcogenide Memristor

NASA Astrophysics Data System (ADS)

Li, Yi; Zhong, Yingpeng; Xu, Lei; Zhang, Jinjian; Xu, Xiaohua; Sun, Huajun; Miao, Xiangshui

2013-04-01

Compact and power-efficient plastic electronic synapses are of fundamental importance to overcoming the bottlenecks of developing a neuromorphic chip. Memristor is a strong contender among the various electronic synapses in existence today. However, the speeds of synaptic events are relatively slow in most attempts at emulating synapses due to the material-related mechanism. Here we revealed the intrinsic memristance of stoichiometric crystalline Ge2Sb2Te5 that originates from the charge trapping and releasing by the defects. The device resistance states, representing synaptic weights, were precisely modulated by 30 ns potentiating/depressing electrical pulses. We demonstrated four spike-timing-dependent plasticity (STDP) forms by applying programmed pre- and postsynaptic spiking pulse pairs in different time windows ranging from 50 ms down to 500 ns, the latter of which is 105 times faster than the speed of STDP in human brain. This study provides new opportunities for building ultrafast neuromorphic computing systems and surpassing Von Neumann architecture.

Ultrafast synaptic events in a chalcogenide memristor.

PubMed

Li, Yi; Zhong, Yingpeng; Xu, Lei; Zhang, Jinjian; Xu, Xiaohua; Sun, Huajun; Miao, Xiangshui

2013-01-01

Compact and power-efficient plastic electronic synapses are of fundamental importance to overcoming the bottlenecks of developing a neuromorphic chip. Memristor is a strong contender among the various electronic synapses in existence today. However, the speeds of synaptic events are relatively slow in most attempts at emulating synapses due to the material-related mechanism. Here we revealed the intrinsic memristance of stoichiometric crystalline Ge2Sb2Te5 that originates from the charge trapping and releasing by the defects. The device resistance states, representing synaptic weights, were precisely modulated by 30 ns potentiating/depressing electrical pulses. We demonstrated four spike-timing-dependent plasticity (STDP) forms by applying programmed pre- and postsynaptic spiking pulse pairs in different time windows ranging from 50 ms down to 500 ns, the latter of which is 10(5) times faster than the speed of STDP in human brain. This study provides new opportunities for building ultrafast neuromorphic computing systems and surpassing Von Neumann architecture.

Non-stationary time series modeling on caterpillars pest of palm oil for early warning system

NASA Astrophysics Data System (ADS)

Setiyowati, Susi; Nugraha, Rida F.; Mukhaiyar, Utriweni

2015-12-01

The oil palm production has an important role for the plantation and economic sector in Indonesia. One of the important problems in the cultivation of oil palm plantation is pests which causes damage to the quality of fruits. The caterpillar pest which feed palm tree's leaves will cause decline in quality of palm oil production. Early warning system is needed to minimize losses due to this pest. Here, we applied non-stationary time series modeling, especially the family of autoregressive models to predict the number of pests based on its historical data. We realized that there is some uniqueness of these pests data, i.e. the spike value that occur almost periodically. Through some simulations and case study, we obtain that the selection of constant factor has a significance influence to the model so that it can shoot the spikes value precisely.

Open source tools for the information theoretic analysis of neural data.

PubMed

Ince, Robin A A; Mazzoni, Alberto; Petersen, Rasmus S; Panzeri, Stefano

2010-01-01

The recent and rapid development of open source software tools for the analysis of neurophysiological datasets consisting of simultaneous multiple recordings of spikes, field potentials and other neural signals holds the promise for a significant advance in the standardization, transparency, quality, reproducibility and variety of techniques used to analyze neurophysiological data and for the integration of information obtained at different spatial and temporal scales. In this review we focus on recent advances in open source toolboxes for the information theoretic analysis of neural responses. We also present examples of their use to investigate the role of spike timing precision, correlations across neurons, and field potential fluctuations in the encoding of sensory information. These information toolboxes, available both in MATLAB and Python programming environments, hold the potential to enlarge the domain of application of information theory to neuroscience and to lead to new discoveries about how neurons encode and transmit information.

Anchoring historical sequences using a new source of astro-chronological tie-points

NASA Astrophysics Data System (ADS)

Dee, Michael W.; Pope, Benjamin J. S.

2016-08-01

The discovery of past spikes in atmospheric radiocarbon activity, caused by major solar energetic particle events, has opened up new possibilities for high-precision chronometry. The two spikes, or Miyake Events, have now been widely identified in tree-rings that grew in the years 775 and 994 CE. Furthermore, all other plant material that grew in these years would also have incorporated the anomalously high concentrations of radiocarbon. Crucially, some plant-based artefacts, such as papyrus documents, timber beams and linen garments, can also be allocated to specific positions within long, currently unfixed, historical sequences. Thus, Miyake Events represent a new source of tie-points that could provide the means for anchoring early chronologies to the absolute timescale. Here, we explore this possibility, outlining the most expeditious approaches, the current challenges and obstacles, and how they might best be overcome.

Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons.

PubMed

Yger, Pierre; El Boustani, Sami; Destexhe, Alain; Frégnac, Yves

2011-10-01

The relationship between the dynamics of neural networks and their patterns of connectivity is far from clear, despite its importance for understanding functional properties. Here, we have studied sparsely-connected networks of conductance-based integrate-and-fire (IF) neurons with balanced excitatory and inhibitory connections and with finite axonal propagation speed. We focused on the genesis of states with highly irregular spiking activity and synchronous firing patterns at low rates, called slow Synchronous Irregular (SI) states. In such balanced networks, we examined the "macroscopic" properties of the spiking activity, such as ensemble correlations and mean firing rates, for different intracortical connectivity profiles ranging from randomly connected networks to networks with Gaussian-distributed local connectivity. We systematically computed the distance-dependent correlations at the extracellular (spiking) and intracellular (membrane potential) levels between randomly assigned pairs of neurons. The main finding is that such properties, when they are averaged at a macroscopic scale, are invariant with respect to the different connectivity patterns, provided the excitatory-inhibitory balance is the same. In particular, the same correlation structure holds for different connectivity profiles. In addition, we examined the response of such networks to external input, and found that the correlation landscape can be modulated by the mean level of synchrony imposed by the external drive. This modulation was found again to be independent of the external connectivity profile. We conclude that first and second-order "mean-field" statistics of such networks do not depend on the details of the connectivity at a microscopic scale. This study is an encouraging step toward a mean-field description of topological neuronal networks.

Interactions between behaviorally relevant rhythms and synaptic plasticity alter coding in the piriform cortex

PubMed Central

Urban, Nathaniel N.

2012-01-01

Understanding how neural and behavioral timescales interact to influence cortical activity and stimulus coding is an important issue in sensory neuroscience. In air-breathing animals, voluntary changes in respiratory frequency alter the temporal patterning olfactory input. In the olfactory bulb, these behavioral timescales are reflected in the temporal properties of mitral/tufted (M/T) cell spike trains. As the odor information contained in these spike trains is relayed from the bulb to the cortex, interactions between presynaptic spike timing and short-term synaptic plasticity dictate how stimulus features are represented in cortical spike trains. Here we demonstrate how the timescales associated with respiratory frequency, spike timing and short-term synaptic plasticity interact to shape cortical responses. Specifically, we quantified the timescales of short-term synaptic facilitation and depression at excitatory synapses between bulbar M/T cells and cortical neurons in slices of mouse olfactory cortex. We then used these results to generate simulated M/T population synaptic currents that were injected into real cortical neurons. M/T population inputs were modulated at frequencies consistent with passive respiration or active sniffing. We show how the differential recruitment of short-term plasticity at breathing versus sniffing frequencies alters cortical spike responses. For inputs at sniffing frequencies, cortical neurons linearly encoded increases in presynaptic firing rates with increased phase locked, firing rates. In contrast, at passive breathing frequencies, cortical responses saturated with changes in presynaptic rate. Our results suggest that changes in respiratory behavior can gate the transfer of stimulus information between the olfactory bulb and cortex. PMID:22553016

Spike Timing Matters in Novel Neuronal Code Involved in Vibrotactile Frequency Perception.

PubMed

Birznieks, Ingvars; Vickery, Richard M

2017-05-22

Skin vibrations sensed by tactile receptors contribute significantly to the perception of object properties during tactile exploration [1-4] and to sensorimotor control during object manipulation [5]. Sustained low-frequency skin vibration (<60 Hz) evokes a distinct tactile sensation referred to as flutter whose frequency can be clearly perceived [6]. How afferent spiking activity translates into the perception of frequency is still unknown. Measures based on mean spike rates of neurons in the primary somatosensory cortex are sufficient to explain performance in some frequency discrimination tasks [7-11]; however, there is emerging evidence that stimuli can be distinguished based also on temporal features of neural activity [12, 13]. Our study's advance is to demonstrate that temporal features are fundamental for vibrotactile frequency perception. Pulsatile mechanical stimuli were used to elicit specified temporal spike train patterns in tactile afferents, and subsequently psychophysical methods were employed to characterize human frequency perception. Remarkably, the most salient temporal feature determining vibrotactile frequency was not the underlying periodicity but, rather, the duration of the silent gap between successive bursts of neural activity. This burst gap code for frequency represents a previously unknown form of neural coding in the tactile sensory system, which parallels auditory pitch perception mechanisms based on purely temporal information where longer inter-pulse intervals receive higher perceptual weights than short intervals [14]. Our study also demonstrates that human perception of stimuli can be determined exclusively by temporal features of spike trains independent of the mean spike rate and without contribution from population response factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Soil fluoride spiking effects on olive trees (Olea europaea L. cv. Chemlali).

PubMed

Zouari, M; Ben Ahmed, C; Fourati, R; Delmail, D; Ben Rouina, B; Labrousse, P; Ben Abdallah, F

2014-10-01

A pot experiment under open air conditions was carried out to investigate the uptake, accumulation and toxicity effects of fluoride in olive trees (Olea europaea L.) grown in a soil spiked with inorganic sodium fluoride (NaF). Six different levels (0, 20, 40, 60, 80 and 100mM NaF) of soil spiking were applied through NaF to irrigation water. At the end of the experiment, total fluoride content in soil was 20 and 1770mgFkg(-1) soil in control and 100mM NaF treatments, respectively. The comparative distribution of fluoride partitioning among the different olive tree parts showed that the roots accumulated the most fluoride and olive fruits were minimally affected by soil NaF spiking as they had the lowest fluoride content. In fact, total fluoride concentration varied between 12 and 1070µgFg(-1) in roots, between 9 and 570µgFg(-1) in shoots, between 12 and 290µgFg(-1) in leaves, and between 10 and 29µgFg(-1) in fruits, respectively for control and 100mM NaF treatments. Indeed, the fluoride accumulation pattern showed the following distribution: roots>shoots>leaves>fruits. On the other hand, fluoride toxicity symptoms such as leaf necrosis and leaf drop appeared only in highly spiked soils (60, 80 and 100mM NaF). Copyright © 2014 Elsevier Inc. All rights reserved.