Science.gov

Sample records for reduces spike timing

  1. Reliability of Spike Timing in Neocortical Neurons

    NASA Astrophysics Data System (ADS)

    Mainen, Zachary F.; Sejnowski, Terrence J.

    1995-06-01

    It is not known whether the variability of neural activity in the cerebral cortex carries information or reflects noisy underlying mechanisms. In an examination of the reliability of spike generation using recordings from neurons in rat neocortical slices, the precision of spike timing was found to depend on stimulus transients. Constant stimuli led to imprecise spike trains, whereas stimuli with fluctuations resembling synaptic activity produced spike trains with timing reproducible to less than 1 millisecond. These data suggest a low intrinsic noise level in spike generation, which could allow cortical neurons to accurately transform synaptic input into spike sequences, supporting a possible role for spike timing in the processing of cortical information by the neocortex.

  2. Motor control by precisely timed spike patterns.

    PubMed

    Srivastava, Kyle H; Holmes, Caroline M; Vellema, Michiel; Pack, Andrea R; Elemans, Coen P H; Nemenman, Ilya; Sober, Samuel J

    2017-01-31

    A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence that the nervous system uses millisecond-scale variations in the timing of spikes within multispike patterns to control a vertebrate behavior-namely, respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision.

  3. Spiking neuron computation with the time machine.

    PubMed

    Garg, Vaibhav; Shekhar, Ravi; Harris, John G

    2012-04-01

    The Time Machine (TM) is a spike-based computation architecture that represents synaptic weights in time. This choice of weight representation allows the use of virtual synapses, providing an excellent tradeoff in terms of flexibility, arbitrary weight connections and hardware usage compared to dedicated synapse architectures. The TM supports an arbitrary number of synapses and is limited only by the number of simultaneously active synapses to each neuron. SpikeSim, a behavioral hardware simulator for the architecture, is described along with example algorithms for edge detection and objection recognition. The TM can implement traditional spike-based processing as well as recently developed time mode operations where step functions serve as the input and output of each neuron block. A custom hybrid digital/analog implementation and a fully digital realization of the TM are discussed. An analog chip with 32 neurons, 1024 synapses and an address event representation (AER) block has been fabricated in 0.5 μm technology. A fully digital field-programmable gate array (FPGA)-based implementation of the architecture has 6,144 neurons and 100,352 simultaneously active synapses. Both implementations utilize a digital controller for routing spikes that can process up to 34 million synapses per second.

  4. Timing Intervals Using Population Synchrony and Spike Timing Dependent Plasticity

    PubMed Central

    Xu, Wei; Baker, Stuart N.

    2016-01-01

    We present a computational model by which ensembles of regularly spiking neurons can encode different time intervals through synchronous firing. We show that a neuron responding to a large population of convergent inputs has the potential to learn to produce an appropriately-timed output via spike-time dependent plasticity. We explain why temporal variability of this population synchrony increases with increasing time intervals. We also show that the scalar property of timing and its violation at short intervals can be explained by the spike-wise accumulation of jitter in the inter-spike intervals of timing neurons. We explore how the challenge of encoding longer time intervals can be overcome and conclude that this may involve a switch to a different population of neurons with lower firing rate, with the added effect of producing an earlier bias in response. Experimental data on human timing performance show features in agreement with the model's output. PMID:27990109

  5. Millisecond precision spike timing shapes tactile perception.

    PubMed

    Mackevicius, Emily L; Best, Matthew D; Saal, Hannes P; Bensmaia, Sliman J

    2012-10-31

    In primates, the sense of touch has traditionally been considered to be a spatial modality, drawing an analogy to the visual system. In this view, stimuli are encoded in spatial patterns of activity over the sheet of receptors embedded in the skin. We propose that the spatial processing mode is complemented by a temporal one. Indeed, the transduction and processing of complex, high-frequency skin vibrations have been shown to play an important role in tactile texture perception, and the frequency composition of vibrations shapes the evoked percept. Mechanoreceptive afferents innervating the glabrous skin exhibit temporal patterning in their responses, but the importance and behavioral relevance of spike timing, particularly for naturalistic stimuli, remains to be elucidated. Based on neurophysiological recordings from Rhesus macaques, we show that spike timing conveys information about the frequency composition of skin vibrations, both for individual afferents and for afferent populations, and that the temporal fidelity varies across afferent class. Furthermore, the perception of skin vibrations, measured in human subjects, is better predicted when spike timing is taken into account, and the resolution that predicts perception best matches the optimal resolution of the respective afferent classes. In light of these results, the peripheral representation of complex skin vibrations draws a powerful analogy with the auditory and vibrissal systems.

  6. Millisecond Precision Spike Timing Shapes Tactile Perception

    PubMed Central

    Mackevicius, Emily L.; Best, Matthew D.; Saal, Hannes P.

    2012-01-01

    In primates, the sense of touch has traditionally been considered to be a spatial modality, drawing an analogy to the visual system. In this view, stimuli are encoded in spatial patterns of activity over the sheet of receptors embedded in the skin. We propose that the spatial processing mode is complemented by a temporal one. Indeed, the transduction and processing of complex, high-frequency skin vibrations have been shown to play an important role in tactile texture perception, and the frequency composition of vibrations shapes the evoked percept. Mechanoreceptive afferents innervating the glabrous skin exhibit temporal patterning in their responses, but the importance and behavioral relevance of spike timing, particularly for naturalistic stimuli, remains to be elucidated. Based on neurophysiological recordings from Rhesus macaques, we show that spike timing conveys information about the frequency composition of skin vibrations, both for individual afferents and for afferent populations, and that the temporal fidelity varies across afferent class. Furthermore, the perception of skin vibrations, measured in human subjects, is better predicted when spike timing is taken into account, and the resolution that predicts perception best matches the optimal resolution of the respective afferent classes. In light of these results, the peripheral representation of complex skin vibrations draws a powerful analogy with the auditory and vibrissal systems. PMID:23115169

  7. Presynaptic spike broadening reduces junctional potential amplitude.

    PubMed

    Spencer, A N; Przysiezniak, J; Acosta-Urquidi, J; Basarsky, T A

    1989-08-24

    Presynaptic modulation of action potential duration may regulate synaptic transmission in both vertebrates and invertebrates. Such synaptic plasticity is brought about by modifications to membrane currents at presynaptic release sites, which, in turn, lead to changes in the concentration of cytosolic calcium available for mediating transmitter release. The 'primitive' neuromuscular junction of the jellyfish Polyorchis penicillatus is a useful model of presynaptic modulation. In this study, we show that the durations of action potentials in the motor neurons of this jellyfish are negatively correlated with the amplitude of excitatory junctional potentials. We present data from in vitro voltage-clamp experiments showing that short duration voltage spikes, which elicit large excitatory junctional potentials in vivo, produce larger and briefer calcium currents than do long duration action potentials, which elicit small excitatory junctional potentials.

  8. Spike timing and information transmission at retinogeniculate synapses

    PubMed Central

    Rathbun, Daniel L.; Warland, David K.; Usrey, W. Martin

    2010-01-01

    This study examines the rules governing the transfer of spikes between the retina and LGN with the goal of determining whether the most informative retinal spikes preferentially drive LGN responses and what role spike timing plays in the process. By recording from monosynaptically-connected pairs of retinal ganglion cells and LGN neurons in vivo in the cat, we show that relayed spikes are more likely than non-relayed spikes to be evoked by stimuli that match the recorded cells’ receptive fields and that an interspike interval (ISI)-based mechanism contributes to the process. Relayed spikes are also more reliable in their timing and number where they often achieve the theoretical limit of minimum variance. As a result, relayed spikes carry more visual information per spike. Based on these results, we conclude that retinogeniculate processing increases sparseness in the neural code by selectively relaying the highest fidelity spikes to the visual cortex. PMID:20943897

  9. 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

  10. 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.

  11. Relationships between spike-free local field potentials and spike timing in human temporal cortex

    PubMed Central

    Zanos, Theodoros P.; Marmarelis, Vasilis Z.; Ojemann, George A.; Fetz, Eberhard E.

    2012-01-01

    Intracortical recordings comprise both fast events, action potentials (APs), and slower events, known as local field potentials (LFPs). Although it is believed that LFPs mostly reflect local synaptic activity, it is unclear which of their signal components are most closely related to synaptic potentials and would therefore be causally related to the occurrence of individual APs. This issue is complicated by the significant contribution from AP waveforms, especially at higher LFP frequencies. In recordings of single-cell activity and LFPs from the human temporal cortex, we computed quantitative, nonlinear, causal dynamic models for the prediction of AP timing from LFPs, at millisecond resolution, before and after removing AP contributions to the LFP. In many cases, the timing of a significant number of single APs could be predicted from spike-free LFPs at different frequencies. Not surprisingly, model performance was superior when spikes were not removed. Cells whose activity was predicted by the spike-free LFP models generally fell into one of two groups: in the first group, neuronal spike activity was associated with specific phases of low LFP frequencies, lower spike activity at high LFP frequencies, and a stronger linear component in the spike-LFP model; in the second group, neuronal spike activity was associated with larger amplitude of high LFP frequencies, less frequent phase locking, and a stronger nonlinear model component. Spike timing in the first group was better predicted by the sign and level of the LFP preceding the spike, whereas spike timing in the second group was better predicted by LFP power during a certain time window before the spike. PMID:22157112

  12. IMPORTANCE OF SPIKE TIMING IN TOUCH: AN ANALOGY WITH HEARING?

    PubMed Central

    Saal, Hannes P.; Wang, Xiaoqin; Bensmaia, Sliman J.

    2017-01-01

    Touch is often conceived as a spatial sense akin to vision. However, touch also involves the transduction and processing of signals that vary rapidly over time, inviting comparisons with hearing. In both sensory systems, first order afferents produce spiking responses that are temporally precise and the timing of their responses carries stimulus information. The precision and informativeness of spike timing in the two systems invites the possibility that both implement similar mechanisms to extract behaviorally relevant information from these precisely timed responses. Here, we explore the putative roles of spike timing in touch and hearing and discuss common mechanisms that may be involved in processing temporal spiking patterns. PMID:27504741

  13. Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

    PubMed Central

    Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

    2016-01-01

    In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision. PMID:27857680

  14. Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

    PubMed

    Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

    2016-01-01

    In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

  15. Regulation of spike timing in visual cortical circuits

    PubMed Central

    Tiesinga, Paul; Fellous, Jean-Marc; Sejnowski, Terrence J.

    2010-01-01

    A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role. PMID:18200026

  16. Spike timing control in retinal prosthetic

    NASA Astrophysics Data System (ADS)

    Werblin, Frank

    2005-03-01

    To restore meaningful vision to blind patients requires a retinal prosthetic device that can generate precise spiking patterns in retinal ganglion cells. We sought to develop a stimulus protocol that could reliably elicit one ganglion cell spike for every stimulation pulse over a broad frequency range. Small tipped platinum-iridium epiretinal electrodes were used to deliver biphasic cathodal electrical stimulus pulses at frequencies ranging from 10 to 125 Hz. We measured spiking responses with on-cell patch clamp from ganglion cells in the flat mount rabbit retina, identified by light response and morphology. Single electrical 30 pA cathodal pulses of 1 msec duration elicited both by direct electrical activation of ganglion cells and synaptic excitation and inhibition. Direct activation elicited a single spike that followed the onset of the cathodic pulse by about 100 μsec; presynaptic activation typically elicited multiple spikes which began after 10 msec and could persist for more than 50 ms depending on pulse amplitude levels. Limiting the pulse duration to 100 μsec eliminated all presynaptic activity: only ganglion cells were driven. Each pulse elicited a single pike for stimulation frequencies tested from 10 to125 Hz. Our ability to elicit one spike per pulse provides many important advantages: This protocol can be used to generate temporal patterns of activity in ganglion cells with precision. We can now mimic normal light evoked responses for either transient or sustained cells, and we can modulate spike frequency to simulate changes in intensity, contrast, motion and other essential cues in the visual environment.

  17. State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.

    PubMed

    Shimazaki, Hideaki; Amari, Shun-Ichi; Brown, Emery N; Grün, Sonja

    2012-01-01

    Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods

  18. On the Analytical Solution of Firing Time for SpikeProp.

    PubMed

    de Montigny, Simon; Mâsse, Benoît R

    2016-08-24

    Error backpropagation in networks of spiking neurons (SpikeProp) shows promise for the supervised learning of temporal patterns. However, its widespread use is hindered by its computational load and occasional convergence failures. In this letter, we show that the neuronal firing time equation at the core of SpikeProp can be solved analytically using the Lambert W function, offering a marked reduction in execution time over the step-based method used in the literature. Applying this analytical method to SpikeProp, we find that training time per epoch can be reduced by 12% to 56% under different experimental conditions. Finally, this work opens the way for further investigations of SpikeProp's convergence behavior.

  19. Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity

    PubMed Central

    Masquelier, Timothée; Thorpe, Simon J

    2007-01-01

    Spike timing dependent plasticity (STDP) is a learning rule that modifies synaptic strength as a function of the relative timing of pre- and postsynaptic spikes. When a neuron is repeatedly presented with similar inputs, STDP is known to have the effect of concentrating high synaptic weights on afferents that systematically fire early, while postsynaptic spike latencies decrease. Here we use this learning rule in an asynchronous feedforward spiking neural network that mimics the ventral visual pathway and shows that when the network is presented with natural images, selectivity to intermediate-complexity visual features emerges. Those features, which correspond to prototypical patterns that are both salient and consistently present in the images, are highly informative and enable robust object recognition, as demonstrated on various classification tasks. Taken together, these results show that temporal codes may be a key to understanding the phenomenal processing speed achieved by the visual system and that STDP can lead to fast and selective responses. PMID:17305422

  20. Spike-timing-dependent synaptic plasticity depends on dendritic location

    NASA Astrophysics Data System (ADS)

    Froemke, Robert C.; Poo, Mu-ming; Dan, Yang

    2005-03-01

    In the neocortex, each neuron receives thousands of synaptic inputs distributed across an extensive dendritic tree. Although postsynaptic processing of each input is known to depend on its dendritic location, it is unclear whether activity-dependent synaptic modification is also location-dependent. Here we report that both the magnitude and the temporal specificity of spike-timing-dependent synaptic modification vary along the apical dendrite of rat cortical layer 2/3 pyramidal neurons. At the distal dendrite, the magnitude of long-term potentiation is smaller, and the window of pre-/postsynaptic spike interval for long-term depression (LTD) is broader. The spike-timing window for LTD correlates with the window of action potential-induced suppression of NMDA (N-methyl-D-aspartate) receptors; this correlation applies to both their dendritic location-dependence and pharmacological properties. Presynaptic stimulation with partial blockade of NMDA receptors induced LTD and occluded further induction of spike-timing-dependent LTD, suggesting that NMDA receptor suppression underlies LTD induction. Computer simulation studies showed that the dendritic inhomogeneity of spike-timing-dependent synaptic modification leads to differential input selection at distal and proximal dendrites according to the temporal characteristics of presynaptic spike trains. Such location-dependent tuning of inputs, together with the dendritic heterogeneity of postsynaptic processing, could enhance the computational capacity of cortical pyramidal neurons.

  1. Spike count, spike timing and temporal information in the cortex of awake, freely moving rats

    NASA Astrophysics Data System (ADS)

    Scaglione, Alessandro; Foffani, Guglielmo; Moxon, Karen A.

    2014-08-01

    Objective. Sensory processing of peripheral information is not stationary but is, in general, a dynamic process related to the behavioral state of the animal. Yet the link between the state of the behavior and the encoding properties of neurons is unclear. This report investigates the impact of the behavioral state on the encoding mechanisms used by cortical neurons for both detection and discrimination of somatosensory stimuli in awake, freely moving, rats. Approach. Neuronal activity was recorded from the primary somatosensory cortex of five rats under two different behavioral states (quiet versus whisking) while electrical stimulation of increasing stimulus strength was delivered to the mystacial pad. Information theoretical measures were then used to measure the contribution of different encoding mechanisms to the information carried by neurons in response to the whisker stimulation. Main results. We found that the behavioral state of the animal modulated the total amount of information conveyed by neurons and that the timing of individual spikes increased the information compared to the total count of spikes alone. However, the temporal information, i.e. information exclusively related to when the spikes occur, was not modulated by behavioral state. Significance. We conclude that information about somatosensory stimuli is modulated by the behavior of the animal and this modulation is mainly expressed in the spike count while the temporal information is more robust to changes in behavioral state.

  2. Spike count, spike timing and temporal information in the cortex of awake, freely moving rats

    PubMed Central

    Scaglione, Alessandro; Foffani, Guglielmo; Moxon, Karen A.

    2014-01-01

    Objective Sensory processing of peripheral information is not stationary but is, in general, a dynamic process related to the behavioral state of the animal. Yet the link between the state of the behavior and the encoding properties of neurons is unclear. This report investigates the impact of the behavioral state on the encoding mechanisms used by cortical neurons for both detection and discrimination of somatosensory stimuli in awake, freely moving, rats. Approach Neuronal activity was recorded from the primary somatosensory cortex of five rats under two different behavioral states (quiet vs. whisking) while electrical stimulation of increasing stimulus strength was delivered to the mystacial pad. Information theoretical measures were then used to measure the contribution of different encoding mechanisms to the information carried by neurons in response to the whisker stimulation. Main Results We found that the behavioral state of the animal modulated the total amount of information conveyed by neurons and that the timing of individual spikes increased the information compared to the total count of spikes alone. However, the temporal information, i.e. information exclusively related to when the spikes occur, was not modulated by behavioral state. Significance We conclude that information about somatosensory stimuli is modulated by the behavior of the animal and this modulation is mainly expressed in the spike count while the temporal information is more robust to changes in behavioral state. PMID:25024291

  3. Financial Time Series Prediction Using Spiking Neural Networks

    PubMed Central

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. PMID:25170618

  4. Financial time series prediction using spiking neural networks.

    PubMed

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  5. State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

    PubMed Central

    Shimazaki, Hideaki; Amari, Shun-ichi; Brown, Emery N.; Grün, Sonja

    2012-01-01

    Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods

  6. GABAergic circuits control spike-timing-dependent plasticity.

    PubMed

    Paille, Vincent; Fino, Elodie; Du, Kai; Morera-Herreras, Teresa; Perez, Sylvie; Kotaleski, Jeanette Hellgren; Venance, Laurent

    2013-05-29

    The spike-timing-dependent plasticity (STDP), a synaptic learning rule for encoding learning and memory, relies on relative timing of neuronal activity on either side of the synapse. GABAergic signaling has been shown to control neuronal excitability and consequently the spike timing, but whether GABAergic circuits rule the STDP remained unknown. Here we show that GABAergic signaling governs the polarity of STDP, because blockade of GABAA receptors was able to completely reverse the temporal order of plasticity at corticostriatal synapses in rats and mice. GABA controls the polarity of STDP in both striatopallidal and striatonigral output neurons. Biophysical simulations and experimental investigations suggest that GABA controls STDP polarity through depolarizing effects at distal dendrites of striatal output neurons by modifying the balance of two calcium sources, NMDARs and voltage-sensitive calcium channels. These findings establish a central role for GABAergic circuits in shaping STDP and suggest that GABA could operate as a Hebbian/anti-Hebbian switch.

  7. Origin of the spike-timing-dependent plasticity rule

    NASA Astrophysics Data System (ADS)

    Cho, Myoung Won; Choi, M. Y.

    2016-08-01

    A biological synapse changes its efficacy depending on the difference between pre- and post-synaptic spike timings. Formulating spike-timing-dependent interactions in terms of the path integral, we establish a neural-network model, which makes it possible to predict relevant quantities rigorously by means of standard methods in statistical mechanics and field theory. In particular, the biological synaptic plasticity rule is shown to emerge as the optimal form for minimizing the free energy. It is further revealed that maximization of the entropy of neural activities gives rise to the competitive behavior of biological learning. This demonstrates that statistical mechanics helps to understand rigorously key characteristic behaviors of a neural network, thus providing the possibility of physics serving as a useful and relevant framework for probing life.

  8. Reducing the Spikes of Avalanche Photodiode Measurements at the National Spherical Torus Experiment

    NASA Astrophysics Data System (ADS)

    Brubaker, Z. E.; Foley, E. L.

    2011-10-01

    Avalanche Photodiodes (APD) used at the National Spherical Torus Experiment (NSTX) make important measurements for the Motional Stark Effect (MSE) diagnostic. However, they are very sensitive, and if radiation consistently reaches these detectors they are damaged over time. Furthermore, they also display spikes in their readings, which greatly complicates the data analysis for MSE. Due to our Collisionally-Induced Fluorescence Motional Stark Effect diagnostic observing significant radiation despite being shielded by a 3 foot concrete wall, we must devise a plan for shielding our new Laser-Induced Fluorescence Motional Stark Effect diagnostic, as well as determining the best possible location for them. In order to reduce the amount of spikes seen in our readings and to preserve our detectors, I investigated the type of radiation responsible, the locations most affected, and tested various materials for shielding. Results will be presented.

  9. Spin-orbit torque induced spike-timing dependent plasticity

    SciTech Connect

    Sengupta, Abhronil Al Azim, Zubair; Fong, Xuanyao; Roy, Kaushik

    2015-03-02

    Nanoelectronic devices that mimic the functionality of synapses are a crucial requirement for performing cortical simulations of the brain. In this work, we propose a ferromagnet-heavy metal heterostructure that employs spin-orbit torque to implement spike-timing dependent plasticity. The proposed device offers the advantage of decoupled spike transmission and programming current paths, thereby leading to reliable operation during online learning. Possible arrangement of such devices in a crosspoint architecture can pave the way for ultra-dense neural networks. Simulation studies indicate that the device has the potential of achieving pico-Joule level energy consumption (maximum 2 pJ per synaptic event) which is comparable to the energy consumption for synaptic events in biological synapses.

  10. Linear control of neuronal spike timing using phase response curves.

    PubMed

    Stigen, Tyler; Danzl, Per; Moehlis, Jeff; Netoff, Theoden

    2009-01-01

    We propose a simple, robust, linear method to control the spike timing of a periodically firing neuron. The control scheme uses the neuron's phase response curve to identify an area of optimal sensitivity for the chosen stimulation parameters. The spike advance as a function of current pulse amplitude is characterized at the optimal phase and a linear least-squares regression is fit to the data. The inverted regression is used as the control function for this method. The efficacy of this method is demonstrated through numerical simulations of a Hodgkin-Huxley style neuron model as well as in real neurons from rat hippocampal slice preparations. The study shows a proof of concept for the application of a linear control scheme to control neuron spike timing in-vitro. This study was done on an individual cell level, but translation to a tissue or network level is possible. Control schemes of this type could be implemented in a closed loop implantable device to treat neuromotor disorders involving pathologically neuronal activity such as epilepsy or Parkinson's disease.

  11. Spike Timing Rigidity Is Maintained in Bursting Neurons under Pentobarbital-Induced Anesthetic Conditions

    PubMed Central

    Kato, Risako; Yamanaka, Masanori; Yokota, Eiko; Koshikawa, Noriaki; Kobayashi, Masayuki

    2016-01-01

    Pentobarbital potentiates γ-aminobutyric acid (GABA)-mediated inhibitory synaptic transmission by prolonging the open time of GABAA receptors. However, it is unknown how pentobarbital regulates cortical neuronal activities via local circuits in vivo. To examine this question, we performed extracellular unit recording in rat insular cortex under awake and anesthetic conditions. Not a few studies apply time-rescaling theorem to detect the features of repetitive spike firing. Similar to these methods, we define an average spike interval locally in time using random matrix theory (RMT), which enables us to compare different activity states on a universal scale. Neurons with high spontaneous firing frequency (>5 Hz) and bursting were classified as HFB neurons (n = 10), and those with low spontaneous firing frequency (<10 Hz) and without bursting were classified as non-HFB neurons (n = 48). Pentobarbital injection (30 mg/kg) reduced firing frequency in all HFB neurons and in 78% of non-HFB neurons. RMT analysis demonstrated that pentobarbital increased in the number of neurons with repulsion in both HFB and non-HFB neurons, suggesting that there is a correlation between spikes within a short interspike interval (ISI). Under awake conditions, in 50% of HFB and 40% of non-HFB neurons, the decay phase of normalized histograms of spontaneous firing were fitted to an exponential function, which indicated that the first spike had no correlation with subsequent spikes. In contrast, under pentobarbital-induced anesthesia conditions, the number of non-HFB neurons that were fitted to an exponential function increased to 80%, but almost no change in HFB neurons was observed. These results suggest that under both awake and pentobarbital-induced anesthetized conditions, spike firing in HFB neurons is more robustly regulated by preceding spikes than by non-HFB neurons, which may reflect the GABAA receptor-mediated regulation of cortical activities. Whole-cell patch-clamp recording in

  12. Neonatal NMDA Receptor Blockade Disrupts Spike Timing and Glutamatergic Synapses in Fast Spiking Interneurons in a NMDA Receptor Hypofunction Model of Schizophrenia

    PubMed Central

    Jones, Kevin S.; Corbin, Joshua G.; Huntsman, Molly M.

    2014-01-01

    The dysfunction of parvalbumin-positive, fast-spiking interneurons (FSI) is considered a primary contributor to the pathophysiology of schizophrenia (SZ), but deficits in FSI physiology have not been explicitly characterized. We show for the first time, that a widely-employed model of schizophrenia minimizes first spike latency and increases GluN2B-mediated current in neocortical FSIs. The reduction in FSI first-spike latency coincides with reduced expression of the Kv1.1 potassium channel subunit which provides a biophysical explanation for the abnormal spiking behavior. Similarly, the increase in NMDA current coincides with enhanced expression of the GluN2B NMDA receptor subunit, specifically in FSIs. In this study mice were treated with the NMDA receptor antagonist, MK-801, during the first week of life. During adolescence, we detected reduced spike latency and increased GluN2B-mediated NMDA current in FSIs, which suggests transient disruption of NMDA signaling during neonatal development exerts lasting changes in the cellular and synaptic physiology of neocortical FSIs. Overall, we propose these physiological disturbances represent a general impairment to the physiological maturation of FSIs which may contribute to schizophrenia-like behaviors produced by this model. PMID:25290690

  13. Endocannabinoids mediate bidirectional striatal spike-timing-dependent plasticity

    PubMed Central

    Cui, Yihui; Paillé, Vincent; Xu, Hao; Genet, Stéphane; Delord, Bruno; Fino, Elodie; Berry, Hugues; Venance, Laurent

    2015-01-01

    Key points Although learning can arise from few or even a single trial, synaptic plasticity is commonly assessed under prolonged activation. Here, we explored the existence of rapid responsiveness of synaptic plasticity at corticostriatal synapses in a major synaptic learning rule, spike-timing-dependent plasticity (STDP). We found that spike-timing-dependent depression (tLTD) progressively disappears when the number of paired stimulations (below 50 pairings) is decreased whereas spike-timing-dependent potentiation (tLTP) displays a biphasic profile: tLTP is observed for 75–100 pairings, is absent for 25–50 pairings and re-emerges for 5–10 pairings. This tLTP induced by low numbers of pairings (5–10) depends on activation of the endocannabinoid system, type-1 cannabinoid receptor and the transient receptor potential vanilloid type-1. Endocannabinoid-tLTP may represent a physiological mechanism operating during the rapid learning of new associative memories and behavioural rules characterizing the flexible behaviour of mammals or during the initial stages of habit learning. Abstract Synaptic plasticity, a main substrate for learning and memory, is commonly assessed with prolonged stimulations. Since learning can arise from few or even a single trial, synaptic strength is expected to adapt rapidly. However, whether synaptic plasticity occurs in response to limited event occurrences remains elusive. To answer this question, we investigated whether a low number of paired stimulations can induce plasticity in a major synaptic learning rule, spike-timing-dependent plasticity (STDP). It is known that 100 pairings induce bidirectional STDP, i.e. spike-timing-dependent potentiation (tLTP) and depression (tLTD) at most central synapses. In rodent striatum, we found that tLTD progressively disappears when the number of paired stimulations is decreased (below 50 pairings) whereas tLTP displays a biphasic profile: tLTP is observed for 75–100 pairings, absent for 25

  14. Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination

    PubMed Central

    2012-01-01

    by the streaming computing Results and discussion Results demonstrate that the stream-based eigenfilter can achieve the same accuracy and is 10 times more computationally efficient when compared with conventional PCA algorithms. Hardware evaluations show that 90.3% logic resources, 95.1% power consumption and 86.8% computing latency can be reduced by the stream-based eigenfilter when compared with PCA hardware. By utilizing the streaming method, 92% memory resources and 67% power consumption can be saved when compared with the direct implementation of GHA. Conclusion Stream-based Hebbian eigenfilter presents a novel approach to enable real-time spike sorting with reduced computational complexity and hardware costs. This new design can be further utilized for multi-channel neuro-physiological experiments or chronic implants. PMID:22490725

  15. Dendritic Synapse Location and Neocortical Spike-Timing-Dependent Plasticity

    PubMed Central

    Froemke, Robert C.; Letzkus, Johannes J.; Kampa, Björn M.; Hang, Giao B.; Stuart, Greg J.

    2010-01-01

    While it has been appreciated for decades that synapse location in the dendritic tree has a powerful influence on signal processing in neurons, the role of dendritic synapse location on the induction of long-term synaptic plasticity has only recently been explored. Here, we review recent work revealing how learning rules for spike-timing-dependent plasticity (STDP) in cortical neurons vary with the spatial location of synaptic input. A common principle appears to be that proximal synapses show conventional STDP, whereas distal inputs undergo plasticity according to novel learning rules. One crucial factor determining location-dependent STDP is the backpropagating action potential, which tends to decrease in amplitude and increase in width as it propagates into the dendritic tree of cortical neurons. We discuss additional location-dependent mechanisms as well as the functional implications of heterogeneous learning rules at different dendritic locations for the organization of synaptic inputs. PMID:21423515

  16. Random walks for spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Williams, Alan; Leen, Todd K.; Roberts, Patrick D.

    2004-08-01

    Random walk methods are used to calculate the moments of negative image equilibrium distributions in synaptic weight dynamics governed by spike-timing-dependent plasticity. The neural architecture of the model is based on the electrosensory lateral line lobe of mormyrid electric fish, which forms a negative image of the reafferent signal from the fish’s own electric discharge to optimize detection of sensory electric fields. Of particular behavioral importance to the fish is the variance of the equilibrium postsynaptic potential in the presence of noise, which is determined by the variance of the equilibrium weight distribution. Recurrence relations are derived for the moments of the equilibrium weight distribution, for arbitrary postsynaptic potential functions and arbitrary learning rules. For the case of homogeneous network parameters, explicit closed form solutions are developed for the covariances of the synaptic weight and postsynaptic potential distributions.

  17. Spike-timing control by dendritic plateau potentials in the presence of synaptic barrages

    PubMed Central

    Shai, Adam S.; Koch, Christof; Anastassiou, Costas A.

    2014-01-01

    Apical and tuft dendrites of pyramidal neurons support regenerative electrical potentials, giving rise to long-lasting (approximately hundreds of milliseconds) and strong (~50 mV from rest) depolarizations. Such plateau events rely on clustered glutamatergic input, can be mediated by calcium or by NMDA currents, and often generate somatic depolarizations that last for the time course of the dendritic plateau event. We address the computational significance of such single-neuron processing via reduced but biophysically realistic modeling. We introduce a model based on two discrete integration zones, a somatic and a dendritic one, that communicate from the dendritic to the somatic compartment via a long plateau-conductance. We show principled differences in the way dendritic vs. somatic inhibition controls spike timing, and demonstrate how this could implement spike time control in the face of barrages of synaptic inputs. PMID:25177288

  18. Parvalbumin tunes spike-timing and efferent short-term plasticity in striatal fast spiking interneurons.

    PubMed

    Orduz, David; Bischop, Don Patrick; Schwaller, Beat; Schiffmann, Serge N; Gall, David

    2013-07-01

      Striatal fast spiking interneurons (FSIs) modulate output of the striatum by synchronizing medium-sized spiny neurons (MSNs). Recent studies have broadened our understanding of FSIs, showing that they are implicated in severe motor disorders such as parkinsonism, dystonia and Tourette syndrome. FSIs are the only striatal neurons to express the calcium-binding protein parvalbumin (PV). This selective expression of PV raises questions about the functional role of this Ca(2+) buffer in controlling FSI Ca(2+) dynamics and, consequently, FSI spiking mode and neurotransmission. To study the functional involvement of FSIs in striatal microcircuit activity and the role of PV in FSI function, we performed perforated patch recordings on enhanced green fluorescent protein-expressing FSIs in brain slices from control and PV-/- mice. Our results revealed that PV-/- FSIs fired more regularly and were more excitable than control FSIs by a mechanism in which Ca(2+) buffering is linked to spiking activity as a result of the activation of small conductance Ca(2+)-dependent K(+) channels. A modelling approach of striatal FSIs supports our experimental results. Furthermore, PV deletion modified frequency-specific short-term plasticity at inhibitory FSI to MSN synapses. Our results therefore reinforce the hypothesis that in FSIs, PV is crucial for fine-tuning of the temporal responses of the FSI network and for the orchestration of MSN populations. This, in turn, may play a direct role in the generation and pathology-related worsening of motor rhythms.

  19. Parvalbumin tunes spike-timing and efferent short-term plasticity in striatal fast spiking interneurons

    PubMed Central

    Orduz, David; Bischop, Don Patrick; Schwaller, Beat; Schiffmann, Serge N; Gall, David

    2013-01-01

    Striatal fast spiking interneurons (FSIs) modulate output of the striatum by synchronizing medium-sized spiny neurons (MSNs). Recent studies have broadened our understanding of FSIs, showing that they are implicated in severe motor disorders such as parkinsonism, dystonia and Tourette syndrome. FSIs are the only striatal neurons to express the calcium-binding protein parvalbumin (PV). This selective expression of PV raises questions about the functional role of this Ca2+ buffer in controlling FSI Ca2+ dynamics and, consequently, FSI spiking mode and neurotransmission. To study the functional involvement of FSIs in striatal microcircuit activity and the role of PV in FSI function, we performed perforated patch recordings on enhanced green fluorescent protein-expressing FSIs in brain slices from control and PV−/− mice. Our results revealed that PV−/− FSIs fired more regularly and were more excitable than control FSIs by a mechanism in which Ca2+ buffering is linked to spiking activity as a result of the activation of small conductance Ca2+-dependent K+ channels. A modelling approach of striatal FSIs supports our experimental results. Furthermore, PV deletion modified frequency-specific short-term plasticity at inhibitory FSI to MSN synapses. Our results therefore reinforce the hypothesis that in FSIs, PV is crucial for fine-tuning of the temporal responses of the FSI network and for the orchestration of MSN populations. This, in turn, may play a direct role in the generation and pathology-related worsening of motor rhythms. PMID:23551945

  20. Just in time connectivity for large spiking networks

    PubMed Central

    Lytton, William W.; Omurtag, Ahmet; Neymotin, Samuel A; Hines, Michael L

    2008-01-01

    The scale of large neuronal network simulations is memory-limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically-relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed – just-in-time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON’s standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory-limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that only added items to the queue when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run. PMID:18533821

  1. Just-in-time connectivity for large spiking networks.

    PubMed

    Lytton, William W; Omurtag, Ahmet; Neymotin, Samuel A; Hines, Michael L

    2008-11-01

    The scale of large neuronal network simulations is memory limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed: just in time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities, and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON's standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that added items to the queue only when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run.

  2. Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate.

    PubMed

    Graupner, Michael; Wallisch, Pascal; Ostojic, Srdjan

    2016-11-02

    Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity.

  3. Dynamically Maintained Spike Timing Sequences in Networks of Pulse-Coupled Oscillators with Delays

    NASA Astrophysics Data System (ADS)

    Gong, Pulin; van Leeuwen, Cees

    2007-01-01

    We demonstrate the widespread occurrence of dynamically maintained spike timing sequences in recurrent networks of pulse-coupled spiking neurons with large time delays. The sequences occur in transient, quasistable phase-locking states. The system spontaneously jumps between these states. This collective dynamics enables the system to generate a large number of distinct precise spike timing sequences. Distributed time delays play a constructive role by enhancing the dominance in parameter space of the dynamics responsible for producing the large variety of spike timing sequences.

  4. Influence of Ionic Conductances on Spike Timing Reliability of Cortical Neurons for Suprathreshold Rhythmic Inputs

    PubMed Central

    Schreiber, Susanne; Fellous, Jean-Marc; Tiesinga, Paul; Sejnowski, Terrence J.

    2010-01-01

    Spike timing reliability of neuronal responses depends on the frequency content of the input. We investigate how intrinsic properties of cortical neurons affect spike timing reliability in response to rhythmic inputs of suprathreshold mean. Analyzing reliability of conductance-based cortical model neurons on the basis of a correlation measure, we show two aspects of how ionic conductances influence spike timing reliability. First, they set the preferred frequency for spike timing reliability, which in accordance with the resonance effect of spike timing reliability is well approximated by the firing rate of a neuron in response to the DC component in the input. We demonstrate that a slow potassium current can modulate the spike timing frequency preference over a broad range of frequencies. This result is confirmed experimentally by dynamic-clamp recordings from rat prefrontal cortical neurons in vitro. Second, we provide evidence that ionic conductances also influence spike timing beyond changes in preferred frequency. Cells with the same DC firing rate exhibit more reliable spike timing at the preferred frequency and its harmonics if the slow potassium current is larger and its kinetics are faster, whereas a larger persistent sodium current impairs reliability. We predict that potassium channels are an efficient target for neuromodulators that can tune spike timing reliability to a given rhythmic input. PMID:14507985

  5. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

    PubMed Central

    Pecevski, Dejan

    2016-01-01

    Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

  6. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

    PubMed

    Pecevski, Dejan; Maass, Wolfgang

    2016-01-01

    Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

  7. Endocannabinoid dynamics gate spike-timing dependent depression and potentiation

    PubMed Central

    Cui, Yihui; Prokin, Ilya; Xu, Hao; Delord, Bruno; Genet, Stephane; Venance, Laurent; Berry, Hugues

    2016-01-01

    Synaptic plasticity is a cardinal cellular mechanism for learning and memory. The endocannabinoid (eCB) system has emerged as a pivotal pathway for synaptic plasticity because of its widely characterized ability to depress synaptic transmission on short- and long-term scales. Recent reports indicate that eCBs also mediate potentiation of the synapse. However, it is not known how eCB signaling may support bidirectionality. Here, we combined electrophysiology experiments with mathematical modeling to question the mechanisms of eCB bidirectionality in spike-timing dependent plasticity (STDP) at corticostriatal synapses. We demonstrate that STDP outcome is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Moreover, we show that eCB-tLTD requires active calcineurin whereas eCB-tLTP necessitates the activity of presynaptic PKA. Therefore, just like glutamate or GABA, eCB form a bidirectional system to encode learning and memory. DOI: http://dx.doi.org/10.7554/eLife.13185.001 PMID:26920222

  8. Dendritic NMDA spikes are necessary for timing-dependent associative LTP in CA3 pyramidal cells

    PubMed Central

    Brandalise, Federico; Carta, Stefano; Helmchen, Fritjof; Lisman, John; Gerber, Urs

    2016-01-01

    The computational repertoire of neurons is enhanced by regenerative electrical signals initiated in dendrites. These events, referred to as dendritic spikes, can act as cell-intrinsic amplifiers of synaptic input. Among these signals, dendritic NMDA spikes are of interest in light of their correlation with synaptic LTP induction. Because it is not possible to block NMDA spikes pharmacologically while maintaining NMDA receptors available to initiate synaptic plasticity, it remains unclear whether NMDA spikes alone can trigger LTP. Here we use dendritic recordings and calcium imaging to analyse the role of NMDA spikes in associative LTP in CA3 pyramidal cells. We show that NMDA spikes produce regenerative branch-specific calcium transients. Decreasing the probability of NMDA spikes reduces LTP, whereas increasing their probability enhances LTP. NMDA spikes and LTP occur without back-propagating action potentials. However, action potentials can facilitate LTP induction by promoting NMDA spikes. Thus, NMDA spikes are necessary and sufficient to produce the critical postsynaptic depolarization required for associative LTP in CA3 pyramidal cells. PMID:27848967

  9. Please Reduce Cycle Time

    DTIC Science & Technology

    2014-12-01

    Defense AT&L: November–December 2014 4 Please Reduce Cycle Time Brian Schultz “Time is what we want most but what we use worst.” — William Penn ...Schultz is a professor of program management at the Defense Acquisition University’s Mid-Atlantic Region in California, Md. As William Penn noted

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

    PubMed Central

    Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E.

    2014-01-01

    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. PMID:25392163

  11. 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.

  12. A History of Spike-Timing-Dependent Plasticity

    PubMed Central

    Markram, Henry; Gerstner, Wulfram; Sjöström, Per Jesper

    2011-01-01

    How learning and memory is achieved in the brain is a central question in neuroscience. Key to today’s research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb's (1949) postulate. Hebb conjectured that repeatedly and persistently co-active cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm – known as spike-timing-dependent plasticity (STDP) – has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today’s STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke, and Ribot, traversing, e.g., Lugaro’s plasticità and Rosenblatt’s perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks. PMID:22007168

  13. Distinct roles for IT and IH in controlling the frequency and timing of rebound spike responses

    PubMed Central

    Engbers, Jordan D T; Anderson, Dustin; Tadayonnejad, Reza; Mehaffey, W Hamish; Molineux, Michael L; Turner, Ray W

    2011-01-01

    Abstract The ability for neurons to generate rebound bursts following inhibitory synaptic input relies on ion channels that respond in a unique fashion to hyperpolarization. Inward currents provided by T-type calcium channels (IT) and hyperpolarization-activated HCN channels (IH) increase in availability upon hyperpolarization, allowing for a rebound depolarization after a period of inhibition. Although rebound responses have long been recognized in deep cerebellar nuclear (DCN) neurons, the actual extent to which IT and IH contribute to rebound spike output following physiological levels of membrane hyperpolarization has not been clearly established. The current study used recordings and simulations of large diameter cells of the in vitro rat DCN slice preparation to define the roles for IT and IH in a rebound response. We find that physiological levels of hyperpolarization make only small proportions of the total IT and IH available, but that these are sufficient to make substantial contributions to a rebound response. At least 50% of the early phase of the rebound spike frequency increase is generated by an IT-mediated depolarization. An additional frequency increase is provided by IH in reducing the time constant and thus the extent of IT inactivation as the membrane returns from a hyperpolarized state to the resting level. An IH-mediated depolarization creates an inverse voltage-first spike latency relationship and produces a 35% increase in the precision of the first spike latency of a rebound. IT and IH can thus be activated by physiologically relevant stimuli and have distinct roles in the frequency, timing and precision of rebound responses. PMID:21969455

  14. 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

  15. Predicting spike timing in highly synchronous auditory neurons at different sound levels

    PubMed Central

    Fontaine, Bertrand; Benichoux, Victor; Joris, Philip X.

    2013-01-01

    A challenge for sensory systems is to encode natural signals that vary in amplitude by orders of magnitude. The spike trains of neurons in the auditory system must represent the fine temporal structure of sounds despite a tremendous variation in sound level in natural environments. It has been shown in vitro that the transformation from dynamic signals into precise spike trains can be accurately captured by simple integrate-and-fire models. In this work, we show that the in vivo responses of cochlear nucleus bushy cells to sounds across a wide range of levels can be precisely predicted by deterministic integrate-and-fire models with adaptive spike threshold. Our model can predict both the spike timings and the firing rate in response to novel sounds, across a large input level range. A noisy version of the model accounts for the statistical structure of spike trains, including the reliability and temporal precision of responses. Spike threshold adaptation was critical to ensure that predictions remain accurate at different levels. These results confirm that simple integrate-and-fire models provide an accurate phenomenological account of spike train statistics and emphasize the functional relevance of spike threshold adaptation. PMID:23864375

  16. Interaction of inhibition and triplets of excitatory spikes modulates the NMDA-R-mediated synaptic plasticity in a computational model of spike timing-dependent plasticity.

    PubMed

    Cutsuridis, Vassilis

    2013-01-01

    Spike timing-dependent plasticity (STDP) experiments have shown that a synapse is strengthened when a presynaptic spike precedes a postsynaptic one and depressed vice versa. The canonical form of STDP has been shown to have an asymmetric shape with the peak long-term potentiation at +6 ms and the peak long-term depression at -5 ms. Experiments in hippocampal cultures with more complex stimuli such as triplets (one presynaptic spike combined with two postsynaptic spikes or one postsynaptic spike with two presynaptic spikes) have shown that pre-post-pre spike triplets result in no change in synaptic strength, whereas post-pre-post spike triplets lead to significant potentiation. The sign and magnitude of STDP have also been experimentally hypothesized to be modulated by inhibition. Recently, a computational study showed that the asymmetrical form of STDP in the CA1 pyramidal cell dendrite when two spikes interact switches to a symmetrical one in the presence of inhibition under certain conditions. In the present study, I investigate computationally how inhibition modulates STDP in the CA1 pyramidal neuron dendrite when it is driven by triplets. The model uses calcium as the postsynaptic signaling agent for STDP and is shown to be consistent with the experimental triplet observations in the absence of inhibition: simulated pre-post-pre spike triplets result in no change in synaptic strength, whereas simulated post-pre-post spike triplets lead to significant potentiation. When inhibition is bounded by the onset and offset of the triplet stimulation, then the strength of the synapse is decreased as the strength of inhibition increases. When inhibition arrives either few milliseconds before or at the onset of the last spike in the pre-post-pre triplet stimulation, then the synapse is potentiated. Variability in the frequency of inhibition (50 vs. 100 Hz) produces no change in synaptic strength. Finally, a 5% variation in model's calcium parameters (calcium thresholds

  17. The time-rescaling theorem and its application to neural spike train data analysis.

    PubMed

    Brown, Emery N; Barbieri, Riccardo; Ventura, Valérie; Kass, Robert E; Frank, Loren M

    2002-02-01

    Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The time-rescaling theorem is a well-known result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the supplementary eye field of a macque monkey and a comparison of temporal and spatial smoothers, inhomogeneous Poisson, inhomogeneous gamma, and inhomogeneous inverse gaussian models of rat hippocampal place cell spiking activity. To help make the logic behind the time-rescaling theorem more accessible to researchers in neuroscience, we present a proof using only elementary probability theory arguments. We also show how the theorem may be used to simulate a general point process model of a spike train. Our paradigm makes it possible to compare parametric and histogram-based neural spike train models directly. These results suggest that the time-rescaling theorem can be a valuable tool for neural spike train data analysis.

  18. Seasonal plasticity of precise spike timing in the avian auditory system.

    PubMed

    Caras, Melissa L; Sen, Kamal; Rubel, Edwin W; Brenowitz, Eliot A

    2015-02-25

    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.

  19. 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

  20. Time resolution dependence of information measures for spiking neurons: scaling and universality.

    PubMed

    Marzen, Sarah E; DeWeese, Michael R; Crutchfield, James P

    2015-01-01

    The mutual information between stimulus and spike-train response is commonly used to monitor neural coding efficiency, but neuronal computation broadly conceived requires more refined and targeted information measures of input-output joint processes. A first step toward that larger goal is to develop information measures for individual output processes, including information generation (entropy rate), stored information (statistical complexity), predictable information (excess entropy), and active information accumulation (bound information rate). We calculate these for spike trains generated by a variety of noise-driven integrate-and-fire neurons as a function of time resolution and for alternating renewal processes. We show that their time-resolution dependence reveals coarse-grained structural properties of interspike interval statistics; e.g., τ-entropy rates that diverge less quickly than the firing rate indicated by interspike interval correlations. We also find evidence that the excess entropy and regularized statistical complexity of different types of integrate-and-fire neurons are universal in the continuous-time limit in the sense that they do not depend on mechanism details. This suggests a surprising simplicity in the spike trains generated by these model neurons. Interestingly, neurons with gamma-distributed ISIs and neurons whose spike trains are alternating renewal processes do not fall into the same universality class. These results lead to two conclusions. First, the dependence of information measures on time resolution reveals mechanistic details about spike train generation. Second, information measures can be used as model selection tools for analyzing spike train processes.

  1. Non-Hebbian spike-timing-dependent plasticity in cerebellar circuits

    PubMed Central

    Piochon, Claire; Kruskal, Peter; MacLean, Jason; Hansel, Christian

    2013-01-01

    Spike-timing-dependent plasticity (STDP) provides a cellular implementation of the Hebb postulate, which states that synapses, whose activity repeatedly drives action potential firing in target cells, are potentiated. At glutamatergic synapses onto hippocampal and neocortical pyramidal cells, synaptic activation followed by spike firing in the target cell causes long-term potentiation (LTP)—as predicted by Hebb—whereas excitatory postsynaptic potentials (EPSPs) evoked after a spike elicit long-term depression (LTD)—a phenomenon that was not specifically addressed by Hebb. In both instances the action potential in the postsynaptic target neuron is an instructive signal that is capable of supporting synaptic plasticity. STDP generally relies on the propagation of Na+ action potentials that are initiated in the axon hillhock back into the dendrite, where they cause depolarization and boost local calcium influx. However, recent studies in CA1 hippocampal pyramidal neurons have suggested that local calcium spikes might provide a more efficient trigger for LTP induction than backpropagating action potentials. Dendritic calcium spikes also play a role in an entirely different type of STDP that can be observed in cerebellar Purkinje cells. These neurons lack backpropagating Na+ spikes. Instead, plasticity at parallel fiber (PF) to Purkinje cell synapses depends on the relative timing of PF-EPSPs and activation of the glutamatergic climbing fiber (CF) input that causes dendritic calcium spikes. Thus, the instructive signal in this system is externalized. Importantly when EPSPs are elicited before CF activity, PF-LTD is induced rather than LTP. Thus, STDP in the cerebellum follows a timing rule that is opposite to its hippocampal/neocortical counterparts. Regardless, a common motif in plasticity is that LTD/LTP induction depends on the relative timing of synaptic activity and regenerative dendritic spikes which are driven by the instructive signal. PMID:23335888

  2. Non-Hebbian spike-timing-dependent plasticity in cerebellar circuits.

    PubMed

    Piochon, Claire; Kruskal, Peter; Maclean, Jason; Hansel, Christian

    2012-01-01

    Spike-timing-dependent plasticity (STDP) provides a cellular implementation of the Hebb postulate, which states that synapses, whose activity repeatedly drives action potential firing in target cells, are potentiated. At glutamatergic synapses onto hippocampal and neocortical pyramidal cells, synaptic activation followed by spike firing in the target cell causes long-term potentiation (LTP)-as predicted by Hebb-whereas excitatory postsynaptic potentials (EPSPs) evoked after a spike elicit long-term depression (LTD)-a phenomenon that was not specifically addressed by Hebb. In both instances the action potential in the postsynaptic target neuron is an instructive signal that is capable of supporting synaptic plasticity. STDP generally relies on the propagation of Na(+) action potentials that are initiated in the axon hillhock back into the dendrite, where they cause depolarization and boost local calcium influx. However, recent studies in CA1 hippocampal pyramidal neurons have suggested that local calcium spikes might provide a more efficient trigger for LTP induction than backpropagating action potentials. Dendritic calcium spikes also play a role in an entirely different type of STDP that can be observed in cerebellar Purkinje cells. These neurons lack backpropagating Na(+) spikes. Instead, plasticity at parallel fiber (PF) to Purkinje cell synapses depends on the relative timing of PF-EPSPs and activation of the glutamatergic climbing fiber (CF) input that causes dendritic calcium spikes. Thus, the instructive signal in this system is externalized. Importantly when EPSPs are elicited before CF activity, PF-LTD is induced rather than LTP. Thus, STDP in the cerebellum follows a timing rule that is opposite to its hippocampal/neocortical counterparts. Regardless, a common motif in plasticity is that LTD/LTP induction depends on the relative timing of synaptic activity and regenerative dendritic spikes which are driven by the instructive signal.

  3. Implementation of Arithmetic Operations With Time-Free Spiking Neural P Systems.

    PubMed

    Liu, Xiangrong; Li, Ziming; Liu, Juan; Liu, Logan; Zeng, Xiangxiang

    2015-09-01

    Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In most applications of SN P systems, synchronization plays a key role which means the execution of a rule is completed in exactly one time unit (one step). However, such synchronization does not coincide with the biological fact: in biological nervous systems, the execution times of spiking rules cannot be known exactly. Therefore, a "realistic" system called time-free SN P systems were proposed, where the precise execution time of rules is removed. In this paper, we consider building arithmetical operation systems based on time-free SN P systems. Specifically, adder, subtracter, multiplier, and divider are constructed by using time-free SN P systems. The obtained systems always produce the same computation result independently from the execution time of the rules.

  4. Role of precise spike timing in coding of dynamic vibrissa stimuli in somatosensory thalamus.

    PubMed

    Montemurro, Marcelo A; Panzeri, Stefano; Maravall, Miguel; Alenda, Andrea; Bale, Michael R; Brambilla, Marco; Petersen, Rasmus S

    2007-10-01

    Rats discriminate texture by whisking their vibrissae across the surfaces of objects. This process induces corresponding vibrissa vibrations, which must be accurately represented by neurons in the somatosensory pathway. In this study, we investigated the neural code for vibrissa motion in the ventroposterior medial (VPm) nucleus of the thalamus by single-unit recording. We found that neurons conveyed a great deal of information (up to 77.9 bits/s) about vibrissa dynamics. The key was precise spike timing, which typically varied by less than a millisecond from trial to trial. The neural code was sparse, the average spike being remarkably informative (5.8 bits/spike). This implies that as few as four VPm spikes, coding independent information, might reliably differentiate between 10(6) textures. To probe the mechanism of information transmission, we compared the role of time-varying firing rate to that of temporally correlated spike patterns in two ways: 93.9% of the information encoded by a neuron could be accounted for by a hypothetical neuron with the same time-dependent firing rate but no correlations between spikes; moreover, > or =93.4% of the information in the spike trains could be decoded even if temporal correlations were ignored. Taken together, these results suggest that the essence of the VPm code for vibrissa motion is firing rate modulation on a submillisecond timescale. The significance of such a code may be that it enables a small number of neurons, firing only few spikes, to convey distinctions between very many different textures to the barrel cortex.

  5. Time scales of spike-train correlation for neural oscillators with common drive.

    PubMed

    Barreiro, Andrea K; Shea-Brown, Eric; Thilo, Evan L

    2010-01-01

    We examine the effect of the phase-resetting curve on the transfer of correlated input signals into correlated output spikes in a class of neural models receiving noisy superthreshold stimulation. We use linear-response theory to approximate the spike correlation coefficient in terms of moments of the associated exit time problem and contrast the results for type I vs type II models and across the different time scales over which spike correlations can be assessed. We find that, on long time scales, type I oscillators transfer correlations much more efficiently than type II oscillators. On short time scales this trend reverses, with the relative efficiency switching at a time scale that depends on the mean and standard deviation of input currents. This switch occurs over time scales that could be exploited by downstream circuits.

  6. Spike-timing-dependent plasticity with weight dependence evoked from physical constraints.

    PubMed

    Bamford, Simeon A; Murray, Alan F; Willshaw, David J

    2012-08-01

    Analogue and mixed-signal VLSI implementations of Spike-Timing-Dependent Plasticity (STDP) are reviewed. A circuit is presented with a compact implementation of STDP suitable for parallel integration in large synaptic arrays. In contrast to previously published circuits, it uses the limitations of the silicon substrate to achieve various forms and degrees of weight dependence of STDP. It also uses reverse-biased transistors to reduce leakage from a capacitance representing weight. Chip results are presented showing: various ways in which the learning rule may be shaped; how synaptic weights may retain some indication of their learned values over periods of minutes; and how distributions of weights for synapses convergent on single neurons may shift between more or less extreme bimodality according to the strength of correlational cues in their inputs.

  7. 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

  8. Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model.

    PubMed

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

    2014-03-01

    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.

  9. Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate

    PubMed Central

    Wallisch, Pascal; Ostojic, Srdjan

    2016-01-01

    Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity. SIGNIFICANCE STATEMENT Synaptic plasticity, the change in efficacy of connections between neurons, is thought to underlie learning and memory. The dominant paradigm posits that the precise timing of neural action potentials (APs) is central for plasticity induction. This concept is based on experiments using highly regular and stereotyped patterns of APs, in stark contrast with natural neuronal activity. Using synaptic plasticity models, we investigated how irregular, in vivo-like activity shapes synaptic plasticity. We found that synaptic changes induced by precise timing of APs are much weaker than suggested by regular stimulation protocols, and can be equivalently induced by modest variations of the AP rate alone. Our results call into question the dominant role of precise AP timing for plasticity in natural conditions. PMID:27807166

  10. Detection of submillisecond spike timing differences based on delay-line anticoincidence detection

    PubMed Central

    Lyons-Warren, Ariel M.; Kohashi, Tsunehiko; Mennerick, Steven

    2013-01-01

    Detection of submillisecond interaural timing differences is the basis for sound localization in reptiles, birds, and mammals. Although comparative studies reveal that different neural circuits underlie this ability, they also highlight common solutions to an inherent challenge: processing information on timescales shorter than an action potential. Discrimination of small timing differences is also important for species recognition during communication among mormyrid electric fishes. These fishes generate a species-specific electric organ discharge (EOD) that is encoded into submillisecond-to-millisecond timing differences between receptors. Small, adendritic neurons (small cells) in the midbrain are thought to analyze EOD waveform by comparing these differences in spike timing, but direct recordings from small cells have been technically challenging. In the present study we use a fluorescent labeling technique to obtain visually guided extracellular recordings from individual small cell axons. We demonstrate that small cells receive 1–2 excitatory inputs from 1 or more receptive fields with latencies that vary by over 10 ms. This wide range of excitatory latencies is likely due to axonal delay lines, as suggested by a previous anatomic study. We also show that inhibition of small cells from a calyx synapse shapes stimulus responses in two ways: through tonic inhibition that reduces spontaneous activity and through precisely timed, stimulus-driven, feed-forward inhibition. Our results reveal a novel delay-line anticoincidence detection mechanism for processing submillisecond timing differences, in which excitatory delay lines and precisely timed inhibition convert a temporal code into a population code. PMID:23966672

  11. Self-motion evokes precise spike timing in the primate vestibular system

    PubMed Central

    Jamali, Mohsen; Chacron, Maurice J.; Cullen, Kathleen E.

    2016-01-01

    The accurate representation of self-motion requires the efficient processing of sensory input by the vestibular system. Conventional wisdom is that vestibular information is exclusively transmitted through changes in firing rate, yet under this assumption vestibular neurons display relatively poor detection and information transmission. Here, we carry out an analysis of the system's coding capabilities by recording neuronal responses to repeated presentations of naturalistic stimuli. We find that afferents with greater intrinsic variability reliably discriminate between different stimulus waveforms through differential patterns of precise (∼6 ms) spike timing, while those with minimal intrinsic variability do not. A simple mathematical model provides an explanation for this result. Postsynaptic central neurons also demonstrate precise spike timing, suggesting that higher brain areas also represent self-motion using temporally precise firing. These findings demonstrate that two distinct sensory channels represent vestibular information: one using rate coding and the other that takes advantage of precise spike timing. PMID:27786265

  12. Modulation of spike timing by sensory deprivation during induction of cortical map plasticity.

    PubMed

    Celikel, Tansu; Szostak, Vanessa A; Feldman, Daniel E

    2004-05-01

    Deprivation-induced plasticity of sensory cortical maps involves long-term potentiation (LTP) and depression (LTD) of cortical synapses, but how sensory deprivation triggers LTP and LTD in vivo is unknown. Here we tested whether spike timing-dependent forms of LTP and LTD are involved in this process. We measured spike trains from neurons in layer 4 (L4) and layers 2 and 3 (L2/3) of rat somatosensory cortex before and after acute whisker deprivation, a manipulation that induces whisker map plasticity involving LTD at L4-to-L2/3 (L4-L2/3) synapses. Whisker deprivation caused an immediate reversal of firing order for most L4 and L2/3 neurons and a substantial decorrelation of spike trains, changes known to drive timing-dependent LTD at L4-L2/3 synapses in vitro. In contrast, spike rate changed only modestly. Thus, whisker deprivation is likely to drive map plasticity by spike timing-dependent mechanisms.

  13. Cell Type-Specific Differences in Spike Timing and Spike Shape in the Rat Parasubiculum and Superficial Medial Entorhinal Cortex.

    PubMed

    Ebbesen, Christian Laut; Reifenstein, Eric Torsten; Tang, Qiusong; Burgalossi, Andrea; Ray, Saikat; Schreiber, Susanne; Kempter, Richard; Brecht, Michael

    2016-07-26

    The medial entorhinal cortex (MEC) and the adjacent parasubiculum are known for their elaborate spatial discharges (grid cells, border cells, etc.) and the precessing of spikes relative to the local field potential. We know little, however, about how spatio-temporal firing patterns map onto cell types. We find that cell type is a major determinant of spatio-temporal discharge properties. Parasubicular neurons and MEC layer 2 (L2) pyramids have shorter spikes, discharge spikes in bursts, and are theta-modulated (rhythmic, locking, skipping), but spikes phase-precess only weakly. MEC L2 stellates and layer 3 (L3) neurons have longer spikes, do not discharge in bursts, and are weakly theta-modulated (non-rhythmic, weakly locking, rarely skipping), but spikes steeply phase-precess. The similarities between MEC L3 neurons and MEC L2 stellates on one hand and parasubicular neurons and MEC L2 pyramids on the other hand suggest two distinct streams of temporal coding in the parahippocampal cortex.

  14. Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

    NASA Astrophysics Data System (ADS)

    Mikkelsen, Kaare; Imparato, Alberto; Torcini, Alessandro

    2013-05-01

    The collective dynamics of excitatory pulse coupled neurons with spike-timing dependent plasticity is studied. The introduction of spike-timing dependent plasticity induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain the oscillations by a mechanism, the Sisyphus Effect, caused by a continuous feedback between the synaptic adjustments and the coherence in the neural firing. Due to this effect, the synaptic weights have oscillating equilibrium values, and this prevents the system from relaxing into a stationary macroscopic state.

  15. Reading Between the Spikes: Real-Time Signal Processing in Neural Systems

    NASA Astrophysics Data System (ADS)

    Warland, David Karsten

    This thesis discusses biological strategies for real-time signal processing in neural systems. Nearly all creatures encode information about the world as patterns of identically shaped action potentials, or "spikes". As a result, all the animal's knowledge of the world is contained in the occurrence times of these discrete events. Traditional approaches to the study of neural coding emphasize the encoding process, resulting in predictions of average neural responses to a limited class of stimuli. However, these studies fail to address the relevant biological question: What can the organism "learn" about the outside world from real-time observations of its own spike trains? Therefore, this thesis approaches neural coding from the point of view of the organism itself: We learn to decode neural spike trains to obtain real-time estimates of sensory stimuli. In particular, this ability to extract continuous signals from spiking cells, together with the definition of an equivalent spectral noise level for a spiking neuron allows characterization of the information contained in patterns of neural response as well as forming the basis for the prediction of optimal neural computation strategies with spike trains. These methods are applied to the design and analysis of experiments on a single wide field, movement -sensitive neuron (H1) in the visual system of the blowfly Calliphora erythrocephela and to the filiform hair receptors of the wind-sensing system of the cricket Acheta domestica. This thesis also discusses the generalization of these strategies to collections of neurons and the applications to future work in the context of neural computation in the retina.

  16. Time resolution dependence of information measures for spiking neurons: scaling and universality

    PubMed Central

    Marzen, Sarah E.; DeWeese, Michael R.; Crutchfield, James P.

    2015-01-01

    The mutual information between stimulus and spike-train response is commonly used to monitor neural coding efficiency, but neuronal computation broadly conceived requires more refined and targeted information measures of input-output joint processes. A first step toward that larger goal is to develop information measures for individual output processes, including information generation (entropy rate), stored information (statistical complexity), predictable information (excess entropy), and active information accumulation (bound information rate). We calculate these for spike trains generated by a variety of noise-driven integrate-and-fire neurons as a function of time resolution and for alternating renewal processes. We show that their time-resolution dependence reveals coarse-grained structural properties of interspike interval statistics; e.g., τ-entropy rates that diverge less quickly than the firing rate indicated by interspike interval correlations. We also find evidence that the excess entropy and regularized statistical complexity of different types of integrate-and-fire neurons are universal in the continuous-time limit in the sense that they do not depend on mechanism details. This suggests a surprising simplicity in the spike trains generated by these model neurons. Interestingly, neurons with gamma-distributed ISIs and neurons whose spike trains are alternating renewal processes do not fall into the same universality class. These results lead to two conclusions. First, the dependence of information measures on time resolution reveals mechanistic details about spike train generation. Second, information measures can be used as model selection tools for analyzing spike train processes. PMID:26379538

  17. Spike output jitter, mean firing time and coefficient of variation

    NASA Astrophysics Data System (ADS)

    Feng, Jianfeng; Brown, David

    1998-01-01

    To understand how a single neurone processes information, it is critical to examine the relationship between input and output. Marsalek, Koch and Maunsell's study focused on output jitter (standard deviation of output interpike interval) found that for the integrate-and-fire (I&F) model this response measure converges towards zero as the number of inputs increases indefinitely when interarrival times of excitatory inputs (EPSPs) are normally or uniformly distributed. In this work we present a complete, theoretical investigation, corroborated by numerical simulation, of output jitter in the I&F model with a variety of input distributions and a range of values of number of inputs, N. Our main results are: the exponential distribution input is a critical case and its output jitter is independent of N. For input distributions with tails which decrease faster than the exponential distribution, output jitter converges to zero as discovered by Marsalek, Koch and Maunsell; whereas an input distribution with a more slowly decreasing tail induces divergence of output jitter. Exact formulae for mean firing time are also obtained which enable us to estimate the coefficient of variation. The I&F model with leakage is also briefly considered.

  18. 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

  19. 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.

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

    PubMed Central

    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. PMID:23908626

  1. Spike Timing Regulation on the Millisecond Scale by Distributed Synaptic Plasticity at the Cerebellum Input Stage: A Simulation Study

    PubMed Central

    Garrido, Jesús A.; Ros, Eduardo; D’Angelo, Egidio

    2013-01-01

    The way long-term synaptic plasticity regulates neuronal spike patterns is not completely understood. This issue is especially relevant for the cerebellum, which is endowed with several forms of long-term synaptic plasticity and has been predicted to operate as a timing and a learning machine. Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular-layer network. In response to mossy fiber (MF) bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. The weight at MF to granule cell synapses regulated the delay of the first spike and the weight at MF and parallel fiber to Golgi cell synapses regulated the duration of the time-window during which the first-spike could be emitted. Moreover, the weights of synapses controlling Golgi cell activation regulated the intensity of granule cell inhibition and therefore the number of spikes that could be emitted. First-spike timing was regulated with millisecond precision and the number of spikes ranged from zero to three. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time-scale and allows the cerebellar granular layer to flexibly control burst transmission along the MF pathway. PMID:23720626

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

    PubMed

    Lee, Christopher M; Osman, Ahmad F; Volgushev, Maxim; Escabí, Monty A; Read, Heather L

    2016-04-01

    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.

  3. Spike timing-dependent plasticity induces non-trivial topology in the brain.

    PubMed

    Borges, R R; Borges, F S; Lameu, E L; Batista, A M; Iarosz, K C; Caldas, I L; Antonopoulos, C G; Baptista, M S

    2017-04-01

    We study the capacity of Hodgkin-Huxley neuron in a network to change temporarily or permanently their connections and behavior, the so called spike timing-dependent plasticity (STDP), as a function of their synchronous behavior. We consider STDP of excitatory and inhibitory synapses driven by Hebbian rules. We show that the final state of networks evolved by a STDP depend on the initial network configuration. Specifically, an initial all-to-all topology evolves to a complex topology. Moreover, external perturbations can induce co-existence of clusters, those whose neurons are synchronous and those whose neurons are desynchronous. This work reveals that STDP based on Hebbian rules leads to a change in the direction of the synapses between high and low frequency neurons, and therefore, Hebbian learning can be explained in terms of preferential attachment between these two diverse communities of neurons, those with low-frequency spiking neurons, and those with higher-frequency spiking neurons.

  4. Reduced chemical and electrical connections of fast-spiking interneurons in experimental cortical dysplasia.

    PubMed

    Zhou, Fu-Wen; Roper, Steven N

    2014-09-15

    Aberrant neural connections are regarded as a principal factor contributing to epileptogenesis. This study examined chemical and electrical connections between fast-spiking (FS), parvalbumin (PV)-immunoreactive (FS-PV) interneurons and regular-spiking (RS) neurons (pyramidal neurons or spiny stellate neurons) in a rat model of prenatal irradiation-induced cortical dysplasia. Presynaptic action potentials were evoked by current injection and the elicited unitary inhibitory or excitatory postsynaptic potentials (uIPSPs or uEPSPs) were recorded in the postsynaptic cell. In dysplastic cortex, connection rates between presynaptic FS-PV interneurons and postsynaptic RS neurons and FS-PV interneurons, and uIPSP amplitudes were significantly smaller than controls, but both failure rates and coefficient of variation of uIPSP amplitudes were larger than controls. In contrast, connection rates from RS neurons to FS-PV interneurons and uEPSPs amplitude were similar in the two groups. Assessment of the paired pulse ratio showed a significant decrease in synaptic release probability at FS-PV interneuronal terminals, and the density of terminal boutons on axons of biocytin-filled FS-PV interneurons was also decreased, suggesting presynaptic dysfunction in chemical synapses formed by FS-PV interneurons. Electrical connections were observed between FS-PV interneurons, and the connection rates and coupling coefficients were smaller in dysplastic cortex than controls. In dysplastic cortex, we found a reduced synaptic efficiency for uIPSPs originating from FS-PV interneurons regardless of the type of target cell, and impaired electrical connections between FS-PV interneurons. This expands our understanding of the fundamental impairment of inhibition in this model and may have relevance for certain types of human cortical dysplasia.

  5. Hebbian spike-timing dependent plasticity at the cerebellar input stage.

    PubMed

    Sgritta, M; Locatelli, F; Soda, T; Prestori, F; D'Angelo, E

    2017-02-10

    Spike-timing dependent plasticity (STDP) is a form of long-term synaptic plasticity exploiting the time relationship between postsynaptic action potentials (AP) and EPSPs. Surprisingly enough, very little was known about STDP in the cerebellum, although it is thought to play a critical role for learning appropriate timing of actions. We speculated that low-frequency oscillations observed in the granular layer may provide a reference for repetitive EPSP/AP phase coupling. Here we show that EPSP-spike pairing at 6Hz can optimally induce STDP at the mossy fiber - granule cell synapse in rats. Spike timing-dependent long-term potentiation and depression (st-LTP and st-LTD) were confined to a ±25 ms time-window. Since EPSPs led APs in st-LTP while APs led EPSPs in st-LTD, STDP was Hebbian in nature. STDP occurred at 6-10 Hz but vanished above 50 Hz or below 1 Hz (where only LTP or LTD occurred). STDP disappeared with randomized EPSP/AP pairing or high intracellular Ca(2+) buffering and its sign was inverted by GABA-A receptor activation. Both st-LTP and st-LTD required NMDA receptors, but st-LTP also required reinforcing signals mediated by mGluRs and intracellular calcium stores. Importantly, st-LTP and st-LTD were significantly larger than LTP and LTD obtained by modulating the frequency and duration of mossy fiber bursts, probably because STDP expression involved postsynaptic in addition to presynaptic mechanisms. These results thus show that a Hebbian form of STDP occurs at the cerebellum input stage providing the substrate for phase-dependent binding of mossy fiber spikes to repetitive theta-frequency cycles of granule cell activity.SIGNIFICANCE STATEMENTLong-term synaptic plasticity is a fundamental property of the brain causing persistent modifications of neuronal communication thought to provide the cellular basis of learning and memory. The cerebellum is critical for learning the appropriate timing of sensorimotor behaviors but whether and how appropriate

  6. Temporal Characteristics of the Predictive Synchronous Firing Modeled by Spike-Timing-Dependent Plasticity

    ERIC Educational Resources Information Center

    Kitano, Katsunori; Fukai, Tomoki

    2004-01-01

    When a sensory cue was repeatedly followed by a behavioral event with fixed delays, pairs of premotor and primary motor neurons showed significant increases of coincident spikes at times a monkey was expecting the event. These results provided evidence that neuronal firing synchrony has predictive power. To elucidate the underlying mechanism, here…

  7. Inhibitory and Excitatory Spike-Timing-Dependent Plasticity in the Auditory Cortex

    PubMed Central

    D'amour, James A.; Froemke, Robert C.

    2015-01-01

    Summary Synapses are plastic and can be modified by changes of spike timing. While most studies of long-term synaptic plasticity focus on excitation, inhibitory plasticity may be critical for controlling information processing, memory storage, and overall excitability in neural circuits. Here we examine spike-timing-dependent plasticity (STDP) of inhibitory synapses onto layer 5 neurons in slices of mouse auditory cortex, together with concomitant STDP of excitatory synapses. Pairing pre- and postsynaptic spikes potentiated inhibitory inputs irrespective of precise temporal order within ~10 msec. This was in contrast to excitatory inputs, which displayed an asymmetrical STDP time window. These combined synaptic modifications both required NMDA receptor activation, and adjusted the excitatory-inhibitory ratio of events paired together with postsynaptic spiking. Finally, subthreshold events became suprathreshold, and the time window between excitation and inhibition became more precise. These findings demonstrate that cortical inhibitory plasticity requires interactions with co-activated excitatory synapses to properly regulate excitatory-inhibitory balance. PMID:25843405

  8. Reduced Spiking in Entorhinal Cortex during the Delay Period of a Cued Spatial Response Task

    ERIC Educational Resources Information Center

    Gupta, Kishan; Keller, Lauren A.; Hasselmo, Michael E.

    2012-01-01

    Intrinsic persistent spiking mechanisms in medial entorhinal cortex (mEC) neurons may play a role in active maintenance of working memory. However, electrophysiological studies of rat mEC units have primarily focused on spatial modulation. We sought evidence of differential spike rates in the mEC in rats trained on a T-maze, cued spatial delayed…

  9. 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.

  10. On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex

    PubMed Central

    Zamarreño-Ramos, Carlos; Camuñas-Mesa, Luis A.; Pérez-Carrasco, Jose A.; Masquelier, Timothée; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

    2011-01-01

    In this paper we present a very exciting overlap between emergent nanotechnology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex. We focus on the type of memristors referred to as voltage or flux driven memristors and focus our discussions on a behavioral macro-model for such devices. The implementations result in fully asynchronous architectures with neurons sending their action potentials not only forward but also backward. One critical aspect is to use neurons that generate spikes of specific shapes. We will see how by changing the shapes of the neuron action potential spikes we can tune and manipulate the STDP learning rules for both excitatory and inhibitory synapses. We will see how neurons and memristors can be interconnected to achieve large scale spiking learning systems, that follow a type of multiplicative STDP learning rule. We will briefly extend the architectures to use three-terminal transistors with similar memristive behavior. We will illustrate how a V1 visual cortex layer can assembled and how it is capable of learning to extract orientations from visual data coming from a real artificial CMOS spiking retina observing real life scenes. Finally, we will discuss limitations of currently available memristors. The results presented are based on behavioral simulations and do not take into account non-idealities of devices and interconnects. The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three-terminal memristive type devices

  11. 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.

  12. Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations.

    PubMed

    Lyamzin, Dmitry R; Macke, Jakob H; Lesica, Nicholas A

    2010-01-01

    As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modeling such data must also be developed. Here, we present a model for the type of data commonly recorded in early sensory pathways: responses to repeated trials of a sensory stimulus in which each neuron has it own time-varying spike rate (as described by its PSTH) and the dependencies between cells are characterized by both signal and noise correlations. This model is an extension of previous attempts to model population spike trains designed to control only the total correlation between cells. In our model, the response of each cell is represented as a binary vector given by the dichotomized sum of a deterministic "signal" that is repeated on each trial and a Gaussian random "noise" that is different on each trial. This model allows the simulation of population spike trains with PSTHs, trial-to-trial variability, and pairwise correlations that match those measured experimentally. Furthermore, the model also allows the noise correlations in the spike trains to be manipulated independently of the signal correlations and single-cell properties. To demonstrate the utility of the model, we use it to simulate and manipulate experimental responses from the mammalian auditory and visual systems. We also present a general form of the model in which both the signal and noise are Gaussian random processes, allowing the mean spike rate, trial-to-trial variability, and pairwise signal and noise correlations to be specified independently. Together, these methods for modeling spike trains comprise a potentially powerful set of tools for both theorists and experimentalists studying population responses in sensory systems.

  13. Reinforcement learning, spike-time-dependent plasticity, and the BCM rule.

    PubMed

    Baras, Dorit; Meir, Ron

    2007-08-01

    Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is influenced by an environmental signal, termed a reward, that directs the changes in appropriate directions. We apply a recently introduced policy learning algorithm from machine learning to networks of spiking neurons and derive a spike-time-dependent plasticity rule that ensures convergence to a local optimum of the expected average reward. The approach is applicable to a broad class of neuronal models, including the Hodgkin-Huxley model. We demonstrate the effectiveness of the derived rule in several toy problems. Finally, through statistical analysis, we show that the synaptic plasticity rule established is closely related to the widely used BCM rule, for which good biological evidence exists.

  14. Stability of negative-image equilibria in spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Williams, Alan; Roberts, Patrick D.; Leen, Todd K.

    2003-08-01

    We investigate the stability of negative image equilibria in mean synaptic weight dynamics governed by spike-timing-dependent plasticity (STDP). The model architecture closely follows the anatomy and physiology of the electrosensory lateral line lobe (ELL) of mormyrid electric fish. The ELL uses a spike-timing-dependent learning rule to form a negative image of the reafferent signal from the fish’s own electric discharge, thus improving detectability of external electric fields. We derive sufficient conditions for existence of the negative image and necessary and sufficient conditions for stability, for arbitrary postsynaptic potential functions and arbitrary learning rules. This significantly generalizes earlier investigations. We then apply the general result to several examples of biological interest, including a class of learning rules consistent with the rule observed experimentally in the mormyrid ELL.

  15. Importance of equilibration time in the partitioning and toxicity of zinc in spiked sediment bioassays

    USGS Publications Warehouse

    Lee, J.-S.; Lee, B.-G.; Luoma, S.N.; Yoo, H.

    2004-01-01

    The influences of spiked Zn concentrations (1-40 ??mol/g) and equilibration time (???95 d) on the partitioning of Zn between pore water (PW) and sediment were evaluated with estuarine sediments containing two levels (5 and 15 ??mol/g) of acid volatile sulfides (AVS). Their influence on Zn bioavailability was also evaluated by a parallel, 10-d amphipod (Leptocheirus plumulosus) mortality test at 5, 20, and 85 d of equilibration. During the equilibration, AVS increased (up to twofold) with spiked Zn concentration ([Zn]), whereas Zn-simultaneously extracted metals ([SEM]; Zn with AVS) remained relatively constant. Concentrations of Zn in PW decreased most rapidly during the initial 30 d and by 11- to 23-fold during the whole 95-d equilibration period. The apparent partitioning coefficient (Kpw, ratio of [Zn] in SEM to PW) increased by 10- to 20-fold with time and decreased with spiked [Zn] in sediments. The decrease of PW [Zn] could be explained by a combination of changes in AVS and redistribution of Zn into more insoluble phases as the sediment aged. Amphipod mortality decreased significantly with the equilibration time, consistent with decrease in dissolved [Zn]. The median lethal concentration (LC50) value (33 ??M) in the second bioassay, conducted after 20 d of equilibration, was twofold the LC50 in the initial bioassay at 5 d of equilibration, probably because of the change of dissolved Zn speciation. Sediment bioassay protocols employing a short equilibration time and high spiked metal concentrations could accentuate partitioning of metals to the dissolved phase and shift the pathway for metal exposure toward the dissolved phase.

  16. A neuromorphic VLSI design for spike timing and rate based synaptic plasticity.

    PubMed

    Rahimi Azghadi, Mostafa; Al-Sarawi, Said; Abbott, Derek; Iannella, Nicolangelo

    2013-09-01

    Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety of biological experiments, while the PSTDP rule fails to reproduce them. Additionally, it has been shown that the behaviour inherent to the spike rate-based Bienenstock-Cooper-Munro (BCM) synaptic plasticity rule can also emerge from the TSTDP rule. This paper proposes an analogue implementation of the TSTDP rule. The proposed VLSI circuit has been designed using the AMS 0.35 μm CMOS process and has been simulated using design kits for Synopsys and Cadence tools. Simulation results demonstrate how well the proposed circuit can alter synaptic weights according to the timing difference amongst a set of different patterns of spikes. Furthermore, the circuit is shown to give rise to a BCM-like learning rule, which is a rate-based rule. To mimic an implementation environment, a 1000 run Monte Carlo (MC) analysis was conducted on the proposed circuit. The presented MC simulation analysis and the simulation result from fine-tuned circuits show that it is possible to mitigate the effect of process variations in the proof of concept circuit; however, a practical variation aware design technique is required to promise a high circuit performance in a large scale neural network. We believe that the proposed design can play a significant role in future VLSI implementations of both spike timing and rate based neuromorphic learning systems.

  17. 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.

  18. Circuit mechanisms revealed by spike-timing correlations in macaque area MT.

    PubMed

    Huang, Xin; Lisberger, Stephen G

    2013-02-01

    We recorded simultaneously from pairs of motion-sensitive neurons in the middle temporal cortex (MT) of macaque monkeys and used cross-correlations in the timing of spikes between neurons to gain insights into cortical circuitry. We characterized the time course and stimulus dependency of the cross-correlogram (CCG) for each pair of neurons and of the auto-correlogram (ACG) of the individual neurons. For some neuron pairs, the CCG showed negative flanks that emerged next to the central peak during stimulus-driven responses. Similar negative flanks appeared in the ACG of many neurons. Negative flanks were most prevalent and deepest when the neurons were driven to high rates by visual stimuli that moved in the neurons' preferred directions. The temporal development of the negative flanks in the CCG coincided with a parallel, modest reduction of the noise correlation between the spike counts of the neurons. Computational analysis of a model cortical circuit suggested that negative flanks in the CCG arise from the excitation-triggered mutual cross-inhibition between pairs of excitatory neurons. Intracortical recurrent inhibition and afterhyperpolarization caused by intrinsic outward currents, such as the calcium-activated potassium current of small conductance, can both contribute to the negative flanks in the ACG. In the model circuit, stronger intracortical inhibition helped to maintain the temporal precision between the spike trains of pairs of neurons and led to weaker noise correlations. Our results suggest a neural circuit architecture that can leverage activity-dependent intracortical inhibition to adaptively modulate both the synchrony of spike timing and the correlations in response variability.

  19. Dynamical estimation of neuron and network properties III: network analysis using neuron spike times.

    PubMed

    Knowlton, Chris; Meliza, C Daniel; Margoliash, Daniel; Abarbanel, Henry D I

    2014-06-01

    Estimating the behavior of a network of neurons requires accurate models of the individual neurons along with accurate characterizations of the connections among them. Whereas for a single cell, measurements of the intracellular voltage are technically feasible and sufficient to characterize a useful model of its behavior, making sufficient numbers of simultaneous intracellular measurements to characterize even small networks is infeasible. This paper builds on prior work on single neurons to explore whether knowledge of the time of spiking of neurons in a network, once the nodes (neurons) have been characterized biophysically, can provide enough information to usefully constrain the functional architecture of the network: the existence of synaptic links among neurons and their strength. Using standardized voltage and synaptic gating variable waveforms associated with a spike, we demonstrate that the functional architecture of a small network of model neurons can be established.

  20. Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors

    PubMed Central

    Prezioso, M.; Merrikh Bayat, F.; Hoskins, B.; Likharev, K.; Strukov, D.

    2016-01-01

    Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses – the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some advanced cognitive tasks require spiking neuromorphic networks, which explicitly model individual neural pulses (“spikes”) in biological neural systems, it is crucial for memristive synapses to support the spike-time-dependent plasticity (STDP). A major challenge for the STDP implementation is that, in contrast to some simplistic models of the plasticity, the elementary change of a synaptic weight in an artificial hardware synapse depends not only on the pre-synaptic and post-synaptic signals, but also on the initial weight (memristor’s conductance) value. Here we experimentally demonstrate, for the first time, an STDP behavior that ensures self-adaptation of the average memristor conductance, making the plasticity stable, i.e. insensitive to the initial state of the devices. The experiments have been carried out with 200-nm Al2O3/TiO2−x memristors integrated into 12 × 12 crossbars. The experimentally observed self-adaptive STDP behavior has been complemented with numerical modeling of weight dynamics in a simple system with a leaky-integrate-and-fire neuron with a random spike-train input, using a compact model of memristor plasticity, fitted for quantitatively correct description of our memristors. PMID:26893175

  1. Real-time encoding and compression of neuronal spikes by metal-oxide memristors

    PubMed Central

    Gupta, Isha; Serb, Alexantrou; Khiat, Ali; Zeitler, Ralf; Vassanelli, Stefano; Prodromakis, Themistoklis

    2016-01-01

    Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real-time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multi-electrode array, demonstrating the technology's potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces. PMID:27666698

  2. Real-time encoding and compression of neuronal spikes by metal-oxide memristors

    NASA Astrophysics Data System (ADS)

    Gupta, Isha; Serb, Alexantrou; Khiat, Ali; Zeitler, Ralf; Vassanelli, Stefano; Prodromakis, Themistoklis

    2016-09-01

    Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real-time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multi-electrode array, demonstrating the technology's potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces.

  3. The role of hyperpolarization-activated cationic current in spike-time precision and intrinsic resonance in cortical neurons in vitro.

    PubMed

    Gastrein, Philippe; Campanac, Emilie; Gasselin, Célia; Cudmore, Robert H; Bialowas, Andrzej; Carlier, Edmond; Fronzaroli-Molinieres, Laure; Ankri, Norbert; Debanne, Dominique

    2011-08-01

    Hyperpolarization-activated cyclic nucleotide modulated current (I(h)) sets resonance frequency within the θ-range (5–12 Hz) in pyramidal neurons. However, its precise contribution to the temporal fidelity of spike generation in response to stimulation of excitatory or inhibitory synapses remains unclear. In conditions where pharmacological blockade of I(h) does not affect synaptic transmission, we show that postsynaptic h-channels improve spike time precision in CA1 pyramidal neurons through two main mechanisms. I(h) enhances precision of excitatory postsynaptic potential (EPSP)--spike coupling because I(h) reduces peak EPSP duration. I(h) improves the precision of rebound spiking following inhibitory postsynaptic potentials (IPSPs) in CA1 pyramidal neurons and sets pacemaker activity in stratum oriens interneurons because I(h) accelerates the decay of both IPSPs and after-hyperpolarizing potentials (AHPs). The contribution of h-channels to intrinsic resonance and EPSP waveform was comparatively much smaller in CA3 pyramidal neurons. Our results indicate that the elementary mechanisms by which postsynaptic h-channels control fidelity of spike timing at the scale of individual neurons may account for the decreased theta-activity observed in hippocampal and neocortical networks when h-channel activity is pharmacologically reduced.

  4. Optimal time scales of input fluctuations for spiking coherence and reliability in stochastic Hodgkin-Huxley neurons

    NASA Astrophysics Data System (ADS)

    Guo, Xinmeng; Wang, Jiang; Liu, Jing; Yu, Haitao; Galán, Roberto F.; Cao, Yibin; Deng, Bin

    2017-02-01

    Channel noise, which is generated by the random transitions of ion channels between open and closed states, is distinguished from external sources of physiological variability such as spontaneous synaptic release and stimulus fluctuations. This inherent stochasticity in ion-channel current can lead to variability of the timing of spikes occurring both spontaneously and in response to stimuli. In this paper, we investigate how intrinsic channel noise affects the response of stochastic Hodgkin-Huxley (HH) neuron to external fluctuating inputs with different amplitudes and correlation time. It is found that there is an optimal correlation time of input fluctuations for the maximal spiking coherence, where the input current has a fluctuating rate approximately matching the inherent oscillation of stochastic HH model and plays a dominating role in the timing of spike firing. We also show that the reliability of spike timing in the model is very sensitive to the properties of the current input. An optimal time scale of input fluctuations exists to induce the most reliable firing. The channel-noise-induced unreliability can be mostly overridden by injecting a fluctuating current with an appropriate correlation time. The spiking coherence and reliability can also be regulated by the size of channel stochasticity. As the membrane area (or total channel number) of the neuron increases, the spiking coherence decreases but the spiking reliability increases.

  5. 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

  6. Neuron-specific stimulus masking reveals interference in spike timing at the cortical level.

    PubMed

    Larson, Eric; Maddox, Ross K; Perrone, Ben P; Sen, Kamal; Billimoria, Cyrus P

    2012-02-01

    The auditory system is capable of robust recognition of sounds in the presence of competing maskers (e.g., other voices or background music). This capability arises despite the fact that masking stimuli can disrupt neural responses at the cortical level. Since the origins of such interference effects remain unknown, in this study, we work to identify and quantify neural interference effects that originate due to masking occurring within and outside receptive fields of neurons. We record from single and multi-unit auditory sites from field L, the auditory cortex homologue in zebra finches. We use a novel method called spike timing-based stimulus filtering that uses the measured response of each neuron to create an individualized stimulus set. In contrast to previous adaptive experimental approaches, which have typically focused on the average firing rate, this method uses the complete pattern of neural responses, including spike timing information, in the calculation of the receptive field. When we generate and present novel stimuli for each neuron that mask the regions within the receptive field, we find that the time-varying information in the neural responses is disrupted, degrading neural discrimination performance and decreasing spike timing reliability and sparseness. We also find that, while removing stimulus energy from frequency regions outside the receptive field does not significantly affect neural responses for many sites, adding a masker in these frequency regions can nonetheless have a significant impact on neural responses and discriminability without a significant change in the average firing rate. These findings suggest that maskers can interfere with neural responses by disrupting stimulus timing information with power either within or outside the receptive fields of neurons.

  7. Decoding of the spike timing of primary afferents during voluntary arm movements in monkeys

    PubMed Central

    Umeda, Tatsuya; Watanabe, Hidenori; Sato, Masa-aki; Kawato, Mitsuo; Isa, Tadashi; Nishimura, Yukio

    2014-01-01

    Understanding the mechanisms of encoding forelimb kinematics in the activity of peripheral afferents is essential for developing a somatosensory neuroprosthesis. To investigate whether the spike timing of dorsal root ganglion (DRG) neurons could be estimated from the forelimb kinematics of behaving monkeys, we implanted two multi-electrode arrays chronically in the DRGs at the level of the cervical segments in two monkeys. Neuronal activity during voluntary reach-to-grasp movements were recorded simultaneously with the trajectories of hand/arm movements, which were tracked in three-dimensional space using a motion capture system. Sixteen and 13 neurons, including muscle spindles, skin receptors, and tendon organ afferents, were recorded in the two monkeys, respectively. We were able to reconstruct forelimb joint kinematics from the temporal firing pattern of a subset of DRG neurons using sparse linear regression (SLiR) analysis, suggesting that DRG neuronal ensembles encoded information about joint kinematics. Furthermore, we estimated the spike timing of the DRG neuronal ensembles from joint kinematics using an integrate-and-fire model (IF) incorporating the SLiR algorithm. The temporal change of firing frequency of a subpopulation of neurons was reconstructed precisely from forelimb kinematics using the SLiR. The estimated firing pattern of the DRG neuronal ensembles encoded forelimb joint angles and velocities as precisely as the originally recorded neuronal activity. These results suggest that a simple model can be used to generate an accurate estimate of the spike timing of DRG neuronal ensembles from forelimb joint kinematics, and is useful for designing a proprioceptive decoder in a brain machine interface. PMID:24860416

  8. Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme.

    PubMed

    Masquelier, Timothée; Hugues, Etienne; Deco, Gustavo; Thorpe, Simon J

    2009-10-28

    Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential-a phenomenon referred to as "phase-of-firing coding" (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions-only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents ( approximately 10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.

  9. Decoding of the spike timing of primary afferents during voluntary arm movements in monkeys.

    PubMed

    Umeda, Tatsuya; Watanabe, Hidenori; Sato, Masa-Aki; Kawato, Mitsuo; Isa, Tadashi; Nishimura, Yukio

    2014-01-01

    Understanding the mechanisms of encoding forelimb kinematics in the activity of peripheral afferents is essential for developing a somatosensory neuroprosthesis. To investigate whether the spike timing of dorsal root ganglion (DRG) neurons could be estimated from the forelimb kinematics of behaving monkeys, we implanted two multi-electrode arrays chronically in the DRGs at the level of the cervical segments in two monkeys. Neuronal activity during voluntary reach-to-grasp movements were recorded simultaneously with the trajectories of hand/arm movements, which were tracked in three-dimensional space using a motion capture system. Sixteen and 13 neurons, including muscle spindles, skin receptors, and tendon organ afferents, were recorded in the two monkeys, respectively. We were able to reconstruct forelimb joint kinematics from the temporal firing pattern of a subset of DRG neurons using sparse linear regression (SLiR) analysis, suggesting that DRG neuronal ensembles encoded information about joint kinematics. Furthermore, we estimated the spike timing of the DRG neuronal ensembles from joint kinematics using an integrate-and-fire model (IF) incorporating the SLiR algorithm. The temporal change of firing frequency of a subpopulation of neurons was reconstructed precisely from forelimb kinematics using the SLiR. The estimated firing pattern of the DRG neuronal ensembles encoded forelimb joint angles and velocities as precisely as the originally recorded neuronal activity. These results suggest that a simple model can be used to generate an accurate estimate of the spike timing of DRG neuronal ensembles from forelimb joint kinematics, and is useful for designing a proprioceptive decoder in a brain machine interface.

  10. Characterization of reliability of spike timing in spinal interneurons during oscillating inputs.

    PubMed

    Beierholm, U; Nielsen, C D; Ryge, J; Alstrøm, P; Kiehn, O

    2001-10-01

    The spike timing in rhythmically active interneurons in the mammalian spinal locomotor network varies from cycle to cycle. We tested the contribution from passive membrane properties to this variable firing pattern, by measuring the reliability of spike timing, P, in interneurons in the isolated neonatal rat spinal cord, using intracellular injection of sinusoidal command currents of different frequencies (0.325-31.25 Hz). P is a measure of the precision of spike timing. In general, P was low at low frequencies and amplitudes (P = 0-0.6; 0-1.875 Hz; 0-30 pA), and high at high frequencies and amplitudes (P = 0.8-1; 3.125-31.25 Hz; 30-200 pA). The exact relationship between P and amplitude was difficult to describe because of the well-known low-pass properties of the membrane, which resulted in amplitude attenuation of high-frequency compared with low-frequency command currents. To formalize the analysis we used a leaky integrate and fire (LIF) model with a noise term added. The LIF model was able to reproduce the experimentally observed properties of P as well as the low-pass properties of the membrane. The LIF model enabled us to use the mathematical theory of nonlinear oscillators to analyze the relationship between amplitude, frequency, and P. This was done by systematically calculating the rotational number, N, defined as the number of spikes divided by the number of periods of the command current, for a large number of frequencies and amplitudes. These calculations led to a phase portrait based on the amplitude of the command current versus the frequency-containing areas [Arnold tongues (ATs)] with the same rotational number. The largest ATs in the phase portrait were those where N was a whole integer, and the largest areas in the ATs were seen for middle to high (>3 Hz) frequencies and middle to high amplitudes (50-120 pA). This corresponded to the amplitude- and frequency-evoked increase in P. The model predicted that P would be high when a cell responded

  11. 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

  12. Real-time PCR for detection of NDM-1 carbapenemase genes from spiked stool samples.

    PubMed

    Naas, Thierry; Ergani, Ayla; Carrër, Amélie; Nordmann, Patrice

    2011-09-01

    An in-house quantitative real-time PCR (qPCR) assay using TaqMan chemistry has been developed to detect NDM-1 carbapenemase genes from bacterial isolates and directly from stool samples. The qPCR amplification of bla(NDM-1) DNA was linear over 10 log dilutions (r(2) = 0.99), and the amplification efficiency was 1.03. The qPCR detection limit was reproducibly 1 CFU, or 10 plasmid molecules, and there was no cross-reaction with DNA extracted from several multidrug-resistant bacteria harboring other β-lactam resistance genes. Feces spiked with decreasing amounts of enterobacterial isolates producing NDM-1 were spread on ChromID ESBL and on CHROMagar KPC media and were subjected to the qPCR. The limits of carbapenem-resistant bacterial detection from stools was reproducibly 1 × 10(1) to 3 × 10(1) CFU/100 mg feces with ChromID ESBL medium. The CHROMagar KPC culture medium had higher limits of detection (1 × 10(1) to 4 × 10(3) CFU/ml), especially with bacterial isolates having low carbapenem MICs. The limits of detection with the qPCR assay were reproducibly below 1 × 10(1) CFU/100 mg of feces by qPCR assay. Samples spiked with NDM-1-negative bacteria were negative by qPCR. The sensitivity and specificity of the bla(NDM-1) qPCR assay on spiked samples were 100% in both cases. Using an automated DNA extraction system (QIAcube system), the qPCR assay was reproducible. The use of qPCR is likely to shorten the time for bla(NDM-1) detection from 48 h to 4 h and will be a valuable tool for outbreak follow-up in order to rapidly isolate colonized patients and assign them to cohorts.

  13. Ionic Mechanisms of Microsecond-Scale Spike Timing in Single Cells

    PubMed Central

    Zakon, Harold H.

    2014-01-01

    Electric fish image their environments and communicate by generating electric organ discharges through the simultaneous action potentials (APs) of electric organ cells (electrocytes) in the periphery. Steatogenys elegans generates a biphasic electrocyte discharge by the precisely regulated timing and waveform of APs generated from two excitable membranes present in each electrocyte. Current-clamp recordings of electrocyte APs reveal that the posterior membrane fires first, followed ∼30 μs later by an AP on the anterior membrane. This delay was maintained even as the onset of the first AP was advanced >5 ms by increasing stimulus intensity and across multiple spikes during bursts of APs elicited by prolonged stimulation. Simultaneous cell-attached loose-patch recordings of Na+ currents on each membrane revealed that activation voltage for Na+ channels on the posterior membrane was 10 mV hyperpolarized compared with Na+ channels on the anterior membrane, with no differences in activation or inactivation kinetics. Computational simulations of electrocyte APs demonstrated that this difference in Na+ current activation voltage was sufficient to maintain the proper firing order and the interspike delay. A similar difference in activation threshold has been reported for the Na+ currents of the axon initial segment compared with somatic Na+ channels of pyramidal neurons, suggesting convergent evolution of spike initiation and timing mechanisms across different systems of excitable cells. PMID:24806692

  14. Hopf bifurcation in the evolution of networks driven by spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Ren, Quansheng; Kolwankar, Kiran M.; Samal, Areejit; Jost, Jürgen

    2012-11-01

    We study the interplay of topology and dynamics in a neural network connected with spike-timing-dependent plasticity (STDP) synapses. Stimulated with periodic spike trains, the STDP-driven network undergoes a synaptic pruning process and evolves to a residual network. We examine the variation of topological and dynamical properties of the residual network by varying two key parameters of STDP: synaptic delay and the ratio between potentiation and depression. Our extensive numerical simulations of the leaky integrate-and-fire model show that there exists two regions in the parameter space. The first corresponds to fixed-point configurations, where the distribution of peak synaptic conductances and the firing rate of neurons remain constant over time. The second corresponds to oscillating configurations, where both topological and dynamical properties vary periodically, which is a result of a fixed point becoming a limit cycle via a Hopf bifurcation. This leads to interesting questions regarding the implications of these rhythms in the topology and dynamics of the network for learning and cognitive processing.

  15. Is a 4-Bit Synaptic Weight Resolution Enough? – Constraints on Enabling Spike-Timing Dependent Plasticity in Neuromorphic Hardware

    PubMed Central

    Pfeil, Thomas; Potjans, Tobias C.; Schrader, Sven; Potjans, Wiebke; Schemmel, Johannes; Diesmann, Markus; Meier, Karlheinz

    2012-01-01

    Large-scale neuromorphic hardware systems typically bear the trade-off between detail level and required chip resources. Especially when implementing spike-timing dependent plasticity, reduction in resources leads to limitations as compared to floating point precision. By design, a natural modification that saves resources would be reducing synaptic weight resolution. In this study, we give an estimate for the impact of synaptic weight discretization on different levels, ranging from random walks of individual weights to computer simulations of spiking neural networks. The FACETS wafer-scale hardware system offers a 4-bit resolution of synaptic weights, which is shown to be sufficient within the scope of our network benchmark. Our findings indicate that increasing the resolution may not even be useful in light of further restrictions of customized mixed-signal synapses. In addition, variations due to production imperfections are investigated and shown to be uncritical in the context of the presented study. Our results represent a general framework for setting up and configuring hardware-constrained synapses. We suggest how weight discretization could be considered for other backends dedicated to large-scale simulations. Thus, our proposition of a good hardware verification practice may rise synergy effects between hardware developers and neuroscientists. PMID:22822388

  16. Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model.

    PubMed

    Luque, Niceto R; Garrido, Jesús A; Naveros, Francisco; Carrillo, Richard R; D'Angelo, Egidio; Ros, Eduardo

    2016-01-01

    Deep cerebellar nuclei neurons receive both inhibitory (GABAergic) synaptic currents from Purkinje cells (within the cerebellar cortex) and excitatory (glutamatergic) synaptic currents from mossy fibers. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP) located at different cerebellar sites (parallel fibers to Purkinje cells, mossy fibers to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells) in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibers to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP) and inhibitory (i-STDP) mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibers to Purkinje cells synapses and then transferred to mossy fibers to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation toward optimizing its working range).

  17. Spike timing-dependent plasticity as the origin of the formation of clustered synaptic efficacy engrams.

    PubMed

    Iannella, Nicolangelo Libero; Launey, Thomas; Tanaka, Shigeru

    2010-01-01

    Synapse location, dendritic active properties and synaptic plasticity are all known to play some role in shaping the different input streams impinging onto a neuron. It remains unclear however, how the magnitude and spatial distribution of synaptic efficacies emerge from this interplay. Here, we investigate this interplay using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and spike timing-dependent plasticity (STDP). Specifically, we focus on the issue of how the efficacy of synapses contributed by different input streams are spatially represented in dendrites after STDP learning. We construct a simple feed forward network where a detailed model neuron receives synaptic inputs independently from multiple yet equally sized groups of afferent fibers with correlated activity, mimicking the spike activity from different neuronal populations encoding, for example, different sensory modalities. Interestingly, ensuing STDP learning, we observe that for all afferent groups, STDP leads to synaptic efficacies arranged into spatially segregated clusters effectively partitioning the dendritic tree. These segregated clusters possess a characteristic global organization in space, where they form a tessellation in which each group dominates mutually exclusive regions of the dendrite. Put simply, the dendritic imprint from different input streams left after STDP learning effectively forms what we term a "dendritic efficacy mosaic." Furthermore, we show how variations of the inputs and STDP rule affect such an organization. Our model suggests that STDP may be an important mechanism for creating a clustered plasticity engram, which shapes how different input streams are spatially represented in dendrite.

  18. Learning the structure of correlated synaptic subgroups using stable and competitive spike-timing-dependent plasticity.

    PubMed

    Meffin, H; Besson, J; Burkitt, A N; Grayden, D B

    2006-04-01

    Synaptic plasticity must be both competitive and stable if ongoing learning of the structure of neural inputs is to occur. In this paper, a wide class of spike-timing-dependent plasticity (STDP) models is identified that have both of these desirable properties in the case in which the input consists of subgroups of synapses that are correlated within the subgroup through the occurrence of simultaneous input spikes. The process of synaptic structure formation is studied, illustrating one particular class of these models. When the learning rate is small, multiple alternative synaptic structures are possible given the same inputs, with the outcome depending on the initial weight configuration. For large learning rates, the synaptic structure does not stabilize, resulting in neurons without consistent response properties. For learning rates in between, a unique and stable synaptic structure typically forms. When this synaptic structure exhibits a bimodal distribution, the neuron will respond selectively to one or more of the subgroups. The robustness with which this selectivity develops during learning is largely determined by the ratio of the subgroup correlation strength to the number of subgroups. The fraction of potentiated subgroups is primarily determined by the balance between potentiation and depression.

  19. Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

    PubMed Central

    Nessler, Bernhard; Pfeiffer, Michael; Buesing, Lars; Maass, Wolfgang

    2013-01-01

    The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex. PMID:23633941

  20. Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model

    PubMed Central

    Luque, Niceto R.; Garrido, Jesús A.; Naveros, Francisco; Carrillo, Richard R.; D'Angelo, Egidio; Ros, Eduardo

    2016-01-01

    Deep cerebellar nuclei neurons receive both inhibitory (GABAergic) synaptic currents from Purkinje cells (within the cerebellar cortex) and excitatory (glutamatergic) synaptic currents from mossy fibers. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP) located at different cerebellar sites (parallel fibers to Purkinje cells, mossy fibers to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells) in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibers to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP) and inhibitory (i-STDP) mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibers to Purkinje cells synapses and then transferred to mossy fibers to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation toward optimizing its working range). PMID:26973504

  1. A model of spike-timing dependent plasticity: one or two coincidence detectors?

    PubMed

    Karmarkar, Uma R; Buonomano, Dean V

    2002-07-01

    In spike-timing dependent plasticity (STDP), synapses exhibit LTD or LTP depending on the order of activity in the presynaptic and postsynaptic cells. LTP occurs when a single presynaptic spike precedes a postsynaptic one (a positive interspike interval, or ISI), while the reverse order of activity (a negative ISI) produces LTD. A fundamental question is whether the "standard model" of plasticity in which moderate increases in Ca(2+) influx through the N-methyl-D-aspartate (NMDA) channels induce LTD and large increases induce LTP, can account for the order and interval sensitivity of STDP. To examine this issue we developed a model that captures postsynaptic Ca(2+) influx dynamics and the associativity of the NMDA receptors. While this model can generate both LTD and LTP, it predicts that LTD will be observed at both negative and positive ISIs. This is because longer and longer positive ISIs induce monotonically decreasing levels of Ca(2+), which eventually fall into the same range that produced LTD at negative ISIs. A second model that incorporated a second coincidence detector in addition to the NMDA receptor generated LTP at positive intervals and LTD only at negative ones. Our findings suggest that a single coincidence detector model based on the standard model of plasticity cannot account for order-specific STDP, and we predict that STDP requires two coincidence detectors.

  2. Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules

    PubMed Central

    Frémaux, Nicolas; Gerstner, Wulfram

    2016-01-01

    Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide “when” to create new memories in response to a flow of sensory stimuli. In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discuss some experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity. We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators. PMID:26834568

  3. Spatial representation of temporal information through spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Nowotny, Thomas; Rabinovich, Misha I.; Abarbanel, Henry D.

    2003-07-01

    We suggest a mechanism based on spike-timing-dependent plasticity (STDP) of synapses to store, retrieve and predict temporal sequences. The mechanism is demonstrated in a model system of simplified integrate-and-fire type neurons densely connected by STDP synapses. All synapses are modified according to the so-called normal STDP rule observed in various real biological synapses. After conditioning through repeated input of a limited number of temporal sequences, the system is able to complete the temporal sequence upon receiving the input of a fraction of them. This is an example of effective unsupervised learning in a biologically realistic system. We investigate the dependence of learning success on entrainment time, system size, and presence of noise. Possible applications include learning of motor sequences, recognition and prediction of temporal sensory information in the visual as well as the auditory system, and late processing in the olfactory system of insects.

  4. Pulse Shape and Timing Dependence on the Spike-Timing Dependent Plasticity Response of Ion-Conducting Memristors as Synapses

    PubMed Central

    Campbell, Kristy A.; Drake, Kolton T.; Barney Smith, Elisa H.

    2016-01-01

    Ion-conducting memristors comprised of the layered materials Ge2Se3/SnSe/Ag are promising candidates for neuromorphic computing applications. Here, the spike-timing dependent plasticity (STDP) application is demonstrated for the first time with a single memristor type operating as a synapse over a timescale of 10 orders of magnitude, from nanoseconds through seconds. This large dynamic range allows the memristors to be useful in applications that require slow biological times, as well as fast times such as needed in neuromorphic computing, thus allowing multiple functions in one design for one memristor type—a “one size fits all” approach. This work also investigated the effects of varying the spike pulse shapes on the STDP response of the memristors. These results showed that small changes in the pre- and postsynaptic pulse shape can have a significant impact on the STDP. These results may provide circuit designers with insights into how pulse shape affects the actual memristor STDP response and aid them in the design of neuromorphic circuits and systems that can take advantage of certain features in the memristor STDP response that are programmable via the pre- and postsynaptic pulse shapes. In addition, the energy requirement per memristor is approximated based on the pulse shape and timing responses. The energy requirement estimated per memristor operating on slower biological timescales (milliseconds to seconds) is larger (nanojoules range), as expected, than the faster (nanoseconds) operating times (~0.1 pJ in some cases). Lastly, the memristors responded in a similar manner under normal STDP conditions (pre- and post-spikes applied to opposite memristor terminals) as they did to the case where a waveform corresponding to the difference between pre- and post-spikes was applied to only one electrode, with the other electrode held at ground potential. By applying the difference signal to only one terminal, testing of the memristor in various

  5. Presynaptic Spike Timing-Dependent Long-Term Depression in the Mouse Hippocampus

    PubMed Central

    Andrade-Talavera, Yuniesky; Duque-Feria, Paloma; Paulsen, Ole; Rodríguez-Moreno, Antonio

    2016-01-01

    Spike timing-dependent plasticity (STDP) is a Hebbian learning rule important for synaptic refinement during development and for learning and memory in the adult. Given the importance of the hippocampus in memory, surprisingly little is known about the mechanisms and functions of hippocampal STDP. In the present work, we investigated the requirements for induction of hippocampal spike timing-dependent long-term potentiation (t-LTP) and spike timing-dependent long-term depression (t-LTD) and the mechanisms of these 2 forms of plasticity at CA3-CA1 synapses in young (P12–P18) mouse hippocampus. We found that both t-LTP and t-LTD can be induced at hippocampal CA3-CA1 synapses by pairing presynaptic activity with single postsynaptic action potentials at low stimulation frequency (0.2 Hz). Both t-LTP and t-LTD require NMDA-type glutamate receptors for their induction, but the location and properties of these receptors are different: While t-LTP requires postsynaptic ionotropic NMDA receptor function, t-LTD does not, and whereas t-LTP is blocked by antagonists at GluN2A and GluN2B subunit-containing NMDA receptors, t-LTD is blocked by GluN2C or GluN2D subunit-preferring NMDA receptor antagonists. Both t-LTP and t-LTD require postsynaptic Ca2+ for their induction. Induction of t-LTD also requires metabotropic glutamate receptor activation, phospholipase C activation, postsynaptic IP3 receptor-mediated Ca2+ release from internal stores, postsynaptic endocannabinoid (eCB) synthesis, activation of CB1 receptors and astrocytic signaling, possibly via release of the gliotransmitter d-serine. We furthermore found that presynaptic calcineurin is required for t-LTD induction. t-LTD is expressed presynaptically as indicated by fluctuation analysis, paired-pulse ratio, and rate of use-dependent depression of postsynaptic NMDA receptor currents by MK801. The results show that CA3-CA1 synapses display both NMDA receptor-dependent t-LTP and t-LTD during development and identify a

  6. Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses

    PubMed Central

    Ambrogio, Stefano; Ciocchini, Nicola; Laudato, Mario; Milo, Valerio; Pirovano, Agostino; Fantini, Paolo; Ielmini, Daniele

    2016-01-01

    We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors. PMID:27013934

  7. Sisyphus effect in pulse-coupled excitatory neural networks with spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Mikkelsen, Kaare; Imparato, Alberto; Torcini, Alessandro

    2014-06-01

    The collective dynamics of excitatory pulse-coupled neural networks with spike-timing-dependent plasticity (STDP) is studied. Depending on the model parameters stationary states characterized by high or low synchronization can be observed. In particular, at the transition between these two regimes, persistent irregular low frequency oscillations between strongly and weakly synchronized states are observable, which can be identified as infraslow oscillations with frequencies ≃0.02-0.03 Hz. Their emergence can be explained in terms of the Sisyphus effect, a mechanism caused by a continuous feedback between the evolution of the coherent population activity and of the average synaptic weight. Due to this effect, the synaptic weights have oscillating equilibrium values, which prevents the neuronal population from relaxing into a stationary macroscopic state.

  8. Dynamical Mean-Field Equations for a Neural Network with Spike Timing Dependent Plasticity

    NASA Astrophysics Data System (ADS)

    Mayer, Jörg; Ngo, Hong-Viet V.; Schuster, Heinz Georg

    2012-09-01

    We study the discrete dynamics of a fully connected network of threshold elements interacting via dynamically evolving synapses displaying spike timing dependent plasticity. Dynamical mean-field equations, which become exact in the thermodynamical limit, are derived to study the behavior of the system driven with uncorrelated and correlated Gaussian noise input. We use correlated noise to verify that our model gives account to the fact that correlated noise provides stronger drive for synaptic modification. Further we find that stochastic independent input leads to a noise dependent transition to the coherent state where all neurons fire together, most notably there exists an optimal noise level for the enhancement of synaptic potentiation in our model.

  9. Dynamic modulation of spike timing-dependent calcium influx during corticostriatal upstates

    PubMed Central

    Evans, R. C.; Maniar, Y. M.

    2013-01-01

    The striatum of the basal ganglia demonstrates distinctive upstate and downstate membrane potential oscillations during slow-wave sleep and under anesthetic. The upstates generate calcium transients in the dendrites, and the amplitude of these calcium transients depends strongly on the timing of the action potential (AP) within the upstate. Calcium is essential for synaptic plasticity in the striatum, and these large calcium transients during the upstates may control which synapses undergo plastic changes. To investigate the mechanisms that underlie the relationship between calcium and AP timing, we have developed a realistic biophysical model of a medium spiny neuron (MSN). We have implemented sophisticated calcium dynamics including calcium diffusion, buffering, and pump extrusion, which accurately replicate published data. Using this model, we found that either the slow inactivation of dendritic sodium channels (NaSI) or the calcium inactivation of voltage-gated calcium channels (CDI) can cause high calcium corresponding to early APs and lower calcium corresponding to later APs. We found that only CDI can account for the experimental observation that sensitivity to AP timing is dependent on NMDA receptors. Additional simulations demonstrated a mechanism by which MSNs can dynamically modulate their sensitivity to AP timing and show that sensitivity to specifically timed pre- and postsynaptic pairings (as in spike timing-dependent plasticity protocols) is altered by the timing of the pairing within the upstate. These findings have implications for synaptic plasticity in vivo during sleep when the upstate-downstate pattern is prominent in the striatum. PMID:23843436

  10. Kinetics of fast short-term depression are matched to spike train statistics to reduce noise.

    PubMed

    Khanbabaie, Reza; Nesse, William H; Longtin, Andre; Maler, Leonard

    2010-06-01

    Short-term depression (STD) is observed at many synapses of the CNS and is important for diverse computations. We have discovered a form of fast STD (FSTD) in the synaptic responses of pyramidal cells evoked by stimulation of their electrosensory afferent fibers (P-units). The dynamics of the FSTD are matched to the mean and variance of natural P-unit discharge. FSTD exhibits switch-like behavior in that it is immediately activated with stimulus intervals near the mean interspike interval (ISI) of P-units (approximately 5 ms) and recovers immediately after stimulation with the slightly longer intervals (>7.5 ms) that also occur during P-unit natural and evoked discharge patterns. Remarkably, the magnitude of evoked excitatory postsynaptic potentials appear to depend only on the duration of the previous ISI. Our theoretical analysis suggests that FSTD can serve as a mechanism for noise reduction. Because the kinetics of depression are as fast as the natural spike statistics, this role is distinct from previously ascribed functional roles of STD in gain modulation, synchrony detection or as a temporal filter.

  11. Spike Timing-Dependent Plasticity as the Origin of the Formation of Clustered Synaptic Efficacy Engrams

    PubMed Central

    Iannella, Nicolangelo Libero; Launey, Thomas; Tanaka, Shigeru

    2010-01-01

    Synapse location, dendritic active properties and synaptic plasticity are all known to play some role in shaping the different input streams impinging onto a neuron. It remains unclear however, how the magnitude and spatial distribution of synaptic efficacies emerge from this interplay. Here, we investigate this interplay using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and spike timing-dependent plasticity (STDP). Specifically, we focus on the issue of how the efficacy of synapses contributed by different input streams are spatially represented in dendrites after STDP learning. We construct a simple feed forward network where a detailed model neuron receives synaptic inputs independently from multiple yet equally sized groups of afferent fibers with correlated activity, mimicking the spike activity from different neuronal populations encoding, for example, different sensory modalities. Interestingly, ensuing STDP learning, we observe that for all afferent groups, STDP leads to synaptic efficacies arranged into spatially segregated clusters effectively partitioning the dendritic tree. These segregated clusters possess a characteristic global organization in space, where they form a tessellation in which each group dominates mutually exclusive regions of the dendrite. Put simply, the dendritic imprint from different input streams left after STDP learning effectively forms what we term a “dendritic efficacy mosaic.” Furthermore, we show how variations of the inputs and STDP rule affect such an organization. Our model suggests that STDP may be an important mechanism for creating a clustered plasticity engram, which shapes how different input streams are spatially represented in dendrite. PMID:20725522

  12. Closed-loop optical stimulation and recording system with GPU-based real-time spike sorting

    NASA Astrophysics Data System (ADS)

    Wang, Ling; Nguyen, Thoa; Cabral, Henrique; Gysbrechts, Barbara; Battaglia, Francesco; Bartic, Carmen

    2014-05-01

    Closed-loop brain computer interfaces are rapidly progressing due to their applications in fundamental neuroscience and prosthetics. For optogenetic experiments, the integration of optical stimulation and electrophysiological recordings is emerging as an imperative engineering research topic. Optical stimulation does not only bring the advantage of cell-type selectivity, but also provides an alternative solution to the electrical stimulation-induced artifacts, a challenge in closedloop architectures. A closed-loop system must identify the neuronal signals in real-time such that a strategy is selected immediately (within a few milliseconds) for delivering stimulation patterns. Real-time spike sorting poses important challenges especially when a large number of recording channels are involved. Here we present a prototype allowing simultaneous optical stimulation and electro-physiological recordings in a closed-loop manner. The prototype was implemented with online spike detection and classification capabilities for selective cell stimulation. Real-time spike sorting was achieved by computations with a high speed, low cost graphic processing unit (GPU). We have successfully demonstrated the closed-loop operation, i.e. optical stimulation in vivo based on spike detection from 8 tetrodes (32 channels). The performance of GPU computation in spike sorting for different channel numbers and signal lengths was also investigated.

  13. Tonic GABAA conductance decreases membrane time constant and increases EPSP-spike precision in hippocampal pyramidal neurons.

    PubMed

    Wlodarczyk, Agnieszka I; Xu, Chun; Song, Inseon; Doronin, Maxim; Wu, Yu-Wei; Walker, Matthew C; Semyanov, Alexey

    2013-01-01

    Because of a complex dendritic structure, pyramidal neurons have a large membrane surface relative to other cells and so a large electrical capacitance and a large membrane time constant (τm). This results in slow depolarizations in response to excitatory synaptic inputs, and consequently increased and variable action potential latencies, which may be computationally undesirable. Tonic activation of GABAA receptors increases membrane conductance and thus regulates neuronal excitability by shunting inhibition. In addition, tonic increases in membrane conductance decrease the membrane time constant (τm), and improve the temporal fidelity of neuronal firing. Here we performed whole-cell current clamp recordings from hippocampal CA1 pyramidal neurons and found that bath application of 10μM GABA indeed decreases τm in these cells. GABA also decreased first spike latency and jitter (standard deviation of the latency) produced by current injection of 2 rheobases (500 ms). However, when larger current injections (3-6 rheobases) were used, GABA produced no significant effect on spike jitter, which was low. Using mathematical modeling we demonstrate that the tonic GABAA conductance decreases rise time, decay time and half-width of EPSPs in pyramidal neurons. A similar effect was observed on EPSP/IPSP pairs produced by stimulation of Schaffer collaterals: the EPSP part of the response became shorter after application of GABA. Consistent with the current injection data, a significant decrease in spike latency and jitter was obtained in cell attached recordings only at near-threshold stimulation (50% success rate, S50). When stimulation was increased to 2- or 3- times S50, GABA significantly affected neither spike latency nor spike jitter. Our results suggest that a decrease in τm associated with elevations in ambient GABA can improve EPSP-spike precision at near-threshold synaptic inputs.

  14. An FPGA Platform for Real-Time Simulation of Spiking Neuronal Networks.

    PubMed

    Pani, Danilo; Meloni, Paolo; Tuveri, Giuseppe; Palumbo, Francesca; Massobrio, Paolo; Raffo, Luigi

    2017-01-01

    In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing. Field programmable gate arrays (FPGAs) can improve flexibility when simple neuronal models are required, obtaining good accuracy, real-time performance, and the possibility to create a hybrid system without any custom hardware, just programming the hardware to achieve the required functionality. In this paper, this possibility is explored presenting a modular and efficient FPGA design of an in silico spiking neural network exploiting the Izhikevich model. The proposed system, prototypically implemented on a Xilinx Virtex 6 device, is able to simulate a fully connected network counting up to 1,440 neurons, in real-time, at a sampling rate of 10 kHz, which is reasonable for small to medium scale extra-cellular closed-loop experiments.

  15. An FPGA Platform for Real-Time Simulation of Spiking Neuronal Networks

    PubMed Central

    Pani, Danilo; Meloni, Paolo; Tuveri, Giuseppe; Palumbo, Francesca; Massobrio, Paolo; Raffo, Luigi

    2017-01-01

    In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing. Field programmable gate arrays (FPGAs) can improve flexibility when simple neuronal models are required, obtaining good accuracy, real-time performance, and the possibility to create a hybrid system without any custom hardware, just programming the hardware to achieve the required functionality. In this paper, this possibility is explored presenting a modular and efficient FPGA design of an in silico spiking neural network exploiting the Izhikevich model. The proposed system, prototypically implemented on a Xilinx Virtex 6 device, is able to simulate a fully connected network counting up to 1,440 neurons, in real-time, at a sampling rate of 10 kHz, which is reasonable for small to medium scale extra-cellular closed-loop experiments. PMID:28293163

  16. A requirement of low-threshold calcium spike for induction of spike-timing-dependent plasticity at corticothalamic synapses on relay neurons in the ventrobasal nucleus of rat thalamus.

    PubMed

    Hsu, Ching-Lung; Yang, Hsiu-Wen; Yen, Cheng-Tung; Min, Ming-Yuan

    2012-12-31

    Relay neurons in sensory thalamus transmit somatosensory information to cerebral cortex and receive sensory and feedback corticothalamic (CT) synaptic inputs. Their duality of firing modes, in bursts and continuous, underlies state dependence of thalamic information transfer, but the impact of different firing patterns on synaptic plasticity was rarely explored. To address this issue, we made whole-cell recording from relay neurons in the ventrobasal nucleus (VBN) of rat thalamus and compared synaptic plasticity induced by pairing CT-EPSP with two different types of burst spiking: low-threshold spike (LTS)-burst spiking triggered at Vm~-70 mV, and high-frequency spiking induced at Vm~-55 mV. The latter mimics natural burst spiking of relay neurons without activation of LTS. We found that, while backpropagating APs alone were not sufficient, low-threshold calcium spike was required for the induction of spike-timing-dependent LTP at CT synapses. Our results reveal a novel role of the calcium spike plays in the induction of long-term plasticity of CT synapse. Considering the dendritic origin of LTS, this study also implies potential physiological regulations over synaptic plasticity in thalamus. We propose that this form of synaptic plasticity may be involved in the dynamic fine-tuning of thalamocortical information relay.

  17. On learning time delays between the spikes from different input neurons in a biophysical model of a pyramidal neuron.

    PubMed

    Koutsou, Achilleas; Bugmann, Guido; Christodoulou, Chris

    2015-10-01

    Biological systems are able to recognise temporal sequences of stimuli or compute in the temporal domain. In this paper we are exploring whether a biophysical model of a pyramidal neuron can detect and learn systematic time delays between the spikes from different input neurons. In particular, we investigate whether it is possible to reinforce pairs of synapses separated by a dendritic propagation time delay corresponding to the arrival time difference of two spikes from two different input neurons. We examine two subthreshold learning approaches where the first relies on the backpropagation of EPSPs (excitatory postsynaptic potentials) and the second on the backpropagation of a somatic action potential, whose production is supported by a learning-enabling background current. The first approach does not provide a learning signal that sufficiently differentiates between synapses at different locations, while in the second approach, somatic spikes do not provide a reliable signal distinguishing arrival time differences of the order of the dendritic propagation time. It appears that the firing of pyramidal neurons shows little sensitivity to heterosynaptic spike arrival time differences of several milliseconds. This neuron is therefore unlikely to be able to learn to detect such differences.

  18. 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

  19. Networks of spiking neurons that compute linear functions using action potential timing

    NASA Astrophysics Data System (ADS)

    Ruf, Berthold

    1999-03-01

    For fast neural computations within the brain it is very likely that the timing of single firing events is relevant. Recently Maass has shown that under certain weak assumptions a weighted sum can be computed in temporal coding by leaky integrate-and-fire neurons. This construction can be extended to approximate arbitrary functions. In comparison to integrate-and-fire neurons there are several sources in biologically more realistic neurons for additional nonlinear effects like e.g. the spatial and temporal interaction of postsynaptic potentials or voltage-gated ion channels at the soma. Here we demonstrate with the help of computer simulations using GENESIS that despite of these nonlinearities such neurons can compute linear functions in a natural and straightforward way based on the main principles of the construction given by Maass. One only has to assume that a neuron receives all its inputs in a time interval of approximately the length of the rising segment of its excitatory postsynaptic potentials. We also show that under certain assumptions there exists within this construction some type of activation function being computed by such neurons. Finally we demonstrate that on the basis of these results it is possible to realize in a simple way pattern analysis with spiking neurons. It allows the analysis of a mixture of several learned patterns within a few milliseconds.

  20. 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

  1. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

    PubMed Central

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

    2015-01-01

    We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 226 (64M) synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted or delayed pre-synaptic spike to the post-synaptic neuron in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 236 (64G) synaptic adaptors on a current high-end FPGA platform. PMID:26041985

  2. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks.

    PubMed

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

    2015-01-01

    We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 2(26) (64M) synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted or delayed pre-synaptic spike to the post-synaptic neuron in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 2(36) (64G) synaptic adaptors on a current high-end FPGA platform.

  3. Using Multiple Whole-Cell Recordings to Study Spike-Timing-Dependent Plasticity in Acute Neocortical Slices

    PubMed Central

    Lalanne, Txomin; Abrahamsson, Therese; Sjöström, P. Jesper

    2017-01-01

    This protocol provides a method for quadruple whole-cell recording to study synaptic plasticity of neocortical connections, with a special focus on spike-timing-dependent plasticity (STDP). It also describes how to morphologically identify recorded cells from two-photon laser-scanning microscopy (2PLSM) stacks. PMID:27250948

  4. Spike timing-dependent long-term potentiation in ventral tegmental area dopamine cells requires PKC.

    PubMed

    Luu, Percy; Malenka, Robert C

    2008-07-01

    Long-term potentiation (LTP) of excitatory synapses on ventral tegmental area (VTA) dopamine (DA) cells is thought to play an important role in mediating some of the behavioral effects of drugs of abuse yet little is known about its underlying mechanisms. We find that spike timing-dependent LTP (STD LTP) in VTA DA cells is absent in slices prepared from mice previously administered cocaine, suggesting that cocaine-induced LTP and STD LTP share underlying mechanisms. This form of STD LTP is dependent on NMDA receptor (NMDAR) activation and a rise in postsynaptic calcium but surprisingly was not affected by an inhibitor of calcium/calmodulin-dependent protein kinase II (CaMKII). It was blocked by antagonists of conventional isoforms of PKC, whereas activation of protein kinase C (PKC) using a phorbol ester enhanced synaptic strength. These results suggest that NMDAR-mediated activation of PKC, but not CaMKII, is a critical trigger for LTP in VTA DA cells.

  5. A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.

    PubMed

    Dethier, Julie; Nuyujukian, Paul; Eliasmith, Chris; Stewart, Terry; Elassaad, Shauki A; Shenoy, Krishna V; Boahen, Kwabena

    2011-01-01

    Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm's velocity and mapped on to the SNN using the Neural Engineering Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.

  6. Linking Neuromodulated Spike-Timing Dependent Plasticity with the Free-Energy Principle.

    PubMed

    Isomura, Takuya; Sakai, Koji; Kotani, Kiyoshi; Jimbo, Yasuhiko

    2016-09-01

    The free-energy principle is a candidate unified theory for learning and memory in the brain that predicts that neurons, synapses, and neuromodulators work in a manner that minimizes free energy. However, electrophysiological data elucidating the neural and synaptic bases for this theory are lacking. Here, we propose a novel theory bridging the information-theoretical principle with the biological phenomenon of spike-timing dependent plasticity (STDP) regulated by neuromodulators, which we term mSTDP. We propose that by integrating an mSTDP equation, we can obtain a form of Friston's free energy (an information-theoretical function). Then we analytically and numerically show that dopamine (DA) and noradrenaline (NA) influence the accuracy of a principal component analysis (PCA) performed using the mSTDP algorithm. From the perspective of free-energy minimization, these neuromodulatory changes alter the relative weighting or precision of accuracy and prior terms, which induces a switch from pattern completion to separation. These results are consistent with electrophysiological findings and validate the free-energy principle and mSTDP. Moreover, our scheme can potentially be applied in computational psychiatry to build models of the faulty neural networks that underlie the positive symptoms of schizophrenia, which involve abnormal DA levels, as well as models of the NA contribution to memory triage and posttraumatic stress disorder.

  7. GABAergic Activities Control Spike Timing- and Frequency-Dependent Long-Term Depression at Hippocampal Excitatory Synapses

    PubMed Central

    Nishiyama, Makoto; Togashi, Kazunobu; Aihara, Takeshi; Hong, Kyonsoo

    2010-01-01

    GABAergic interneuronal network activities in the hippocampus control a variety of neural functions, including learning and memory, by regulating θ and γ oscillations. How these GABAergic activities at pre- and postsynaptic sites of hippocampal CA1 pyramidal cells differentially contribute to synaptic function and plasticity during their repetitive pre- and postsynaptic spiking at θ and γ oscillations is largely unknown. We show here that activities mediated by postsynaptic GABAARs and presynaptic GABABRs determine, respectively, the spike timing- and frequency-dependence of activity-induced synaptic modifications at Schaffer collateral-CA1 excitatory synapses. We demonstrate that both feedforward and feedback GABAAR-mediated inhibition in the postsynaptic cell controls the spike timing-dependent long-term depression of excitatory inputs (“e-LTD”) at the θ frequency. We also show that feedback postsynaptic inhibition specifically causes e-LTD of inputs that induce small postsynaptic currents (<70 pA) with LTP-timing, thus enforcing the requirement of cooperativity for induction of long-term potentiation at excitatory inputs (“e-LTP”). Furthermore, under spike-timing protocols that induce e-LTP and e-LTD at excitatory synapses, we observed parallel induction of LTP and LTD at inhibitory inputs (“i-LTP” and “i-LTD”) to the same postsynaptic cells. Finally, we show that presynaptic GABABR-mediated inhibition plays a major role in the induction of frequency-dependent e-LTD at α and β frequencies. These observations demonstrate the critical influence of GABAergic interneuronal network activities in regulating the spike timing- and frequency-dependences of long-term synaptic modifications in the hippocampus. PMID:21423508

  8. Testing the necessity of transient spikes in the drivers for creating a storm-time ring current

    NASA Astrophysics Data System (ADS)

    Liemohn, M. W.; Ilie, R.; Ridley, A. J.; Kozyra, J. U.; Thomsen, M. F.; Borovsky, J. E.

    2007-12-01

    The role of transient spikes in upstream solar wind parameters and near-Earth plasma sheet parameters is investigated through a series of numerical simulations. During magnetic storms, the near-Earth plasma sheet density (as observed at geosynchronous altitude) is often enhanced relative to its normal, quiescent level. In addition to a baseline increase of the density of up to a few per cubic centimeter lasting several hours, there are usually short-lived (a few to tens of minutes) increases on top of this (up to double the baseline). In addition, the solar wind parameters also often have numerous short-lived spikes and fluctuations within it. The question then arises of the relative contribution of these transient spikes in the drivers to the storm-time ring current intensity. To address this issue, a series of simulations are conducted using the Hot Electron and Ion Drift Integrator (HEIDI) model (formerly the Michigan version of RAM). Various running averages of the upstream solar wind conditions and geosynchronous orbit nightside boundary conditions are used to drive HEIDI. It is found that the spikes are simply adding a linear contribution to the ring current intensity over the baseline (averaged) input levels, and that any nonlinear influences occur beyond the HEIDI simulation domain (i.e., at high latitudes or in the tail). That is, the spikes do not last long enough to develop nonlinear influences on the ring current's total energy content. The HEIDI results are compared against global magnetospheric modeling results using averaged input parameters into the Space Weather Modeling Framework (SWMF), which show a nonlinear response to transient spikes.

  9. A Developmental Sensitive Period for Spike Timing-Dependent Plasticity in the Retinotectal Projection

    PubMed Central

    Tsui, Jennifer; Schwartz, Neil; Ruthazer, Edward S.

    2010-01-01

    The retinotectal projection in Xenopus laevis has been shown to exhibit correlation-based refinement of both anatomical and functional connectivity during development. Spike timing-dependent plasticity (STDP) is an appealing experimental model for correlation-based synaptic plasticity because, in contrast to plasticity induction paradigms using tetanic stimulation or sustained postsynaptic depolarization, its induction protocol more closely resembles natural physiological activity. In Xenopus tadpoles, where anatomical remodeling has been reported throughout much of the life of the animal, in vivo retinotectal STDP has only been examined under a limited set of experimental conditions. Using perforated-patch recordings of retina-evoked EPSCs in tectal neurons, we confirmed that repeatedly driving a retinotectal EPSP 5–10 ms prior to inducing an action potential in the postsynaptic cell, reliably produced timing-dependent long-term potentiation (t-LTP) of the retinotectal synapse in young wild type tadpoles (stages 41–44). At these stages, retinotectal timing-dependent long-term depression (t-LTD) also could be induced by evoking an EPSP to arrive 5–10 ms after an action potential in the tectal cell. However, retinotectal STDP using this standard protocol was limited to a developmental sensitive period, as we were unable to induce t-LTP or t-LTD after stage 44. Surprisingly, this STDP protocol also failed to induce reliable STDP in albino tadpoles at the early ages when it was effective in wild type pigmented animals. Nonetheless, low-frequency flashes to the eye produced a robust NMDA receptor-dependent retinotectal LTD in stage 47 albino tadpoles, demonstrating that the retinotectal synapse can nonetheless be modified in these animals using different plasticity paradigms. PMID:21423499

  10. Recognition of disturbances with specified morphology in time series: Part 2. Spikes on 1-s magnetograms

    NASA Astrophysics Data System (ADS)

    Soloviev, A. A.; Agayan, S. M.; Gvishiani, A. D.; Bogoutdinov, Sh. R.; Chulliat, A.

    2012-05-01

    Preliminary magnetograms contain different types of temporal anthropogenic disturbances: spikes, baseline jumps, drifts, etc. These disturbances should be identified and filtered out during the preprocessing of the preliminary records for the definitive data. As of now, at the geomagnetic observatories, such filtering is carried out manually. Most of the disturbances in the records sampled every second are spikes, which are much more abundant than those on the magnetograms sampled every minute. Another important feature of the 1-s magnetograms is the presence of a plenty of specific disturbances caused by short-period geomagnetic pulsations, which must be retained in the definitive records. Thus, creating an instrument for formalized and unified recognition of spikes on the preliminary 1-s magnetograms would largely solve the problem of labor-consuming manual preprocessing of the magnetic records. In the context of this idea, in the present paper, we focus on recognition of the spikes on the 1-s magnetograms as a key point of the problem. We describe here the new algorithm of pattern recognition, SPs, which is capable of automatically identifying the spikes on the 1-s magnetograms with a low probability of missed events and false alarms. The algorithm was verified on the real magnetic data recorded at the French observatory located on Easter Island in the Pacific.

  11. Depression biased non-Hebbian spike-timing-dependent synaptic plasticity in the rat subiculum

    PubMed Central

    Pandey, Anurag; Sikdar, Sujit Kumar

    2014-01-01

    The subiculum is a structure that forms a bridge between the hippocampus and the entorhinal cortex (EC), and plays a major role in the memory consolidation process. Here, we demonstrate spike-timing-dependent plasticity (STDP) at the proximal excitatory inputs on the subicular pyramidal neurons of juvenile rat. Causal (positive) pairing of a single EPSP with a single back-propagating action potential (bAP) after a time interval of 10 ms (+10 ms) failed to induce plasticity. However, increasing the number of bAPs in a burst to three, at two different frequencies of 50 Hz (bAP burst) and 150 Hz, induced long-term depression (LTD) after a time interval of +10 ms in both the regular-firing (RF), and the weak burst firing (WBF) neurons. The LTD amplitude decreased with increasing time interval between the EPSP and the bAP burst. Reversing the order of the pairing of the EPSP and the bAP burst induced LTP at a time interval of −10 ms. This finding is in contrast with reports at other synapses, wherein pre- before postsynaptic (causal) pairing induced LTP and vice versa. Our results reaffirm the earlier observations that the relative timing of the pre- and postsynaptic activities can lead to multiple types of plasticity profiles. The induction of timing-dependent LTD (t-LTD) was dependent on postsynaptic calcium change via NMDA receptors in the WBF neurons, while it was independent of postsynaptic calcium change, but required active L-type calcium channels in the RF neurons. Thus the mechanism of synaptic plasticity may vary within a hippocampal subfield depending on the postsynaptic neuron involved. This study also reports a novel mechanism of LTD induction, where L-type calcium channels are involved in a presynaptically induced synaptic plasticity. The findings may have strong implications in the memory consolidation process owing to the central role of the subiculum and LTD in this process. PMID:24907304

  12. Depression biased non-Hebbian spike-timing-dependent synaptic plasticity in the rat subiculum.

    PubMed

    Pandey, Anurag; Sikdar, Sujit Kumar

    2014-08-15

    The subiculum is a structure that forms a bridge between the hippocampus and the entorhinal cortex (EC), and plays a major role in the memory consolidation process. Here, we demonstrate spike-timing-dependent plasticity (STDP) at the proximal excitatory inputs on the subicular pyramidal neurons of juvenile rat. Causal (positive) pairing of a single EPSP with a single back-propagating action potential (bAP) after a time interval of 10 ms (+10 ms) failed to induce plasticity. However, increasing the number of bAPs in a burst to three, at two different frequencies of 50 Hz (bAP burst) and 150 Hz, induced long-term depression (LTD) after a time interval of +10 ms in both the regular-firing (RF), and the weak burst firing (WBF) neurons. The LTD amplitude decreased with increasing time interval between the EPSP and the bAP burst. Reversing the order of the pairing of the EPSP and the bAP burst induced LTP at a time interval of -10 ms. This finding is in contrast with reports at other synapses, wherein pre- before postsynaptic (causal) pairing induced LTP and vice versa. Our results reaffirm the earlier observations that the relative timing of the pre- and postsynaptic activities can lead to multiple types of plasticity profiles. The induction of timing-dependent LTD (t-LTD) was dependent on postsynaptic calcium change via NMDA receptors in the WBF neurons, while it was independent of postsynaptic calcium change, but required active L-type calcium channels in the RF neurons. Thus the mechanism of synaptic plasticity may vary within a hippocampal subfield depending on the postsynaptic neuron involved. This study also reports a novel mechanism of LTD induction, where L-type calcium channels are involved in a presynaptically induced synaptic plasticity. The findings may have strong implications in the memory consolidation process owing to the central role of the subiculum and LTD in this process.

  13. Kisspeptin inhibits a slow afterhyperpolarization current via protein kinase C and reduces spike frequency adaptation in GnRH neurons

    PubMed Central

    Zhang, Chunguang

    2013-01-01

    Kisspeptin signaling via its cognate receptor G protein-coupled receptor 54 (GPR54) in gonadotropin-releasing hormone (GnRH) neurons plays a critical role in regulating pituitary secretion of luteinizing hormone and thus reproductive function. GPR54 is Gq-coupled to activation of phospholipase C and multiple second messenger signaling pathways. Previous studies have shown that kisspeptin potently depolarizes GnRH neurons through the activation of canonical transient receptor potential channels and inhibition of inwardly rectifying K+ channels to generate sustained firing. Since the initial studies showing that kisspeptin has prolonged effects, the question has been why is there very little spike frequency adaption during sustained firing? Presently, we have discovered that kisspeptin reduces spike frequency adaptation and prolongs firing via the inhibition of a calcium-activated slow afterhyperpolarization current (IsAHP). GnRH neurons expressed two distinct IsAHP, a kisspeptin-sensitive and an apamin-sensitive IsAHP. Essentially, kisspeptin inhibited 50% of the IsAHP and apamin inhibited the other 50% of the current. Furthermore, the kisspeptin-mediated inhibition of IsAHP was abrogated by the protein kinase C (PKC) inhibitor calphostin C, and the PKC activator phorbol 12,13-dibutyrate mimicked and occluded any further effects of kisspeptin on IsAHP. The protein kinase A (PKA) inhibitors H-89 and the Rp diastereomer of adenosine 3′,5′-cyclic monophosphorothioate had no effect on the kisspeptin-mediated inhibition but were able to abrogate the inhibitory effects of forskolin on the IsAHP, suggesting that PKA is not involved. Therefore, in addition to increasing the firing rate through an overt depolarization, kisspeptin can also facilitate sustained firing through inhibiting an apamin-insensitive IsAHP in GnRH neurons via a PKC. PMID:23548613

  14. Stochastic resonance, coherence resonance, and spike timing reliability of Hodgkin-Huxley neurons with ion-channel noise

    NASA Astrophysics Data System (ADS)

    Yu, Haitao; Galán, Roberto F.; Wang, Jiang; Cao, Yibin; Liu, Jing

    2017-04-01

    The random transitions of ion channels between open and closed states are a major source of noise in neurons. In this study, we investigate the stochastic dynamics of a single Hodgkin-Huxley (HH) neuron with realistic, physiological channel noise, which depends on the channel number and the voltage potential of the membrane. Without external input, the stochastic HH model can generate spontaneous spikes induced by ion-channel noise, and the variability of inter-spike intervals attains a minimum for an optimal membrane area, a phenomenon known as coherence resonance. When a subthreshold periodic input current is added, the neuron can optimally detect the input frequency for an intermediate membrane area, corresponding to the phenomenon of stochastic resonance. We also investigate spike timing reliability of neuronal responses to repeated presentations of the same stimulus with different realizations of channel noise. We show that, with increasing membrane area, the reliability of neuronal response decreases for subthreshold periodic inputs, and attains a minimum for suprathreshold inputs. Furthermore, Arnold tongues of high reliability arise in a two-dimensional plot of frequency and amplitude of the sinusoidal input current, resulting from the resonance effect of spike timing reliability.

  15. Real-time classification and sensor fusion with a spiking deep belief network

    PubMed Central

    O'Connor, Peter; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi; Pfeiffer, Michael

    2013-01-01

    Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input. PMID:24115919

  16. Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model.

    PubMed

    Luz, Yotam; Shamir, Maoz

    2016-04-01

    Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. Next, we analyze STDP-driven synaptic dynamics of a pre-synaptic population of neurons onto a single post-synaptic cell. The pre-synaptic neural population is assumed to be oscillating at the same frequency, albeit with different phases, such that the net activity of the pre-synaptic population is constant in time. Thus, in the homogeneous case in which all synapses are equal, the post-synaptic neuron receives constant input and hence does not oscillate. To investigate the transition to oscillatory activity, we develop a mean-field Fokker-Planck approximation of the synaptic dynamics. We analyze the conditions causing the homogeneous solution to lose its stability. The findings show that oscillatory activity appears through a mechanism of spontaneous symmetry breaking. However, in the general case the homogeneous solution is unstable, and the synaptic dynamics does not converge to a different fixed point, but rather to a limit cycle. We show how the temporal structure of the STDP rule determines the stability of the homogeneous solution and the drift velocity of the limit cycle.

  17. Real-time classification and sensor fusion with a spiking deep belief network.

    PubMed

    O'Connor, Peter; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi; Pfeiffer, Michael

    2013-01-01

    Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input.

  18. Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study

    NASA Astrophysics Data System (ADS)

    Shimazaki, Hideaki

    2013-12-01

    Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of neuronal interactions which is reproducible in repeated trials under identical experimental conditions. However, the method may not be suitable for detecting stimulus responses if the neuronal dynamics exhibits significant variability across trials. In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity. In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects. For this goal, we model ensemble neuronal activity as a latent process and include the stimulus and spike-history effects as exogenous inputs to the latent process. We develop an expectation-maximization algorithm that simultaneously achieves estimation of the latent process, stimulus responses, and spike-history effects. The proposed method is useful to analyze an interaction of internal cortical states and sensory evoked activity.

  19. 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

  20. Dynamically Sliding Threshold Model Reproduces the Initial-Strength Dependence of Spike-Timing Dependent Synaptic Plasticity

    NASA Astrophysics Data System (ADS)

    Kurashige, Hiroki; Sakai, Yutaka

    2007-11-01

    It has been considered that an amount of calcium elevation in a synaptic spine determines whether the synapse is potentiated or depressed. However, it has been pointed out that simple application of the principle can not reproduce properties of spike-timing-dependent plasticity (STDP). To solve the problem, we present a possible mechanism using dynamically sliding threshold determined as the linear summation of calcium elevations induced by single pre- and post-synaptic spikes. We demonstrate that the model can reproduce the timing dependence of biological STDP. In addition, we find that the model can reproduce the dependence of biological STDP on the initial synaptic strength, which is found to be asymmetric for synaptic potentiation and depression, whereas no explicit initial-strength dependence nor asymmetric mechanism are incorporated into the model.

  1. Effects of the spike timing-dependent plasticity on the synchronisation in a random Hodgkin-Huxley neuronal network

    NASA Astrophysics Data System (ADS)

    Borges, R. R.; Borges, F. S.; Lameu, E. L.; Batista, A. M.; Iarosz, K. C.; Caldas, I. L.; Viana, R. L.; Sanjuán, M. A. F.

    2016-05-01

    In this paper, we study the effects of spike timing-dependent plasticity on synchronisation in a network of Hodgkin-Huxley neurons. Neuron plasticity is a flexible property of a neuron and its network to change temporarily or permanently their biochemical, physiological, and morphological characteristics, in order to adapt to the environment. Regarding the plasticity, we consider Hebbian rules, specifically for spike timing-dependent plasticity (STDP), and with regard to network, we consider that the connections are randomly distributed. We analyse the synchronisation and desynchronisation according to an input level and probability of connections. Moreover, we verify that the transition for synchronisation depends on the neuronal network architecture, and the external perturbation level.

  2. STICK: Spike Time Interval Computational Kernel, a Framework for General Purpose Computation Using Neurons, Precise Timing, Delays, and Synchrony.

    PubMed

    Lagorce, Xavier; Benosman, Ryad

    2015-11-01

    There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimic biology. They use neural networks, which can be trained to perform specific tasks to mainly solve pattern recognition problems. These machines can do more than simulate biology; they allow us to rethink our current paradigm of computation. The ultimate goal is to develop brain-inspired general purpose computation architectures that can breach the current bottleneck introduced by the von Neumann architecture. This work proposes a new framework for such a machine. We show that the use of neuron-like units with precise timing representation, synaptic diversity, and temporal delays allows us to set a complete, scalable compact computation framework. The framework provides both linear and nonlinear operations, allowing us to represent and solve any function. We show usability in solving real use cases from simple differential equations to sets of nonlinear differential equations leading to chaotic attractors.

  3. Mature BDNF, but not proBDNF, reduces excitability of fast-spiking interneurons in mouse dentate gyrus.

    PubMed

    Holm, Mai Marie; Nieto-Gonzalez, Jose Luis; Vardya, Irina; Vaegter, Christian Bjerggaard; Nykjaer, Anders; Jensen, Kimmo

    2009-10-07

    Mature BDNF and its precursor proBDNF may both be secreted to exert opposite effects on synaptic plasticity in the hippocampus. However, it is unknown how proBDNF and mature BDNF affect the excitability of GABAergic interneurons and thereby regulate GABAergic inhibition. We made recordings of GABAergic spontaneous IPSCs (sIPSCs) in mouse dentate gyrus granule cells and found that chronic or acute BDNF reductions led to large increases in the sIPSC frequencies, which were TTX (tetrodotoxin) sensitive and therefore action-potential driven. Conversely, addition of mature BDNF, but not proBDNF, within minutes led to a decrease in the sIPSC frequency to 44%. Direct recordings from fast-spiking GABAergic interneurons revealed that mature BDNF reduced their excitability and depressed their action potential firing, whereas proBDNF had no effect. Using the TrkB inhibitor K-252a, or mice deficient for the common neurotrophin receptor p75(NTR), the regulation of GABAergic activity was shown specifically to be mediated by BDNF binding to the neurotrophin receptor TrkB. In agreement, immunohistochemistry demonstrated that TrkB, but not p75(NTR), was expressed in parvalbumin-positive interneurons. Our results suggest that mature BDNF decreases the excitability of GABAergic interneurons via activation of TrkB, while proBDNF does not impact on GABAergic activity. Thus, by affecting the firing of GABAergic interneurons, mature BDNF may play an important role in regulating network oscillations in the hippocampus.

  4. Comparison of surgical time and IOP spikes with two ophthalmic viscosurgical devices following Visian STAAR (ICL, V4c model) insertion in the immediate postoperative period

    PubMed Central

    Ganesh, Sri; Brar, Sheetal

    2016-01-01

    Purpose To compare the effect of two ocular viscosurgical devices (OVDs) on intraocular pressure (IOP) and surgical time in immediate postoperative period after bilateral implantable collamer lens (using the V4c model) implantation. Methods A total of 20 eligible patients were randomized to receive 2% hydroxypropylmethylcellulose (HPMC) in one eye and 1% hyaluronic acid in fellow eye. Time taken for complete removal of OVD and total surgical time were recorded. At the end of surgery, IOP was adjusted between 15 and 20 mmHg in both the eyes. Results Mean time for complete OVD evacuation and total surgical time were significantly higher in the HPMC group (P=0.00). Four eyes in the HPMC group had IOP spike, requiring treatment. IOP values with noncontact tonometry at 1, 2, 4, 24, and 48 hours were not statistically significant (P>0.05) for both the groups. Conclusion The study concluded that 1% hyaluronic acid significantly reduces total surgical time, and incidence of acute spikes may be lower compared to 2% HPMC when used for implantable collamer lens (V4c model). PMID:26869754

  5. 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

  6. Neonatal Tissue Damage Promotes Spike Timing-Dependent Synaptic Long-Term Potentiation in Adult Spinal Projection Neurons

    PubMed Central

    Li, Jie

    2016-01-01

    Mounting evidence from both humans and rodents suggests that tissue damage during the neonatal period can “prime” developing nociceptive pathways such that a subsequent injury during adulthood causes an exacerbated degree of pain hypersensitivity. However, the cellular and molecular mechanisms that underlie this priming effect remain poorly understood. Here, we demonstrate that neonatal surgical injury relaxes the timing rules governing long-term potentiation (LTP) at mouse primary afferent synapses onto mature lamina I projection neurons, which serve as a major output of the spinal nociceptive network and are essential for pain perception. In addition, whereas LTP in naive mice was only observed if the presynaptic input preceded postsynaptic firing, early tissue injury removed this temporal requirement and LTP was observed regardless of the order in which the inputs were activated. Neonatal tissue damage also reduced the dependence of spike-timing-dependent LTP on NMDAR activation and unmasked a novel contribution of Ca2+-permeable AMPARs. These results suggest for the first time that transient tissue damage during early life creates a more permissive environment for the production of LTP within adult spinal nociceptive circuits. This persistent metaplasticity may promote the excessive amplification of ascending nociceptive transmission to the mature brain and thereby facilitate the generation of chronic pain after injury, thus representing a novel potential mechanism by which early trauma can prime adult pain pathways in the CNS. SIGNIFICANCE STATEMENT Tissue damage during early life can “prime” developing nociceptive pathways in the CNS, leading to greater pain severity after repeat injury via mechanisms that remain poorly understood. Here, we demonstrate that neonatal surgical injury widens the timing window during which correlated presynaptic and postsynaptic activity can evoke long-term potentiation (LTP) at sensory synapses onto adult lamina I

  7. Comparison of DNA extraction kits for detection of Burkholderia pseudomallei in spiked human whole blood using real-time PCR.

    PubMed

    Podnecky, Nicole L; Elrod, Mindy G; Newton, Bruce R; Dauphin, Leslie A; Shi, Jianrong; Chawalchitiporn, Sutthinan; Baggett, Henry C; Hoffmaster, Alex R; Gee, Jay E

    2013-01-01

    Burkholderia pseudomallei, the etiologic agent of melioidosis, is endemic in northern Australia and Southeast Asia and can cause severe septicemia that may lead to death in 20% to 50% of cases. Rapid detection of B. pseudomallei infection is crucial for timely treatment of septic patients. This study evaluated seven commercially available DNA extraction kits to determine the relative recovery of B. pseudomallei DNA from spiked EDTA-containing human whole blood. The evaluation included three manual kits: the QIAamp DNA Mini kit, the QIAamp DNA Blood Mini kit, and the High Pure PCR Template Preparation kit; and four automated systems: the MagNAPure LC using the DNA Isolation Kit I, the MagNAPure Compact using the Nucleic Acid Isolation Kit I, and the QIAcube using the QIAamp DNA Mini kit and the QIAamp DNA Blood Mini kit. Detection of B. pseudomallei DNA extracted by each kit was performed using the B. pseudomallei specific type III secretion real-time PCR (TTS1) assay. Crossing threshold (C T ) values were used to compare the limit of detection and reproducibility of each kit. This study also compared the DNA concentrations and DNA purity yielded for each kit. The following kits consistently yielded DNA that produced a detectable signal from blood spiked with 5.5×10(4) colony forming units per mL: the High Pure PCR Template Preparation, QIAamp DNA Mini, MagNA Pure Compact, and the QIAcube running the QIAamp DNA Mini and QIAamp DNA Blood Mini kits. The High Pure PCR Template Preparation kit yielded the lowest limit of detection with spiked blood, but when this kit was used with blood from patients with confirmed cases of melioidosis, the bacteria was not reliably detected indicating blood may not be an optimal specimen.

  8. Entorhinal-CA3 Dual-Input Control of Spike Timing in the Hippocampus by Theta-Gamma Coupling.

    PubMed

    Fernández-Ruiz, Antonio; Oliva, Azahara; Nagy, Gergő A; Maurer, Andrew P; Berényi, Antal; Buzsáki, György

    2017-03-08

    Theta-gamma phase coupling and spike timing within theta oscillations are prominent features of the hippocampus and are often related to navigation and memory. However, the mechanisms that give rise to these relationships are not well understood. Using high spatial resolution electrophysiology, we investigated the influence of CA3 and entorhinal inputs on the timing of CA1 neurons. The theta-phase preference and excitatory strength of the afferent CA3 and entorhinal inputs effectively timed the principal neuron activity, as well as regulated distinct CA1 interneuron populations in multiple tasks and behavioral states. Feedback potentiation of distal dendritic inhibition by CA1 place cells attenuated the excitatory entorhinal input at place field entry, coupled with feedback depression of proximal dendritic and perisomatic inhibition, allowing the CA3 input to gain control toward the exit. Thus, upstream inputs interact with local mechanisms to determine theta-phase timing of hippocampal neurons to support memory and spatial navigation.

  9. Light gas gun with reduced timing jitter

    DOEpatents

    Laabs, G.W.; Funk, D.J.; Asay, B.W.

    1998-06-09

    Gas gun with reduced timing jitter is disclosed. A gas gun having a prepressurized projectile held in place with a glass rod in compression is described. The glass rod is destroyed with an explosive at a precise time which allows a restraining pin to be moved and free the projectile. 4 figs.

  10. Light gas gun with reduced timing jitter

    DOEpatents

    Laabs, Gary W.; Funk, David J.; Asay, Blaine W.

    1998-01-01

    Gas gun with reduced timing jitter. A gas gun having a prepressurized projectile held in place with a glass rod in compression is described. The glass rod is destroyed with an explosive at a precise time which allows a restraining pin to be moved and free the projectile.

  11. Dopamine regulates intrinsic excitability thereby gating successful induction of spike timing-dependent plasticity in CA1 of the hippocampus

    PubMed Central

    Edelmann, Elke; Lessmann, Volkmar

    2013-01-01

    Long-term potentiation (LTP) and long-term depression (LTD) are generally assumed to be cellular correlates for learning and memory. Different types of LTP induction protocols differing in severity of stimulation can be distinguished in CA1 of the hippocampus. To better understand signaling mechanisms and involvement of neuromodulators such as dopamine (DA) in synaptic plasticity, less severe and more physiological low frequency induction protocols should be used. In the study which is reviewed here, critical determinants of spike timing-dependent plasticity (STDP) at hippocampal CA3-CA1 synapses were investigated. We found that DA via D1 receptor signaling, but not adrenergic signaling activated by the β-adrenergic agonist isoproterenol, is important for successful expression of STDP at CA3-CA1 synapses. The DA effect on STDP is paralleled by changes in spike firing properties, thereby changing intrinsic excitability of postsynaptic CA1 neurons, and gating STDP. Whereas β-adrenergic signaling also leads to a similar (but not identical) regulation of firing pattern, it does not enable STDP. In this focused review we will discuss the current literature on dopaminergic modulation of LTP in CA1, with a special focus on timing dependent (t-)LTP, and we will suggest possible reasons for the selective gating of STDP by DA [but not noradrenaline (NA)] in CA1. PMID:23508132

  12. Dopamine regulates intrinsic excitability thereby gating successful induction of spike timing-dependent plasticity in CA1 of the hippocampus.

    PubMed

    Edelmann, Elke; Lessmann, Volkmar

    2013-01-01

    Long-term potentiation (LTP) and long-term depression (LTD) are generally assumed to be cellular correlates for learning and memory. Different types of LTP induction protocols differing in severity of stimulation can be distinguished in CA1 of the hippocampus. To better understand signaling mechanisms and involvement of neuromodulators such as dopamine (DA) in synaptic plasticity, less severe and more physiological low frequency induction protocols should be used. In the study which is reviewed here, critical determinants of spike timing-dependent plasticity (STDP) at hippocampal CA3-CA1 synapses were investigated. We found that DA via D1 receptor signaling, but not adrenergic signaling activated by the β-adrenergic agonist isoproterenol, is important for successful expression of STDP at CA3-CA1 synapses. The DA effect on STDP is paralleled by changes in spike firing properties, thereby changing intrinsic excitability of postsynaptic CA1 neurons, and gating STDP. Whereas β-adrenergic signaling also leads to a similar (but not identical) regulation of firing pattern, it does not enable STDP. In this focused review we will discuss the current literature on dopaminergic modulation of LTP in CA1, with a special focus on timing dependent (t-)LTP, and we will suggest possible reasons for the selective gating of STDP by DA [but not noradrenaline (NA)] in CA1.

  13. Real-time prediction of neuronal population spiking activity using FPGA.

    PubMed

    Li, Will X Y; Cheung, Ray C C; Chan, Rosa H M; Song, Dong; Berger, Theodore W

    2013-08-01

    A field-programmable gate array (FPGA)-based hardware architecture is proposed and utilized for prediction of neuronal population firing activity. The hardware system adopts the multi-input multi-output (MIMO) generalized Laguerre-Volterra model (GLVM) structure to describe the nonlinear dynamic neural process of mammalian brain and can switch between the two important functions: estimation of GLVM coefficients and prediction of neuronal population spiking activity (model outputs). The model coefficients are first estimated using the in-sample training data; then the output is predicted using the out-of-sample testing data and the field estimated coefficients. Test results show that compared with previous software implementation of the generalized Laguerre-Volterra algorithm running on an Intel Core i7-2620M CPU, the FPGA-based hardware system can achieve up to 2.66×10(3) speedup in doing model parameters estimation and 698.84 speedup in doing model output prediction. The proposed hardware platform will facilitate research on the highly nonlinear neural process of the mammal brain, and the cognitive neural prosthesis design.

  14. Robustness of retrieval properties against imbalance between long-term potentiation and depression of spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Matsumoto, Narihisa; Okada, Masato

    2003-12-01

    Spike-timing-dependent plasticity (STDP) has recently been shown in some physiological studies. STDP depends on the precise temporal relationship of presynaptic and postsynaptic spikes. Many authors have indicated that a precise balance between long-term potentiation (LTP) and long-term depression (LTD) of STDP is significant for a stable learning. However, a situation in which the balance is maintained precisely is inconceivable in the brain. Using a method of the statistical neurodynamics, we show robust retrieval properties of spatiotemporal patterns in an associative memory model against the imbalance between LTP and LTD. When the fluctuation of LTD is assumed to obey a Gaussian distribution with mean 0 and variance δ2, the storage capacity takes a finite value even at large δ. This means that the balance between LTP and LTD of STDP need not be maintained precisely, but must be maintained on average. Furthermore, we found that the basin of attraction becomes smaller as δ increases while an initial critical overlap remains unchanged.

  15. Developmental regulation of CB1-mediated spike-time dependent depression at immature mossy fiber-CA3 synapses

    PubMed Central

    Caiati, Maddalena D.; Sivakumaran, Sudhir; Lanore, Frederic; Mulle, Christophe; Richard, Elodie; Verrier, Dany; Marsicano, Giovanni; Miles, Richard; Cherubini, Enrico

    2012-01-01

    Early in postnatal life, mossy fibres (MF), the axons of granule cells in the dentate gyrus, release GABA which is depolarizing and excitatory. Synaptic currents undergo spike-time dependent long-term depression (STD-LTD) regardless of the temporal order of stimulation (pre versus post and viceversa). Here we show that at P3 but not at P21, STD-LTD, induced by negative pairing, is mediated by endocannabinoids mobilized from the postsynaptic cell during spiking-induced membrane depolarization. By diffusing backward, endocannabinoids activate cannabinoid type-1 (CB1) receptors probably expressed on MF. Thus, STD-LTD was prevented by CB1 receptor antagonists and was absent in CB1-KO mice. Consistent with these data, in situ hybridization experiments revealed detectable level of CB1 mRNA in the granule cell layer at P3 but not at P21. These results indicate that CB1 receptors are transiently expressed on immature MF terminals where they counteract the enhanced neuronal excitability induced by the excitatory action of GABA. PMID:22368777

  16. 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

  17. Spike-train acquisition, analysis and real-time experimental control using a graphical programming language (LabView).

    PubMed

    Nordstrom, M A; Mapletoft, E A; Miles, T S

    1995-11-01

    A solution is described for the acquisition on a personal computer of standard pulses derived from neuronal discharge, measurement of neuronal discharge times, real-time control of stimulus delivery based on specified inter-pulse interval conditions in the neuronal spike train, and on-line display and analysis of the experimental data. The hardware consisted of an Apple Macintosh IIci computer and a plug-in card (National Instruments NB-MIO16) that supports A/D, D/A, digital I/O and timer functions. The software was written in the object-oriented graphical programming language LabView. Essential elements of the source code of the LabView program are presented and explained. The use of the system is demonstrated in an experiment in which the reflex responses to muscle stretch are assessed for a single motor unit in the human masseter muscle.

  18. Spike Timing-Dependent Plasticity in the Long-Latency Stretch Reflex Following Paired Stimulation from a Wearable Electronic Device.

    PubMed

    Foysal, K M Riashad; de Carvalho, Felipe; Baker, Stuart N

    2016-10-19

    The long-latency stretch reflex (LLSR) in human elbow muscles probably depends on multiple pathways; one possible contributor is the reticulospinal tract. Here we attempted to induce plastic changes in the LLSR by pairing noninvasive stimuli that are known to activate reticulospinal pathways, at timings predicted to cause spike timing-dependent plasticity in the brainstem. In healthy human subjects, reflex responses in flexor muscles were recorded following extension perturbations at the elbow. Subjects were then fitted with a portable device that delivered auditory click stimuli through an earpiece, and electrical stimuli around motor threshold to the biceps muscle via surface electrodes. We tested the following four paradigms: biceps stimulus 10 ms before click (Bi-10ms-C); click 25 ms before biceps (C-25ms-Bi); click alone (C only); and biceps alone (Bi only). The average stimulus rate was 0.67 Hz. Subjects left the laboratory wearing the device and performed normal daily activities. Approximately 7 h later, they returned, and stretch reflexes were remeasured. The LLSR was significantly enhanced in the biceps muscle (on average by 49%) after the Bi-10ms-C paradigm, but was suppressed for C-25ms-Bi (by 31%); it was unchanged for Bi only and C only. No paradigm induced LLSR changes in the unstimulated brachioradialis muscle. Although we cannot exclude contributions from spinal or cortical pathways, our results are consistent with spike timing-dependent plasticity in reticulospinal circuits, specific to the stimulated muscle. This is the first demonstration that the LLSR can be modified via paired-pulse methods, and may open up new possibilities in motor systems neuroscience and rehabilitation.

  19. Spike sorting of synchronous spikes from local neuron ensembles

    PubMed Central

    Pröpper, Robert; Alle, Henrik; Meier, Philipp; Geiger, Jörg R. P.; Obermayer, Klaus; Munk, Matthias H. J.

    2015-01-01

    Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons. PMID:26289473

  20. Spike-Timing Dependent Plasticity Beyond Synapse – Pre- and Post-Synaptic Plasticity of Intrinsic Neuronal Excitability

    PubMed Central

    Debanne, Dominique; Poo, Mu-Ming

    2010-01-01

    Long-lasting plasticity of synaptic transmission is classically thought to be the cellular substrate for information storage in the brain. Recent data indicate however that it is not the whole story and persistent changes in the intrinsic neuronal excitability have been shown to occur in parallel to the induction of long-term synaptic modifications. This form of plasticity depends on the regulation of voltage-gated ion channels. Here we review the experimental evidence for plasticity of neuronal excitability induced at pre- or postsynaptic sites when long-term plasticity of synaptic transmission is induced with Spike-Timing Dependent Plasticity (STDP) protocols. We describe the induction and expression mechanisms of the induced changes in excitability. Finally, the functional synergy between synaptic and non-synaptic plasticity and their spatial extent are discussed. PMID:21423507

  1. Spike-timing-dependent potentiation of sensory surround in the somatosensory cortex is facilitated by deprivation-mediated disinhibition.

    PubMed

    Gambino, Frédéric; Holtmaat, Anthony

    2012-08-09

    Functional maps in the cerebral cortex reorganize in response to changes in experience, but the synaptic underpinnings remain uncertain. Here, we demonstrate that layer (L) 2/3 pyramidal cell synapses in mouse barrel cortex can be potentiated upon pairing of whisker-evoked postsynaptic potentials (PSPs) with action potentials (APs). This spike-timing-dependent long-term potentiation (STD-LTP) was only effective for PSPs evoked by deflections of a whisker in the neuron's receptive field center, and not its surround. Trimming of all except two whiskers rapidly opened the possibility to drive STD-LTP by the spared surround whisker. This facilitated STD-LTP was associated with a strong decrease in the surrounding whisker-evoked inhibitory conductance and partially occluded picrotoxin-mediated LTP facilitation. Taken together, our data demonstrate that sensory deprivation-mediated disinhibition facilitates STD-LTP from the sensory surround, which may promote correlation- and experience-dependent expansion of receptive fields.

  2. Detection of cashew nut DNA in spiked baked goods using a real-time polymerase chain reaction method.

    PubMed

    Brzezinski, Jennifer L

    2006-01-01

    The detection of potentially allergenic foods, such as tree nuts, in food products is a major concern for the food processing industry. A real-time polymerase chain reaction (PCR) method was designed to determine the presence of cashew DNA in food products. The PCR amplifies a 67 bp fragment of the cashew 2S albumin gene, which is detected with a cashew-specific, dual-labeled TaqMan probe. This reaction will not amplify DNA derived from other tree nut species, such as almond, Brazil nut, hazelnut, and walnut, as well as 4 varieties of peanut. This assay was sensitive enough to detect 5 pg purified cashew DNA as well as cashew DNA in a spiked chocolate cookie sample containing 0.01% (100 mg/kg) cashew.

  3. Influence of Contact Time on the Extraction of 233Uranyl Spike and Contaminant Uranium From Hanford Sediment

    SciTech Connect

    Smith, Steven C.; Szecsody, James E.

    2011-11-01

    In this study 233Uranyl nitrate was added to uranium (U) contaminated Hanford 300 Area sediment and incubated under moist conditions for 1 year. It hypothesized that geochemical transformations and/or physical processes will result in decreased extractability of 233U as the incubation period increases, and eventually the extraction behavior of the 233U spike will be congruent to contaminant U that has been associated with sediment for decades. Following 1 week, 1 month, and 1 year incubation periods, sediment extractions were performed using either batch or dynamic (sediment column flow) chemical extraction techniques. Overall, extraction of U from sediment using batch extraction was less complicated to conduct compared to dynamic extraction, but dynamic extraction could distinguish the range of U forms associated with sediment which are eluted at different times.

  4. Spike-timing-dependent plasticity enhanced synchronization transitions induced by autapses in adaptive Newman-Watts neuronal networks.

    PubMed

    Gong, Yubing; Wang, Baoying; Xie, Huijuan

    2016-12-01

    In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on synchronization transitions induced by autaptic activity in adaptive Newman-Watts Hodgkin-Huxley neuron networks. It is found that synchronization transitions induced by autaptic delay vary with the adjusting rate Ap of STDP and become strongest at a certain Ap value, and the Ap value increases when network randomness or network size increases. It is also found that the synchronization transitions induced by autaptic delay become strongest at a certain network randomness and network size, and the values increase and related synchronization transitions are enhanced when Ap increases. These results show that there is optimal STDP that can enhance the synchronization transitions induced by autaptic delay in the adaptive neuronal networks. These findings provide a new insight into the roles of STDP and autapses for the information transmission in neural systems.

  5. Identification of Pre-Spike Network in Patients with Mesial Temporal Lobe Epilepsy

    PubMed Central

    Faizo, Nahla L.; Burianová, Hana; Gray, Marcus; Hocking, Julia; Galloway, Graham; Reutens, David

    2014-01-01

    Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation. PMID

  6. Training Deep Spiking Neural Networks Using Backpropagation

    PubMed Central

    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. PMID:27877107

  7. 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.

  8. Spiking neural network for recognizing spatiotemporal sequences of spikes

    NASA Astrophysics Data System (ADS)

    Jin, Dezhe Z.

    2004-02-01

    Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons.

  9. Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity

    PubMed Central

    Eguchi, Akihiro; Neymotin, Samuel A.; Stringer, Simon M.

    2014-01-01

    Although many computational models have been proposed to explain orientation maps in primary visual cortex (V1), it is not yet known how similar clusters of color-selective neurons in macaque V1/V2 are connected and develop. In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. Each color input is decomposed into a red, green, and blue representation and transmitted to the visual cortex via a simulated optic nerve in a luminance channel and red–green and blue–yellow opponent color channels. Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. Each neuron in the V1 output layer makes synaptic connections to neighboring neurons and receives the three types of signals in the different channels from the corresponding photoreceptor position. Synaptic weights are randomized and learned using spike-timing-dependent plasticity (STDP). After training with natural images, the neurons display heightened sensitivity to specific colors. Information-theoretic analysis reveals mutual information between particular stimuli and responses, and that the information reaches a maximum with fewer neurons in the higher layers, indicating that estimations of the input colors can be done using the output of fewer cells in the later stages of cortical processing. In addition, cells with similar color receptive fields form clusters. Analysis of spiking activity reveals increased firing synchrony between neurons when particular color inputs are presented or removed (ON-cell/OFF-cell). PMID:24659956

  10. Time-recovering PCI-AER interface for bio-inspired spiking systems

    NASA Astrophysics Data System (ADS)

    Paz-Vicente, R.; Linares-Barranco, A.; Cascado, D.; Vicente, S.; Jimenez, G.; Civit, A.

    2005-06-01

    Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate 'events' according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems it is absolutely necessary to have a computer interface that allows (a) to read AER interchip traffic into the computer and visualize it on screen, and (b) inject a sequence of events at some point of the AER structure. This is necessary for testing and debugging complex AER systems. This paper presents a PCI to AER interface, that dispatches a sequence of events received from the PCI bus with embedded timing information to establish when each event will be delivered. A set of specialized states machines has been introduced to recovery the possible time delays introduced by the asynchronous AER bus. On the input channel, the interface capture events assigning a timestamp and delivers them through the PCI bus to MATLAB applications. It has been implemented in real time hardware using VHDL and it has been tested in a PCI-AER board, developed by authors, that includes a Spartan II 200 FPGA. The demonstration hardware is currently capable to send and receive events at a peak rate of 8,3 Mev/sec, and a typical rate of 1 Mev/sec.

  11. Time-accurate unsteady flow simulations supporting the SRM T+68-second pressure spike anomaly investigation (STS-54B)

    NASA Astrophysics Data System (ADS)

    Dougherty, N. S.; Burnette, D. W.; Holt, J. B.; Matienzo, Jose

    1993-07-01

    Time-accurate unsteady flow simulations are being performed supporting the SRM T+68sec pressure 'spike' anomaly investigation. The anomaly occurred in the RH SRM during the STS-54 flight (STS-54B) but not in the LH SRM (STS-54A) causing a momentary thrust mismatch approaching the allowable limit at that time into the flight. Full-motor internal flow simulations using the USA-2D axisymmetric code are in progress for the nominal propellant burn-back geometry and flow conditions at T+68-sec--Pc = 630 psi, gamma = 1.1381, T(sub c) = 6200 R, perfect gas without aluminum particulate. In a cooperative effort with other investigation team members, CFD-derived pressure loading on the NBR and castable inhibitors was used iteratively to obtain nominal deformed geometry of each inhibitor, and the deformed (bent back) inhibitor geometry was entered into this model. Deformed geometry was computed using structural finite-element models. A solution for the unsteady flow has been obtained for the nominal flow conditions (existing prior to the occurrence of the anomaly) showing sustained standing pressure oscillations at nominally 14.5 Hz in the motor IL acoustic mode that flight and static test data confirm to be normally present at this time. Average mass flow discharged from the nozzle was confirmed to be the nominal expected (9550 lbm/sec). The local inlet boundary condition is being perturbed at the location of the presumed reconstructed anomaly as identified by interior ballistics performance specialist team members. A time variation in local mass flow is used to simulate sudden increase in burning area due to localized propellant grain cracks. The solution will proceed to develop a pressure rise (proportional to total mass flow rate change squared). The volume-filling time constant (equivalent to 0.5 Hz) comes into play in shaping the rise rate of the developing pressure 'spike' as it propagates at the speed of sound in both directions to the motor head end and nozzle. The

  12. 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.

  13. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    NASA Astrophysics Data System (ADS)

    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.

  14. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    SciTech Connect

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-11-15

    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.

  15. Reducing the computational footprint for real-time BCPNN learning

    PubMed Central

    Vogginger, Bernhard; Schüffny, René; Lansner, Anders; Cederström, Love; Partzsch, Johannes; Höppner, Sebastian

    2015-01-01

    The implementation of synaptic plasticity in neural simulation or neuromorphic hardware is usually very resource-intensive, often requiring a compromise between efficiency and flexibility. A versatile, but computationally-expensive plasticity mechanism is provided by the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm. Building upon Bayesian statistics, and having clear links to biological plasticity processes, the BCPNN learning rule has been applied in many fields, ranging from data classification, associative memory, reward-based learning, probabilistic inference to cortical attractor memory networks. In the spike-based version of this learning rule the pre-, postsynaptic and coincident activity is traced in three low-pass-filtering stages, requiring a total of eight state variables, whose dynamics are typically simulated with the fixed step size Euler method. We derive analytic solutions allowing an efficient event-driven implementation of this learning rule. Further speedup is achieved by first rewriting the model which reduces the number of basic arithmetic operations per update to one half, and second by using look-up tables for the frequently calculated exponential decay. Ultimately, in a typical use case, the simulation using our approach is more than one order of magnitude faster than with the fixed step size Euler method. Aiming for a small memory footprint per BCPNN synapse, we also evaluate the use of fixed-point numbers for the state variables, and assess the number of bits required to achieve same or better accuracy than with the conventional explicit Euler method. All of this will allow a real-time simulation of a reduced cortex model based on BCPNN in high performance computing. More important, with the analytic solution at hand and due to the reduced memory bandwidth, the learning rule can be efficiently implemented in dedicated or existing digital neuromorphic hardware. PMID:25657618

  16. Spike Timing-Dependent Plasticity in the Long-Latency Stretch Reflex Following Paired Stimulation from a Wearable Electronic Device

    PubMed Central

    Foysal, K. M. Riashad; de Carvalho, Felipe

    2016-01-01

    The long-latency stretch reflex (LLSR) in human elbow muscles probably depends on multiple pathways; one possible contributor is the reticulospinal tract. Here we attempted to induce plastic changes in the LLSR by pairing noninvasive stimuli that are known to activate reticulospinal pathways, at timings predicted to cause spike timing-dependent plasticity in the brainstem. In healthy human subjects, reflex responses in flexor muscles were recorded following extension perturbations at the elbow. Subjects were then fitted with a portable device that delivered auditory click stimuli through an earpiece, and electrical stimuli around motor threshold to the biceps muscle via surface electrodes. We tested the following four paradigms: biceps stimulus 10 ms before click (Bi-10ms-C); click 25 ms before biceps (C-25ms-Bi); click alone (C only); and biceps alone (Bi only). The average stimulus rate was 0.67 Hz. Subjects left the laboratory wearing the device and performed normal daily activities. Approximately 7 h later, they returned, and stretch reflexes were remeasured. The LLSR was significantly enhanced in the biceps muscle (on average by 49%) after the Bi-10ms-C paradigm, but was suppressed for C-25ms-Bi (by 31%); it was unchanged for Bi only and C only. No paradigm induced LLSR changes in the unstimulated brachioradialis muscle. Although we cannot exclude contributions from spinal or cortical pathways, our results are consistent with spike timing-dependent plasticity in reticulospinal circuits, specific to the stimulated muscle. This is the first demonstration that the LLSR can be modified via paired-pulse methods, and may open up new possibilities in motor systems neuroscience and rehabilitation. SIGNIFICANCE STATEMENT This report is the first demonstration that the long-latency stretch reflex can be modified by repeated, precisely timed pairing of stimuli known to activate brainstem pathways. Furthermore, pairing was achieved with a portable electronic device

  17. Existence and stability criteria for phase-locked modes in ring neural networks based on the spike time resetting curve method.

    PubMed

    Oprisan, Sorinel Adrian

    2010-01-21

    We developed a systematic and consistent mathematical approach to predicting 1:1 phase-locked modes in ring neural networks of spiking neurons based on the open loop spike time resetting curve (STRC) and its almost equivalent counterpart-the phase resetting curve (PRC). The open loop STRCs/PRCs were obtained by injecting into an isolated model neuron a triangular shaped time-dependent stimulus current closely resembling an actual synaptic input. Among other advantages, the STRC eliminates the confusion regarding the undefined phase for stimuli driving the neuron outside of the unperturbed limit cycle. We derived both open loop PRC and STRC-based existence and stability criteria for 1:1 phase-locked modes developed in ring networks of spiking neurons. Our predictions were in good agreement with the closed loop numerical simulations. Intuitive graphical methods for predicting phase-locked modes were also developed both for half-centers and for larger ring networks.

  18. Spike-triggered reaction-time EEG as a possible assessment tool for driving ability.

    PubMed

    Krestel, Heinz E; Nirkko, Arto; von Allmen, Andreas; Liechti, Christian; Wettstein, Janine; Mosbacher, Antoinette; Mathis, Johannes

    2011-10-01

    The impact of interictal epileptic activity (IEA) on driving is a rarely investigated issue. We analyzed the impact of IEA on reaction time in a pilot study. Reactions to simple visual stimuli (light flash) in the Flash test or complex visual stimuli (obstacle on a road) in a modified car driving computer game, the Steer Clear, were measured during IEA bursts and unremarkable electroencephalography (EEG) periods. Individual epilepsy patients showed slower reaction times (RTs) during generalized IEA compared to RTs during unremarkable EEG periods. RT differences were approximately 300 ms (p < 0.001) in the Flash test and approximately 200 ms (p < 0.001) in the Steer Clear. Prior work suggested that RT differences >100 ms may become clinically relevant. This occurred in 40% of patients in the Flash test and in up to 50% in the Steer Clear. When RT were pooled, mean RT differences were 157 ms in the Flash test (p < 0.0001) and 116 ms in the Steer Clear (p < 0.0001). Generalized IEA of short duration seems to impair brain function, that is, the ability to react. The reaction-time EEG could be used routinely to assess driving ability.

  19. Efficient population coding of naturalistic whisker motion in the ventro-posterior medial thalamus based on precise spike timing

    PubMed Central

    Bale, Michael R.; Ince, Robin A. A.; Santagata, Greta; Petersen, Rasmus S.

    2015-01-01

    The rodent whisker-associated thalamic nucleus (VPM) contains a somatotopic map where whisker representation is divided into distinct neuronal sub-populations, called “barreloids”. Each barreloid projects to its associated cortical barrel column and so forms a gateway for incoming sensory stimuli to the barrel cortex. We aimed to determine how the population of neurons within one barreloid encodes naturalistic whisker motion. In rats, we recorded the extracellular activity of up to nine single neurons within a single barreloid, by implanting silicon probes parallel to the longitudinal axis of the barreloids. We found that play-back of texture-induced whisker motion evoked sparse responses, timed with millisecond precision. At the population level, there was synchronous activity: however, different subsets of neurons were synchronously active at different times. Mutual information between population responses and whisker motion increased near linearly with population size. When normalized to factor out firing rate differences, we found that texture was encoded with greater informational-efficiency than white noise. These results indicate that, within each VPM barreloid, there is a rich and efficient population code for naturalistic whisker motion based on precisely timed, population spike patterns. PMID:26441549

  20. Spike-Timing Precision and Neuronal Synchrony Are Enhanced by an Interaction between Synaptic Inhibition and Membrane Oscillations in the Amygdala

    PubMed Central

    Daftary, Shabrine; Madsen, Teresa E.; Rainnie, Donald G.

    2012-01-01

    The basolateral complex of the amygdala (BLA) is a critical component of the neural circuit regulating fear learning. During fear learning and recall, the amygdala and other brain regions, including the hippocampus and prefrontal cortex, exhibit phase-locked oscillations in the high delta/low theta frequency band (∼2–6 Hz) that have been shown to contribute to the learning process. Network oscillations are commonly generated by inhibitory synaptic input that coordinates action potentials in groups of neurons. In the rat BLA, principal neurons spontaneously receive synchronized, inhibitory input in the form of compound, rhythmic, inhibitory postsynaptic potentials (IPSPs), likely originating from burst-firing parvalbumin interneurons. Here we investigated the role of compound IPSPs in the rat and rhesus macaque BLA in regulating action potential synchrony and spike-timing precision. Furthermore, because principal neurons exhibit intrinsic oscillatory properties and resonance between 4 and 5 Hz, in the same frequency band observed during fear, we investigated whether compound IPSPs and intrinsic oscillations interact to promote rhythmic activity in the BLA at this frequency. Using whole-cell patch clamp in brain slices, we demonstrate that compound IPSPs, which occur spontaneously and are synchronized across principal neurons in both the rat and primate BLA, significantly improve spike-timing precision in BLA principal neurons for a window of ∼300 ms following each IPSP. We also show that compound IPSPs coordinate the firing of pairs of BLA principal neurons, and significantly improve spike synchrony for a window of ∼130 ms. Compound IPSPs enhance a 5 Hz calcium-dependent membrane potential oscillation (MPO) in these neurons, likely contributing to the improvement in spike-timing precision and synchronization of spiking. Activation of the cAMP-PKA signaling cascade enhanced the MPO, and inhibition of this cascade blocked the MPO. We discuss these results in

  1. Defined types of cortical interneurone structure space and spike timing in the hippocampus

    PubMed Central

    Somogyi, Peter; Klausberger, Thomas

    2005-01-01

    The cerebral cortex encodes, stores and combines information about the internal and external environment in rhythmic activity of multiple frequency ranges. Neurones of the cortex can be defined, recognized and compared on the comprehensive application of the following measures: (i) brain area- and cell domain-specific distribution of input and output synapses, (ii) expression of molecules involved in cell signalling, (iii) membrane and synaptic properties reflecting the expression of membrane proteins, (iv) temporal structure of firing in vivo, resulting from (i)–(iii). Spatial and temporal measures of neurones in the network reflect an indivisible unity of evolutionary design, i.e. neurones do not have separate structure or function. The blueprint of this design is most easily accessible in the CA1 area of the hippocampus, where a relatively uniform population of pyramidal cells and their inputs follow an instantly recognizable laminated pattern and act within stereotyped network activity patterns. Reviewing the cell types and their spatio-temporal interactions, we suggest that CA1 pyramidal cells are supported by at least 16 distinct types of GABAergic neurone. During a given behaviour-contingent network oscillation, interneurones of a given type exhibit similar firing patterns. During different network oscillations representing two distinct brain states, interneurones of the same class show different firing patterns modulating their postsynaptic target-domain in a brain-state-dependent manner. These results suggest roles for specific interneurone types in structuring the activity of pyramidal cells via their respective target domains, and accurately timing and synchronizing pyramidal cell discharge, rather than providing generalized inhibition. Finally, interneurones belonging to different classes may fire preferentially at distinct time points during a given oscillation. As different interneurones innervate distinct domains of the pyramidal cells, the

  2. Self-Organized Near-Zero-Lag Synchronization Induced by Spike-Timing Dependent Plasticity in Cortical Populations

    PubMed Central

    Matias, Fernanda S.; Carelli, Pedro V.; Mirasso, Claudio R.; Copelli, Mauro

    2015-01-01

    Several cognitive tasks related to learning and memory exhibit synchronization of macroscopic cortical areas together with synaptic plasticity at neuronal level. Therefore, there is a growing effort among computational neuroscientists to understand the underlying mechanisms relating synchrony and plasticity in the brain. Here we numerically study the interplay between spike-timing dependent plasticity (STDP) and anticipated synchronization (AS). AS emerges when a dominant flux of information from one area to another is accompanied by a negative time lag (or phase). This means that the receiver region pulses before the sender does. In this paper we study the interplay between different synchronization regimes and STDP at the level of three-neuron microcircuits as well as cortical populations. We show that STDP can promote auto-organized zero-lag synchronization in unidirectionally coupled neuronal populations. We also find synchronization regimes with negative phase difference (AS) that are stable against plasticity. Finally, we show that the interplay between negative phase difference and STDP provides limited synaptic weight distribution without the need of imposing artificial boundaries. PMID:26474165

  3. Self-Organized Near-Zero-Lag Synchronization Induced by Spike-Timing Dependent Plasticity in Cortical Populations.

    PubMed

    Matias, Fernanda S; Carelli, Pedro V; Mirasso, Claudio R; Copelli, Mauro

    2015-01-01

    Several cognitive tasks related to learning and memory exhibit synchronization of macroscopic cortical areas together with synaptic plasticity at neuronal level. Therefore, there is a growing effort among computational neuroscientists to understand the underlying mechanisms relating synchrony and plasticity in the brain. Here we numerically study the interplay between spike-timing dependent plasticity (STDP) and anticipated synchronization (AS). AS emerges when a dominant flux of information from one area to another is accompanied by a negative time lag (or phase). This means that the receiver region pulses before the sender does. In this paper we study the interplay between different synchronization regimes and STDP at the level of three-neuron microcircuits as well as cortical populations. We show that STDP can promote auto-organized zero-lag synchronization in unidirectionally coupled neuronal populations. We also find synchronization regimes with negative phase difference (AS) that are stable against plasticity. Finally, we show that the interplay between negative phase difference and STDP provides limited synaptic weight distribution without the need of imposing artificial boundaries.

  4. Neuregulin-Dependent Regulation of Fast-Spiking Interneuron Excitability Controls the Timing of the Critical Period.

    PubMed

    Gu, Yu; Tran, Trinh; Murase, Sachiko; Borrell, Andrew; Kirkwood, Alfredo; Quinlan, Elizabeth M

    2016-10-05

    Maturation of excitatory drive onto fast-spiking interneurons (FS INs) in the visual cortex has been implicated in the control of the timing of the critical period for ocular dominance plasticity. However, the mechanisms that regulate the strength of these synapses over cortical development are not understood. Here we use a mouse model to show that neuregulin (NRG) and the receptor tyrosine kinase erbB4 regulate the timing of the critical period. NRG1 enhanced the strength of excitatory synapses onto FS INs, which inhibited ocular dominance plasticity during the critical period but rescued plasticity in transgenics with hypoexcitable FS INs. Blocking the effects of endogenous neuregulin via inhibition of erbBs rescued ocular dominance plasticity in postcritical period adults, allowing recovery from amblyopia induced by chronic monocular deprivation. Thus, the strength of excitation onto FS INs is a key determinant of critical period plasticity and is maintained at high levels by NRG-erbB4 signaling to constrain plasticity in adulthood.

  5. Wavelet transform of neural spike trains

    NASA Astrophysics Data System (ADS)

    Kim, Youngtae; Jung, Min Whan; Kim, Yunbok

    2000-02-01

    Wavelet transform of neural spike trains recorded with a tetrode in the rat primary somatosensory cortex is described. Continuous wavelet transform (CWT) of the spike train clearly shows singularities hidden in the noisy or chaotic spike trains. A multiresolution analysis of the spike train is also carried out using discrete wavelet transform (DWT) for denoising and approximating at different time scales. Results suggest that this multiscale shape analysis can be a useful tool for classifying the spike trains.

  6. Spike synchronization and firing rate in a population of motor cortical neurons in relation to movement direction and reaction time.

    PubMed

    Grammont, F; Riehle, A

    2003-05-01

    We studied the dynamics of precise spike synchronization and rate modulation in a population of neurons recorded in monkey motor cortex during performance of a delayed multidirectional pointing task and determined their relation to behavior. We showed that at the population level neurons coherently synchronized their activity at various moments during the trial in relation to relevant task events. The comparison of the time course of the modulation of synchronous activity with that of the firing rate of the same neurons revealed a considerable difference. Indeed, when synchronous activity was highest, at the end of the preparatory period, firing rate was low, and, conversely, when the firing rate was highest, at movement onset, synchronous activity was almost absent. There was a clear tendency for synchrony to precede firing rate, suggesting that the coherent activation of cell assemblies may trigger the increase in firing rate in large groups of neurons, although it appeared that there was no simple parallel shifting in time of these two activity measures. Interestingly, there was a systematic relationship between the amount of significant synchronous activity within the population of neurons and movement direction at the end of the preparatory period. Furthermore, about 400 ms later, at movement onset, the mean firing rate of the same population was also significantly tuned to movement direction, having roughly the same preferred direction as synchronous activity. Finally, reaction time measurements revealed a directional preference of the monkey with, once again, the same preferred direction as synchronous activity and firing rate. These results lead us to speculate that synchronous activity and firing rate are cooperative neuronal processes and that the directional matching of our three measures--firing rate, synchronicity, and reaction times--might be an effect of behaviorally induced network cooperativity acquired during learning.

  7. Role of AMPA and NMDA receptors and back-propagating action potentials in spike timing-dependent plasticity.

    PubMed

    Fuenzalida, Marco; Fernández de Sevilla, David; Couve, Alejandro; Buño, Washington

    2010-01-01

    The cellular mechanisms that mediate spike timing-dependent plasticity (STDP) are largely unknown. We studied in vitro in CA1 pyramidal neurons the contribution of AMPA and N-methyl-d-aspartate (NMDA) components of Schaffer collateral (SC) excitatory postsynaptic potentials (EPSPs; EPSP(AMPA) and EPSP(NMDA)) and of the back-propagating action potential (BAP) to the long-term potentiation (LTP) induced by a STDP protocol that consisted in pairing an EPSP and a BAP. Transient blockade of EPSP(AMPA) with 7-nitro-2,3-dioxo-1,4-dihydroquinoxaline-6-carbonitrile (CNQX) during the STDP protocol prevented LTP. Contrastingly LTP was induced under transient inhibition of EPSP(AMPA) by combining SC stimulation, an imposed EPSP(AMPA)-like depolarization, and BAP or by coupling the EPSP(NMDA) evoked under sustained depolarization (approximately -40 mV) and BAP. In Mg(2+)-free solution EPSP(NMDA) and BAP also produced LTP. Suppression of EPSP(NMDA) or BAP always prevented LTP. Thus activation of NMDA receptors and BAPs are needed but not sufficient because AMPA receptor activation is also obligatory for STDP. However, a transient depolarization of another origin that unblocks NMDA receptors and a BAP may also trigger LTP.

  8. Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks

    SciTech Connect

    Yu, Haitao; Guo, Xinmeng; Wang, Jiang Deng, Bin; Wei, Xile

    2014-09-01

    The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient for the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.

  9. Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks.

    PubMed

    Garrido, Jesús A; Luque, Niceto R; Tolu, Silvia; D'Angelo, Egidio

    2016-08-01

    The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly understood. Here, we have used a simple model of afferent excitatory neurons and interneurons with lateral inhibition, reproducing a network topology found in many brain areas from the cerebellum to cortical columns. When endowed with spike-timing dependent plasticity (STDP) at the excitatory input synapses and at the inhibitory interneuron-interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity effectively distributed multiple patterns among available interneurons, thus allowing the simultaneous detection of multiple overlapping patterns. The addition of plasticity in intrinsic excitability made the system more robust allowing self-adjustment and rescaling in response to a broad range of input patterns. The combination of plasticity in lateral inhibitory connections and homeostatic mechanisms in the inhibitory interneurons optimized mutual information (MI) transfer. The storage of multiple complex patterns in plastic interneuron networks could be critical for the generation of sparse representations of information in excitatory neuron populations falling under their control.

  10. Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks.

    PubMed

    Yu, Haitao; Guo, Xinmeng; Wang, Jiang; Deng, Bin; Wei, Xile

    2014-09-01

    The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient for the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.

  11. Rayleigh--Taylor spike evaporation

    SciTech Connect

    Schappert, G. T.; Batha, S. H.; Klare, K. A.; Hollowell, D. E.; Mason, R. J.

    2001-09-01

    Laser-based experiments have shown that Rayleigh--Taylor (RT) growth in thin, perturbed copper foils leads to a phase dominated by narrow spikes between thin bubbles. These experiments were well modeled and diagnosed until this '' spike'' phase, but not into this spike phase. Experiments were designed, modeled, and performed on the OMEGA laser [T. R. Boehly, D. L. Brown, R. S. Craxton , Opt. Commun. 133, 495 (1997)] to study the late-time spike phase. To simulate the conditions and evolution of late time RT, a copper target was fabricated consisting of a series of thin ridges (spikes in cross section) 150 {mu}m apart on a thin flat copper backing. The target was placed on the side of a scale-1.2 hohlraum with the ridges pointing into the hohlraum, which was heated to 190 eV. Side-on radiography imaged the evolution of the ridges and flat copper backing into the typical RT bubble and spike structure including the '' mushroom-like feet'' on the tips of the spikes. RAGE computer models [R. M. Baltrusaitis, M. L. Gittings, R. P. Weaver, R. F. Benjamin, and J. M. Budzinski, Phys. Fluids 8, 2471 (1996)] show the formation of the '' mushrooms,'' as well as how the backing material converges to lengthen the spike. The computer predictions of evolving spike and bubble lengths match measurements fairly well for the thicker backing targets but not for the thinner backings.

  12. 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.

  13. Reducing employee travelling time through smart commuting

    NASA Astrophysics Data System (ADS)

    Rahman, A. N. N. A.; Yusoff, Z. M.; Aziz, I. S.; Omar, D.

    2014-02-01

    Extremely congested roads will definitely delay the arrival time of each trip.This certainly impacted the journey of employees. Tardiness at the workplace has become a perturbing issue for companies where traffic jams are the most common worker excuses. A depressing consequence on daily life and productivity of the employee occurs. The issues of commuting distance between workplace and resident area become the core point of this research. This research will emphasize the use of Geographical Information System (GIS) technique to explore the distance parameter to the employment area and will focus on the accessibility pattern of low-cost housing. The research methodology consists of interview sessions and a questionnaire to residents of low-cost housing areas in Melaka Tengah District in Malaysia. The combination of these processes will show the criteria from the selected parameter for each respondent from their resident area to the employment area. This will further help in the recommendation of several options for a better commute or improvement to the existing routes and public transportations system. Thus enhancing quality of life for employees and helping to reduce stress, decrease lateness, absenteeism and improving productivity in workplace.

  14. Phasic boosting of medial perforant path-evoked granule cell output time-locked to spontaneous dentate EEG spikes in awake rats.

    PubMed

    Bramham, C R

    1998-06-01

    Dentate spikes (DSs) are positive-going field potential transients that occur intermittently in the hilar region of the dentate gyrus during alert wakefulness and slow-wave sleep. The function of dentate spikes is unknown; they have been suggested to be triggered by perforant path input and are associated with firing of hilar interneurons and inhibition of CA3 pyramidal cells. Here we investigated the effect of DSs on medial perforant path (MPP)-granule cell-evoked transmission in freely moving rats. The MPP was stimulated selectively in the angular bundle while evoked field potentials and the EEG were recorded with a vertical multielectrode array in the dentate gyrus. DSs were identified readily on the basis of their characteristic voltage-versus-depth profile, amplitude, duration, and state dependency. Using on-line detection of the DS peak, the timing of MPP stimulation relative to single DSs was controlled. DS-triggered evoked responses were compared with conventional, manually evoked responses in still-alert wakefulness (awake immobility) and, in some cases, slow-wave sleep. Input-output curves were obtained with stimulation on the positive DS peak (0 delay) and at delays of 50, 100, and 500 ms. Stimulation on the peak DS was associated with a significant increase in the population spike amplitude, a reduction in population spike latency, and a decrease in the field excitatory postsynaptic potential (fEPSP) slope, relative to manual stimulation. Granule cell excitability was enhanced markedly during DSs, as indicated by a mean 93% increase in the population spike amplitude and a leftward shift in the fEPSP-spike relation. Maximum effects occurred at the DS peak, and lasted between 50 and 100 ms. Paired-pulse inhibition of the population spike was unaffected, indicating intact recurrent inhibition during DSs. The results demonstrate enhancement of perforant path-evoked granule cell output time-locked to DSs. DSs therefore may function to intermittently boost

  15. Predictability of EEG interictal spikes.

    PubMed Central

    Scott, D A; Schiff, S J

    1995-01-01

    To determine whether EEG spikes are predictable, time series of EEG spike intervals were generated from subdural and depth electrode recordings from four patients. The intervals between EEG spikes were hand edited to ensure high accuracy and eliminate false positive and negative spikes. Spike rates (per minute) were generated from longer time series, but for these data hand editing was usually not feasible. Linear and nonlinear models were fit to both types of data. One patient had no linear or nonlinear predictability, two had predictability that could be well accounted for with a linear stochastic model, and one had a degree of nonlinear predictability for both interval and rate data that no linear model could adequately account for. PMID:8580318

  16. The homeodomain transcription factor TaHDZipI-2 from wheat regulates frost tolerance, flowering time and spike development in transgenic barley.

    PubMed

    Kovalchuk, Nataliya; Chew, William; Sornaraj, Pradeep; Borisjuk, Nikolai; Yang, Nannan; Singh, Rohan; Bazanova, Natalia; Shavrukov, Yuri; Guendel, Andre; Munz, Eberhard; Borisjuk, Ljudmilla; Langridge, Peter; Hrmova, Maria; Lopato, Sergiy

    2016-07-01

    Homeodomain leucine zipper class I (HD-Zip I) transcription factors (TFs) play key roles in the regulation of plant growth and development under stresses. Functions of the TaHDZipI-2 gene isolated from the endosperm of developing wheat grain were revealed. Molecular characterization of TaHDZipI-2 protein included studies of its dimerisation, protein-DNA interactions and gene activation properties using pull-down assays, in-yeast methods and transient expression assays in wheat cells. The analysis of TaHDZipI-2 gene functions was performed using transgenic barley plants. It included comparison of developmental phenotypes, yield components, grain quality, frost tolerance and the levels of expression of potential target genes in transgenic and control plants. Transgenic TaHDZipI-2 lines showed characteristic phenotypic features that included reduced growth rates, reduced biomass, early flowering, light-coloured leaves and narrowly elongated spikes. Transgenic lines produced 25-40% more seeds per spike than control plants, but with 50-60% smaller grain size. In vivo lipid imaging exposed changes in the distribution of lipids between the embryo and endosperm in transgenic seeds. Transgenic lines were significantly more tolerant to frost than control plants. Our data suggest the role of TaHDZipI-2 in controlling several key processes underlying frost tolerance, transition to flowering and spike development.

  17. Dopamine Modulates Spike Timing-Dependent Plasticity and Action Potential Properties in CA1 Pyramidal Neurons of Acute Rat Hippocampal Slices

    PubMed Central

    Edelmann, Elke; Lessmann, Volkmar

    2011-01-01

    Spike timing-dependent plasticity (STDP) is a cellular model of Hebbian synaptic plasticity which is believed to underlie memory formation. In an attempt to establish a STDP paradigm in CA1 of acute hippocampal slices from juvenile rats (P15–20), we found that changes in excitability resulting from different slice preparation protocols correlate with the success of STDP induction. Slice preparation with sucrose containing ACSF prolonged rise time, reduced frequency adaptation, and decreased latency of action potentials in CA1 pyramidal neurons compared to preparation in conventional ASCF, while other basal electrophysiological parameters remained unaffected. Whereas we observed prominent timing-dependent long-term potentiation (t-LTP) to 171 ± 10% of controls in conventional ACSF, STDP was absent in sucrose prepared slices. This sucrose-induced STDP deficit could not be rescued by stronger STDP paradigms, applying either more pre- and/or postsynaptic stimuli, or by a higher stimulation frequency. Importantly, slice preparation with sucrose containing ACSF did not eliminate theta-burst stimulation induced LTP in CA1 in field potential recordings in our rat hippocampal slices. Application of dopamine (for 10–20 min) to sucrose prepared slices completely rescued t-LTP and recovered action potential properties back to levels observed in ACSF prepared slices. Conversely, acute inhibition of D1 receptor signaling impaired t-LTP in ACSF prepared slices. No similar restoring effect for STDP as seen with dopamine was observed in response to the β-adrenergic agonist isoproterenol. ELISA measurements demonstrated a significant reduction of endogenous dopamine levels (to 61.9 ± 6.9% of ACSF values) in sucrose prepared slices. These results suggest that dopamine signaling is involved in regulating the efficiency to elicit STDP in CA1 pyramidal neurons. PMID:22065958

  18. Balanced synaptic input shapes the correlation between neural spike trains.

    PubMed

    Litwin-Kumar, Ashok; Oswald, Anne-Marie M; Urban, Nathaniel N; Doiron, Brent

    2011-12-01

    Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states.

  19. Three Policies to Reduce Time to Degree

    ERIC Educational Resources Information Center

    Johnson, Nate

    2011-01-01

    The old saying that time is money is nowhere more true than in U.S. higher education. Time is measured two ways in academia--by the calendar and by the credit hour. Both can be costly, whether in the form of tuition, taxpayer subsidies, or the wages students lose with each additional term enrolled. For many years, in credit terms, the standard for…

  20. Static micro-array isolation, dynamic time series classification, capture and enumeration of spiked breast cancer cells in blood: the nanotube-CTC chip

    NASA Astrophysics Data System (ADS)

    Khosravi, Farhad; Trainor, Patrick J.; Lambert, Christopher; Kloecker, Goetz; Wickstrom, Eric; Rai, Shesh N.; Panchapakesan, Balaji

    2016-11-01

    We demonstrate the rapid and label-free capture of breast cancer cells spiked in blood using nanotube-antibody micro-arrays. 76-element single wall carbon nanotube arrays were manufactured using photo-lithography, metal deposition, and etching techniques. Anti-epithelial cell adhesion molecule (anti-EpCAM), Anti-human epithelial growth factor receptor 2 (anti-Her2) and non-specific IgG antibodies were functionalized to the surface of the nanotube devices using 1-pyrene-butanoic acid succinimidyl ester. Following device functionalization, blood spiked with SKBR3, MCF7 and MCF10A cells (100/1000 cells per 5 μl per device, 170 elements totaling 0.85 ml of whole blood) were adsorbed on to the nanotube device arrays. Electrical signatures were recorded from each device to screen the samples for differences in interaction (specific or non-specific) between samples and devices. A zone classification scheme enabled the classification of all 170 elements in a single map. A kernel-based statistical classifier for the ‘liquid biopsy’ was developed to create a predictive model based on dynamic time warping series to classify device electrical signals that corresponded to plain blood (control) or SKBR3 spiked blood (case) on anti-Her2 functionalized devices with ˜90% sensitivity, and 90% specificity in capture of 1000 SKBR3 breast cancer cells in blood using anti-Her2 functionalized devices. Screened devices that gave positive electrical signatures were confirmed using optical/confocal microscopy to hold spiked cancer cells. Confocal microscopic analysis of devices that were classified to hold spiked blood based on their electrical signatures confirmed the presence of cancer cells through staining for DAPI (nuclei), cytokeratin (cancer cells) and CD45 (hematologic cells) with single cell sensitivity. We report 55%-100% cancer cell capture yield depending on the active device area for blood adsorption with mean of 62% (˜12 500 captured off 20 000 spiked cells in 0.1 ml

  1. Reducing waiting time at security checkpoints

    SciTech Connect

    Landauer, E.G.; Becker, L.C.

    1988-08-01

    The authors were asked to improve the time it took a queue of cars to go through a security gate every morning and afternoon. Each individual passing through the gate was required to show a security badge to a guard. The goal was to improve processing time and to eliminate a safety problem at the gate without increasing the required resources. This problem required quick resolution. The project was initiated using queuing theory. After collecting data, the authors determined that a simulation model would be more appropriate. The study demonstrated that a significant improvement could be seen when the same number of guards worked two parallel traffic lines leading to the single gate, in place of the security guards working in a serial process from a single line of traffic. 7 tabs., 4 figs.

  2. Spike history neural response model.

    PubMed

    Kameneva, Tatiana; Abramian, Miganoosh; Zarelli, Daniele; Nĕsić, Dragan; Burkitt, Anthony N; Meffin, Hamish; Grayden, David B

    2015-06-01

    There is a potential for improved efficacy of neural stimulation if stimulation levels can be modified dynamically based on the responses of neural tissue in real time. A neural model is developed that describes the response of neurons to electrical stimulation and that is suitable for feedback control neuroprosthetic stimulation. Experimental data from NZ white rabbit retinae is used with a data-driven technique to model neural dynamics. The linear-nonlinear approach is adapted to incorporate spike history and to predict the neural response of ganglion cells to electrical stimulation. To validate the fitness of the model, the penalty term is calculated based on the time difference between each simulated spike and the closest spike in time in the experimentally recorded train. The proposed model is able to robustly predict experimentally observed spike trains.

  3. Neural cache: a low-power online digital spike-sorting architecture.

    PubMed

    Peng, Chung-Ching; Sabharwal, Pawan; Bashirullah, Rizwan

    2008-01-01

    Transmitting large amounts of data sensed by multi-electrode array devices is widely considered to be a challenging problem in designing implantable neural recording systems. Spike sorting is an important step to reducing the data bandwidth before wireless data transmission. The feasibility of spike sorting algorithms in scaled CMOS technologies, which typically operate on low frequency neural spikes, is determined largely by its power consumption, a dominant portion of which is leakage power. Our goal is to explore energy saving architectures for online spike sorting without sacrificing performance. In the face of non-stationary neural data, training is not always a feasible option. We present a low-power architecture for real-time online spike sorting that does not require any training period and has the capability to quickly respond to the changing spike shapes.

  4. Automatic Spike Removal Algorithm for Raman Spectra.

    PubMed

    Tian, Yao; Burch, Kenneth S

    2016-11-01

    Raman spectroscopy is a powerful technique, widely used in both academia and industry. In part, the technique's extensive use stems from its ability to uniquely identify and image various material parameters: composition, strain, temperature, lattice/excitation symmetry, and magnetism in bulk, nano, solid, and organic materials. However, in nanomaterials and samples with low thermal conductivity, these measurements require long acquisition times. On the other hand, charge-coupled device (CCD) detectors used in Raman microscopes are vulnerable to cosmic rays. As a result, many spurious spikes occur in the measured spectra, which can distort the result or require the spectra to be ignored. In this paper, we outline a new method that significantly improves upon existing algorithms for removing these spikes. Specifically, we employ wavelet transform and data clustering in a new spike-removal algorithm. This algorithm results in spike-free spectra with negligible spectral distortion. The reduced dependence on the selection of wavelets and intuitive wavelet coefficient adjustment strategy enables non-experts to employ these powerful spectra-filtering techniques.

  5. Spike and Neuropeptide-Dependent Mechanisms Control GnRH Neuron Nerve Terminal Ca(2+) over Diverse Time Scales.

    PubMed

    Iremonger, Karl J; Porteous, Robert; Herbison, Allan E

    2017-03-22

    Fast cell-to-cell communication in the brain is achieved by action potential-dependent synaptic release of neurotransmitters. The fast kinetics of transmitter release are determined by transient Ca(2+) elevations in presynaptic nerve terminals. Neuromodulators have previously been shown to regulate transmitter release by inhibiting presynaptic Ca(2+) influx. Few studies to date have demonstrated the opposite, that is, neuromodulators directly driving presynaptic Ca(2+) rises and increases in nerve terminal excitability. Here we use GCaMP Ca(2+) imaging in brain slices from mice to address how nerve terminal Ca(2+) is controlled in gonadotropin-releasing hormone (GnRH) neurons via action potentials and neuromodulators. Single spikes and bursts of action potentials evoked fast, voltage-gated Ca(2+) channel-dependent Ca(2+) elevations. In contrast, brief exposure to the neuropeptide kisspeptin-evoked long-lasting Ca(2+) plateaus that persisted for tens of minutes. Neuropeptide-mediated Ca(2+) elevations were independent of action potentials, requiring Ca(2+) entry via voltage-gated Ca(2+) channels and transient receptor potential channels in addition to release from intracellular store mechanisms. Together, these data reveal that neuromodulators can exert powerful and long-lasting regulation of nerve terminal Ca(2+) independently from actions at the soma. Thus, GnRH nerve terminal function is controlled over disparate timescales via both classical spike-dependent and nonclassical neuropeptide-dependent mechanisms.SIGNIFICANCE STATEMENT Nerve terminals are highly specialized regions of a neuron where neurotransmitters and neurohormones are released. Many neuroendocrine neurons release neurohormones in long-duration bursts of secretion. To understand how this is achieved, we have performed live Ca(2+) imaging in the nerve terminals of gonadotropin-releasing hormone neurons. We find that bursts of action potentials and local neuropeptide signals are both capable of

  6. A simple algorithm for averaging spike trains.

    PubMed

    Julienne, Hannah; Houghton, Conor

    2013-02-25

    Although spike trains are the principal channel of communication between neurons, a single stimulus will elicit different spike trains from trial to trial. This variability, in both spike timings and spike number can obscure the temporal structure of spike trains and often means that computations need to be run on numerous spike trains in order to extract features common across all the responses to a particular stimulus. This can increase the computational burden and obscure analytical results. As a consequence, it is useful to consider how to calculate a central spike train that summarizes a set of trials. Indeed, averaging responses over trials is routine for other signal types. Here, a simple method for finding a central spike train is described. The spike trains are first mapped to functions, these functions are averaged, and a greedy algorithm is then used to map the average function back to a spike train. The central spike trains are tested for a large data set. Their performance on a classification-based test is considerably better than the performance of the medoid spike trains.

  7. Desynchronization of Fast-Spiking Interneurons Reduces β-Band Oscillations and Imbalance in Firing in the Dopamine-Depleted Striatum

    PubMed Central

    Damodaran, Sriraman; Cressman, John R.; Jedrzejewski-Szmek, Zbigniew

    2015-01-01

    Oscillations in the β-band (8–30 Hz) that emerge in the output nuclei of the basal ganglia during Parkinson's disease, along with an imbalanced activation of the direct and indirect pathways, have been linked to the hypokinetic motor output associated with the disease. Although dopamine depletion causes a change in cellular and network properties in the striatum, it is unclear whether abnormal activity measured in the globus pallidus and substantia nigra pars reticulata is caused by abnormal striatal activity. Here we use a computational network model of medium spiny neurons (MSNs)—fast-spiking interneurons (FSIs), based on data from several mammalian species, and find that robust β-band oscillations and imbalanced firing emerge from implementation of changes to cellular and circuit properties caused by dopamine depletion. These changes include a reduction in connections between MSNs, a doubling of FSI inhibition to D2 MSNs, an increase in D2 MSN dendritic excitability, and a reduction in D2 MSN somatic excitability. The model reveals that the reduced decorrelation between MSNs attributable to weakened lateral inhibition enables the strong influence of synchronous FSIs on MSN firing and oscillations. Weakened lateral inhibition also produces an increased sensitivity of MSN output to cortical correlation, a condition relevant to the parkinsonian striatum. The oscillations of FSIs, in turn, are strongly modulated by fast electrical transmission between FSIs through gap junctions. These results suggest that pharmaceuticals that desynchronize FSI activity may provide a novel treatment for the enhanced β-band oscillations, imbalanced firing, and motor dysfunction in Parkinson's disease. PMID:25609629

  8. The dynamic relationship between cerebellar Purkinje cell simple spikes and the spikelet number of complex spikes

    PubMed Central

    Burroughs, Amelia; Wise, Andrew K.; Xiao, Jianqiang; Houghton, Conor; Tang, Tianyu; Suh, Colleen Y.; Lang, Eric J.

    2016-01-01

    Key points Purkinje cells are the sole output of the cerebellar cortex and fire two distinct types of action potential: simple spikes and complex spikes.Previous studies have mainly considered complex spikes as unitary events, even though the waveform is composed of varying numbers of spikelets.The extent to which differences in spikelet number affect simple spike activity (and vice versa) remains unclear.We found that complex spikes with greater numbers of spikelets are preceded by higher simple spike firing rates but, following the complex spike, simple spikes are reduced in a manner that is graded with spikelet number.This dynamic interaction has important implications for cerebellar information processing, and suggests that complex spike spikelet number may maintain Purkinje cells within their operational range. Abstract Purkinje cells are central to cerebellar function because they form the sole output of the cerebellar cortex. They exhibit two distinct types of action potential: simple spikes and complex spikes. It is widely accepted that interaction between these two types of impulse is central to cerebellar cortical information processing. Previous investigations of the interactions between simple spikes and complex spikes have mainly considered complex spikes as unitary events. However, complex spikes are composed of an initial large spike followed by a number of secondary components, termed spikelets. The number of spikelets within individual complex spikes is highly variable and the extent to which differences in complex spike spikelet number affects simple spike activity (and vice versa) remains poorly understood. In anaesthetized adult rats, we have found that Purkinje cells recorded from the posterior lobe vermis and hemisphere have high simple spike firing frequencies that precede complex spikes with greater numbers of spikelets. This finding was also evident in a small sample of Purkinje cells recorded from the posterior lobe hemisphere in awake

  9. Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system.

    PubMed

    Diamond, A; Schmuker, M; Berna, A Z; Trowell, S; Nowotny, Thomas

    2016-02-18

    In many application domains, conventional e-noses are frequently outperformed in both speed and accuracy by their biological counterparts. Exploring potential bio-inspired improvements, we note a number of neuronal network models have demonstrated some success in classifying static datasets by abstracting the insect olfactory system. However, these designs remain largely unproven in practical settings, where sensor data is real-time, continuous, potentially noisy, lacks a precise onset signal and accurate classification requires the inclusion of temporal aspects into the feature set. This investigation therefore seeks to inform and develop the potential and suitability of biomimetic classifiers for use with typical real-world sensor data. Taking a generic classifier design inspired by the inhibition and competition in the insect antennal lobe, we apply it to identifying 20 individual chemical odours from the timeseries of responses of metal oxide sensors. We show that four out of twelve available sensors and the first 30 s (10%) of the sensors' continuous response are sufficient to deliver 92% accurate classification without access to an odour onset signal. In contrast to previous approaches, once training is complete, sensor signals can be fed continuously into the classifier without requiring discretization. We conclude that for continuous data there may be a conceptual advantage in using spiking networks, in particular where time is an essential component of computation. Classification was achieved in real time using a GPU-accelerated spiking neural network simulator developed in our group.

  10. Spiking neural networks for cortical neuronal spike train decoding.

    PubMed

    Fang, Huijuan; Wang, Yongji; He, Jiping

    2010-04-01

    Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the timing of spikes, are a better alternative to the coding scheme based on spike frequency (histogram) alone. The SNN model analyzes cortical neural spike trains directly without losing temporal information for generating more reliable motor command for cortically controlled prosthetics. In this letter, we compared the temporal pattern classification result from the SNN approach with results generated from firing-rate-based approaches: conventional artificial neural networks, support vector machines, and linear regression. The results show that the SNN algorithm can achieve higher classification accuracy and identify the spiking activity related to movement control earlier than the other methods. Both are desirable characteristics for fast neural information processing and reliable control command pattern recognition for neuroprosthetic applications.

  11. Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units.

    PubMed

    Igarashi, Jun; Shouno, Osamu; Fukai, Tomoki; Tsujino, Hiroshi

    2011-11-01

    Real-time simulation of a biologically realistic spiking neural network is necessary for evaluation of its capacity to interact with real environments. However, the real-time simulation of such a neural network is difficult due to its high computational costs that arise from two factors: (1) vast network size and (2) the complicated dynamics of biologically realistic neurons. In order to address these problems, mainly the latter, we chose to use general purpose computing on graphics processing units (GPGPUs) for simulation of such a neural network, taking advantage of the powerful computational capability of a graphics processing unit (GPU). As a target for real-time simulation, we used a model of the basal ganglia that has been developed according to electrophysiological and anatomical knowledge. The model consists of heterogeneous populations of 370 spiking model neurons, including computationally heavy conductance-based models, connected by 11,002 synapses. Simulation of the model has not yet been performed in real-time using a general computing server. By parallelization of the model on the NVIDIA Geforce GTX 280 GPU in data-parallel and task-parallel fashion, faster-than-real-time simulation was robustly realized with only one-third of the GPU's total computational resources. Furthermore, we used the GPU's full computational resources to perform faster-than-real-time simulation of three instances of the basal ganglia model; these instances consisted of 1100 neurons and 33,006 synapses and were synchronized at each calculation step. Finally, we developed software for simultaneous visualization of faster-than-real-time simulation output. These results suggest the potential power of GPGPU techniques in real-time simulation of realistic neural networks.

  12. Transsynaptic modality codes in the brain: possible involvement of synchronized spike timing, microRNAs, exosomes and epigenetic processes

    PubMed Central

    Smythies, John; Edelstein, Lawrence

    2013-01-01

    This paper surveys two different mechanisms by which a presynaptic cell can modulate the structure and function of the postsynaptic cell. We first present the evidence that this occurs, and then discuss two mechanisms that could bring this about. The first hypothesis relates to the long lasting effects that the spike patterns of presynaptic axons may exert by modulating activity–inducible programs in postsynaptic cells. The second hypothesis is based on recently obtained evidence that, the afferent neuron at the neuromuscular junction buds off exosomes at its synapse and carries a cargo of Wg and Evi, which are large molecular transsynaptic signaling agents (LMTSAs). Further evidence indicates that many types of neurons bud off exosomes containing payloads of various lipids, proteins, and types of RNA. The evidence suggests that they are transmitted across the synapse and are taken up by the postsynaptic structure either by perisynaptic or exosynaptic mechanisms, thus mediating the transfer of information between neurons. To date, the molecular hypothesis has been limited to local interactions within the synapse of concern. In this paper, we explore the possibility that this represents a mechanism for information transfer involving the postsynaptic neuron as a whole. This entails a review of the known functions of these molecules in neuronal physiology, together with an estimate of the possible types of information they could carry and how they might affect neurocomputations. PMID:23316146

  13. Doubling the spectrum of time-domain induced polarization by harmonic de-noising, drift correction, spike removal, tapered gating and data uncertainty estimation

    NASA Astrophysics Data System (ADS)

    Olsson, Per-Ivar; Fiandaca, Gianluca; Larsen, Jakob Juul; Dahlin, Torleif; Auken, Esben

    2016-11-01

    The extraction of spectral information in the inversion process of time-domain (TD) induced polarization (IP) data is changing the use of the TDIP method. Data interpretation is evolving from a qualitative description of the subsurface, able only to discriminate the presence of contrasts in chargeability parameters, towards a quantitative analysis of the investigated media, which allows for detailed soil- and rock-type characterization. Two major limitations restrict the extraction of the spectral information of TDIP data in the field: (i) the difficulty of acquiring reliable early-time measurements in the millisecond range and (ii) the self-potential background drift in the measured potentials distorting the shape of the late-time IP responses, in the second range. Recent developments in TDIP acquisition equipment have given access to full-waveform recordings of measured potentials and transmitted current, opening for a breakthrough in data processing. For measuring at early times, we developed a new method for removing the significant noise from power lines contained in the data through a model-based approach, localizing the fundamental frequency of the power-line signal in the full-waveform IP recordings. By this, we cancel both the fundamental signal and its harmonics. Furthermore, an efficient processing scheme for identifying and removing spikes in TDIP data was developed. The noise cancellation and the de-spiking allow the use of earlier and narrower gates, down to a few milliseconds after the current turn-off. In addition, tapered windows are used in the final gating of IP data, allowing the use of wider and overlapping gates for higher noise suppression with minimal distortion of the signal. For measuring at late times, we have developed an algorithm for removal of the self-potential drift. Usually constant or linear drift-removal algorithms are used, but these algorithms often fail in removing the background potentials present when the electrodes used for

  14. Impact of calcium-activated potassium channels on NMDA spikes in cortical layer 5 pyramidal neurons

    PubMed Central

    Bock, Tobias

    2016-01-01

    Active electrical events play an important role in shaping signal processing in dendrites. As these events are usually associated with an increase in intracellular calcium, they are likely to be under the control of calcium-activated potassium channels. Here, we investigate the impact of calcium-activated potassium channels on N-methyl-d-aspartate (NMDA) receptor-dependent spikes, or NMDA spikes, evoked by glutamate iontophoresis onto basal dendrites of cortical layer 5 pyramidal neurons. We found that small-conductance calcium-activated potassium channels (SK channels) act to reduce NMDA spike amplitude but at the same time, also decrease the iontophoretic current required for their generation. This SK-mediated decrease in NMDA spike threshold was dependent on R-type voltage-gated calcium channels and indicates a counterintuitive, excitatory effect of SK channels on NMDA spike generation, whereas the capacity of SK channels to suppress NMDA spike amplitude is in line with the expected inhibitory action of potassium channels on dendritic excitability. Large-conductance calcium-activated potassium channels had no significant impact on NMDA spikes, indicating that these channels are either absent from basal dendrites or not activated by NMDA spikes. These experiments reveal complex and opposing interactions among NMDA receptors, SK channels, and voltage-gated calcium channels in basal dendrites of cortical layer 5 pyramidal neurons during NMDA spike generation, which are likely to play an important role in regulating the way these neurons integrate the thousands of synaptic inputs they receive. PMID:26936985

  15. 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.

  16. Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

    NASA Astrophysics Data System (ADS)

    Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

    2013-06-01

    Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

  17. Striatal Cholinergic Interneurons Modulate Spike-Timing in Striosomes and Matrix by an Amphetamine-Sensitive Mechanism

    PubMed Central

    Crittenden, Jill R.; Lacey, Carolyn J.; Weng, Feng-Ju; Garrison, Catherine E.; Gibson, Daniel J.; Lin, Yingxi; Graybiel, Ann M.

    2017-01-01

    The striatum is key for action-selection and the motivation to move. Dopamine and acetylcholine release sites are enriched in the striatum and are cross-regulated, possibly to achieve optimal behavior. Drugs of abuse, which promote abnormally high dopamine release, disrupt normal action-selection and drive restricted, repetitive behaviors (stereotypies). Stereotypies occur in a variety of disorders including obsessive-compulsive disorder, autism, schizophrenia and Huntington's disease, as well as in addictive states. The severity of drug-induced stereotypy is correlated with induction of c-Fos expression in striosomes, a striatal compartment that is related to the limbic system and that directly projects to dopamine-producing neurons of the substantia nigra. These characteristics of striosomes contrast with the properties of the extra-striosomal matrix, which has strong sensorimotor and associative circuit inputs and outputs. Disruption of acetylcholine signaling in the striatum blocks the striosome-predominant c-Fos expression pattern induced by drugs of abuse and alters drug-induced stereotypy. The activity of striatal cholinergic interneurons is associated with behaviors related to sensory cues, and cortical inputs to striosomes can bias action-selection in the face of conflicting cues. The neurons and neuropil of striosomes and matrix neurons have observably separate distributions, both at the input level in the striatum and at the output level in the substantia nigra. Notably, cholinergic axons readily cross compartment borders, providing a potential route for local cross-compartment communication to maintain a balance between striosomal and matrix activity. We show here, by slice electrophysiology in transgenic mice, that repetitive evoked firing patterns in striosomal and matrix striatal projection neurons (SPNs) are interrupted by optogenetic activation of cholinergic interneurons either by the addition or the deletion of spikes. We demonstrate that this

  18. Non-fibrillar beta-amyloid abates spike-timing-dependent synaptic potentiation at excitatory synapses in layer 2/3 of the neocortex by targeting postsynaptic AMPA receptors.

    PubMed

    Shemer, Isaac; Holmgren, Carl; Min, Rogier; Fülöp, Livia; Zilberter, Misha; Sousa, Kyle M; Farkas, Tamás; Härtig, Wolfgang; Penke, Botond; Burnashev, Nail; Tanila, Heikki; Zilberter, Yuri; Harkany, Tibor

    2006-04-01

    Cognitive decline in Alzheimer's disease (AD) stems from the progressive dysfunction of synaptic connections within cortical neuronal microcircuits. Recently, soluble amyloid beta protein oligomers (Abeta(ol)s) have been identified as critical triggers for early synaptic disorganization. However, it remains unknown whether a deficit of Hebbian-related synaptic plasticity occurs during the early phase of AD. Therefore, we studied whether age-dependent Abeta accumulation affects the induction of spike-timing-dependent synaptic potentiation at excitatory synapses on neocortical layer 2/3 (L2/3) pyramidal cells in the APPswe/PS1dE9 transgenic mouse model of AD. Synaptic potentiation at excitatory synapses onto L2/3 pyramidal cells was significantly reduced at the onset of Abeta pathology and was virtually absent in mice with advanced Abeta burden. A decreased alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA)/N-methyl-D-aspartate (NMDA) receptor-mediated current ratio implicated postsynaptic mechanisms underlying Abeta synaptotoxicity. The integral role of Abeta(ol)s in these processes was verified by showing that pretreatment of cortical slices with Abeta((25-35)ol)s disrupted spike-timing-dependent synaptic potentiation at unitary connections between L2/3 pyramidal cells, and reduced the amplitude of miniature excitatory postsynaptic currents therein. A robust decrement of AMPA, but not NMDA, receptor-mediated currents in nucleated patches from L2/3 pyramidal cells confirmed that Abeta(ol)s perturb basal glutamatergic synaptic transmission by affecting postsynaptic AMPA receptors. Inhibition of AMPA receptor desensitization by cyclothiazide significantly increased the amplitude of excitatory postsynaptic potentials evoked by afferent stimulation, and rescued synaptic plasticity even in mice with pronounced Abeta pathology. We propose that soluble Abeta(ol)s trigger the diminution of synaptic plasticity in neocortical pyramidal cell networks during early

  19. Reducing Outpatient Waiting Time: A Simulation Modeling Approach

    PubMed Central

    Aeenparast, Afsoon; Tabibi, Seyed Jamaleddin; Shahanaghi, Kamran; Aryanejhad, Mir Bahador

    2013-01-01

    Objectives The objective of this study was to provide a model for reducing outpatient waiting time by using simulation. Materials and Methods A simulation model was constructed by using the data of arrival time, service time and flow of 357 patients referred to orthopedic clinic of a general teaching hospital in Tehran. The simulation model was validated before constructing different scenarios. Results In this study 10 scenarios were presented for reducing outpatient waiting time. Patients waiting time was divided into three levels regarding their physicians. These waiting times for all scenarios were computed by simulation model. According to the final scores the 9th scenario was selected as the best way for reducing outpatient's waiting time. Conclusions Using the simulation as a decision making tool helps us to decide how we can reduce outpatient's waiting time. Comparison of outputs of this scenario and the based- case scenario in simulation model shows that combining physician's work time changing with patient's admission time changing (scenario 9) would reduce patient waiting time about 73.09%. Due to dynamic and complex nature of healthcare systems, the application of simulation for the planning, modeling and analysis of these systems has lagged behind traditional manufacturing practices. Rapid growth in health care system expenditures, technology and competition has increased the complexity of health care systems. Simulation is a useful tool for decision making in complex and probable systems. PMID:24616801

  20. Comparative evaluation of automated and manual commercial DNA extraction methods for detection of Francisella tularensis DNA from suspensions and spiked swabs by real-time polymerase chain reaction.

    PubMed

    Dauphin, Leslie A; Walker, Roblena E; Petersen, Jeannine M; Bowen, Michael D

    2011-07-01

    This study evaluated commercial automated and manual DNA extraction methods for the isolation of Francisella tularensis DNA suitable for real-time polymerase chain reaction (PCR) analysis from cell suspensions and spiked cotton, foam, and polyester swabs. Two automated methods, the MagNA Pure Compact and the QIAcube, were compared to 4 manual methods, the IT 1-2-3 DNA sample purification kit, the MasterPure Complete DNA and RNA purification kit, the QIAamp DNA blood mini kit, and the UltraClean Microbial DNA isolation kit. The methods were compared using 6 F. tularensis strains representing the 2 subspecies which cause the majority of reported cases of tularemia in humans. Cell viability testing of the DNA extracts showed that all 6 extraction methods efficiently inactivated F. tularensis at concentrations of ≤10⁶ CFU/mL. Real-time PCR analysis using a multitarget 5' nuclease assay for F. tularensis revealed that the PCR sensitivity was equivalent using DNA extracted by the 2 automated methods and the manual MasterPure and QIAamp methods. These 4 methods resulted in significantly better levels of detection from bacterial suspensions and performed equivalently for spiked swab samples than the remaining 2. This study identifies optimal DNA extraction methods for processing swab specimens for the subsequent detection of F. tularensis DNA using real-time PCR assays. Furthermore, the results provide diagnostic laboratories with the option to select from 2 automated DNA extraction methods as suitable alternatives to manual methods for the isolation of DNA from F. tularensis.

  1. Sparse and powerful cortical spikes.

    PubMed

    Wolfe, Jason; Houweling, Arthur R; Brecht, Michael

    2010-06-01

    Activity in cortical networks is heterogeneous, sparse and often precisely timed. The functional significance of sparseness and precise spike timing is debated, but our understanding of the developmental and synaptic mechanisms that shape neuronal discharge patterns has improved. Evidence for highly specialized, selective and abstract cortical response properties is accumulating. Singe-cell stimulation experiments demonstrate a high sensitivity of cortical networks to the action potentials of some, but not all, single neurons. It is unclear how this sensitivity of cortical networks to small perturbations comes about and whether it is a generic property of cortex. The unforeseen sensitivity to cortical spikes puts serious constraints on the nature of neural coding schemes.

  2. Multiscale analysis of neural spike trains.

    PubMed

    Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin

    2014-01-30

    This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code.

  3. A Re-Examination of Hebbian-Covariance Rules and Spike Timing-Dependent Plasticity in Cat Visual Cortex in vivo

    PubMed Central

    Frégnac, Yves; Pananceau, Marc; René, Alice; Huguet, Nazyed; Marre, Olivier; Levy, Manuel; Shulz, Daniel E.

    2010-01-01

    Spike timing-dependent plasticity (STDP) is considered as an ubiquitous rule for associative plasticity in cortical networks in vitro. However, limited supporting evidence for its functional role has been provided in vivo. In particular, there are very few studies demonstrating the co-occurrence of synaptic efficiency changes and alteration of sensory responses in adult cortex during Hebbian or STDP protocols. We addressed this issue by reviewing and comparing the functional effects of two types of cellular conditioning in cat visual cortex. The first one, referred to as the “covariance” protocol, obeys a generalized Hebbian framework, by imposing, for different stimuli, supervised positive and negative changes in covariance between postsynaptic and presynaptic activity rates. The second protocol, based on intracellular recordings, replicated in vivo variants of the theta-burst paradigm (TBS), proven successful in inducing long-term potentiation in vitro. Since it was shown to impose a precise correlation delay between the electrically activated thalamic input and the TBS-induced postsynaptic spike, this protocol can be seen as a probe of causal (“pre-before-post”) STDP. By choosing a thalamic region where the visual field representation was in retinotopic overlap with the intracellularly recorded cortical receptive field as the afferent site for supervised electrical stimulation, this protocol allowed to look for possible correlates between STDP and functional reorganization of the conditioned cortical receptive field. The rate-based “covariance protocol” induced significant and large amplitude changes in receptive field properties, in both kitten and adult V1 cortex. The TBS STDP-like protocol produced in the adult significant changes in the synaptic gain of the electrically activated thalamic pathway, but the statistical significance of the functional correlates was detectable mostly at the population level. Comparison of our observations with the

  4. Wavelet-based processing of neuronal spike trains prior to discriminant analysis.

    PubMed

    Laubach, Mark

    2004-04-30

    Investigations of neural coding in many brain systems have focused on the role of spike rate and timing as two means of encoding information within a spike train. Recently, statistical pattern recognition methods, such as linear discriminant analysis (LDA), have emerged as a standard approach for examining neural codes. These methods work well when data sets are over-determined (i.e., there are more observations than predictor variables). But this is not always the case in many experimental data sets. One way to reduce the number of predictor variables is to preprocess data prior to classification. Here, a wavelet-based method is described for preprocessing spike trains. The method is based on the discriminant pursuit (DP) algorithm of Buckheit and Donoho [Proc. SPIE 2569 (1995) 540-51]. DP extracts a reduced set of features that are well localized in the time and frequency domains and that can be subsequently analyzed with statistical classifiers. DP is illustrated using neuronal spike trains recorded in the motor cortex of an awake, behaving rat [Laubach et al. Nature 405 (2000) 567-71]. In addition, simulated spike trains that differed only in the timing of spikes are used to show that DP outperforms another method for preprocessing spike trains, principal component analysis (PCA) [Richmond and Optican J. Neurophysiol. 57 (1987) 147-61].

  5. Independent component analysis in spiking neurons.

    PubMed

    Savin, Cristina; Joshi, Prashant; Triesch, Jochen

    2010-04-22

    Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition.

  6. 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

  7. Enhancing outpatient clinics management software by reducing patients' waiting time.

    PubMed

    Almomani, Iman; AlSarheed, Ahlam

    The Kingdom of Saudi Arabia (KSA) gives great attention to improving the quality of services provided by health care sectors including outpatient clinics. One of the main drawbacks in outpatient clinics is long waiting time for patients-which affects the level of patient satisfaction and the quality of services. This article addresses this problem by studying the Outpatient Management Software (OMS) and proposing solutions to reduce waiting times. Many hospitals around the world apply solutions to overcome the problem of long waiting times in outpatient clinics such as hospitals in the USA, China, Sri Lanka, and Taiwan. These clinics have succeeded in reducing wait times by 15%, 78%, 60% and 50%, respectively. Such solutions depend mainly on adding more human resources or changing some business or management policies. The solutions presented in this article reduce waiting times by enhancing the software used to manage outpatient clinics services. Both quantitative and qualitative methods have been used to understand current OMS and examine level of patient's satisfaction. Five main problems that may cause high or unmeasured waiting time have been identified: appointment type, ticket numbering, doctor late arrival, early arriving patient and patients' distribution list. These problems have been mapped to the corresponding OMS components. Solutions to the above problems have been introduced and evaluated analytically or by simulation experiments. Evaluation of the results shows a reduction in patient waiting time. When late doctor arrival issues are solved, this can reduce the clinic service time by up to 20%. However, solutions for early arriving patients reduces 53.3% of vital time, 20% of the clinic time and overall 30.3% of the total waiting time. Finally, well patient-distribution lists make improvements by 54.2%. Improvements introduced to the patients' waiting time will consequently affect patients' satisfaction and improve the quality of health care services.

  8. Reducing Design Cycle Time and Cost Through Process Resequencing

    NASA Technical Reports Server (NTRS)

    Rogers, James L.

    2004-01-01

    In today's competitive environment, companies are under enormous pressure to reduce the time and cost of their design cycle. One method for reducing both time and cost is to develop an understanding of the flow of the design processes and the effects of the iterative subcycles that are found in complex design projects. Once these aspects are understood, the design manager can make decisions that take advantage of decomposition, concurrent engineering, and parallel processing techniques to reduce the total time and the total cost of the design cycle. One software tool that can aid in this decision-making process is the Design Manager's Aid for Intelligent Decomposition (DeMAID). The DeMAID software minimizes the feedback couplings that create iterative subcycles, groups processes into iterative subcycles, and decomposes the subcycles into a hierarchical structure. The real benefits of producing the best design in the least time and at a minimum cost are obtained from sequencing the processes in the subcycles.

  9. Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

    PubMed

    Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R

    2017-01-01

    Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

  10. Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks

    PubMed Central

    Naveros, Francisco; Garrido, Jesus A.; Carrillo, Richard R.; Ros, Eduardo; Luque, Niceto R.

    2017-01-01

    Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

  11. Developmental Switch in Spike Timing-Dependent Plasticity and Cannabinoid-Dependent Reorganization of the Thalamocortical Projection in the Barrel Cortex

    PubMed Central

    Huang, Jui-Yen; Yamasaki, Miwako; Watanabe, Masahiko; Lu, Hui-Chen

    2016-01-01

    The formation and refinement of thalamocortical axons (TCAs) is an activity-dependent process (Katz and Shatz, 1996), but its mechanism and nature of activity are elusive. We studied the role of spike timing-dependent plasticity (STDP) in TCA formation and refinement in mice. At birth (postnatal day 0, P0), TCAs invade the cortical plate, from which layers 4 (L4) and L2/3 differentiate at P3-P4. A portion of TCAs transiently reach toward the pia surface around P2-P4 (Senft and Woolsey, 1991; Rebsam et al., 2002) but are eventually confined below the border between L2/3 and L4. We previously showed that L4-L2/3 synapses exhibit STDP with only potentiation (timing-dependent long-term potentiation [t-LTP]) during synapse formation, then switch to a Hebbian form of STDP. Here we show that TCA-cortical plate synapses exhibit robust t-LTP in neonates, whose magnitude decreased gradually after P4-P5. After L2/3 is differentiated, TCA-L2/3 gradually switched to STDP with only depression (t-LTD) after P7-P8, whereas TCA-L4 lost STDP. t-LTP was dependent on NMDA receptor and PKA, whereas t-LTD was mediated by Type 1 cannabinoid receptors (CB1Rs) probably located at TCA terminals, revealed by global and cortical excitatory cell-specific knock-out of CB1R. Moreover, we found that administration of CB1R agonists, including Δ9-tetrahydrocannabinol, caused substantial retraction of TCAs. Consistent with this, individual thalamocortical axons exuberantly innervated L2/3 at P12 in CB1R knock-outs, indicating that endogenous cannabinoid signaling shapes TCA projection. These results suggest that the developmental switch in STDP and associated appearance of CB1R play important roles in the formation and refinement of TCAs. SIGNIFICANCE STATEMENT It has been shown that neural activity is required for initial synapse formation of thalamocortical axons with cortical cells, but precisely what sort of activities in presynaptic and postsynaptic cells are required is not yet clear. In

  12. Dendritic K+ channels contribute to spike-timing dependent long-term potentiation in hippocampal pyramidal neurons

    PubMed Central

    Watanabe, Shigeo; Hoffman, Dax A.; Migliore, Michele; Johnston, Daniel

    2002-01-01

    We investigated the role of A-type K+ channels for the induction of long-term potentiation (LTP) of Schaffer collateral inputs to hippocampal CA1 pyramidal neurons. When low-amplitude excitatory postsynaptic potentials (EPSPs) were paired with two postsynaptic action potentials in a theta-burst pattern, N-methyl-d-aspartate (NMDA)-receptor-dependent LTP was induced. The amplitudes of the back-propagating action potentials were boosted in the dendrites only when they were coincident with the EPSPs. Mitogen-activated protein kinase (MAPK) inhibitors PD 098059 or U0126 shifted the activation of dendritic K+ channels to more hyperpolarized potentials, reduced the boosting of dendritic action potentials by EPSPs, and suppressed the induction of LTP. These results support the hypothesis that dendritic K+ channels and the boosting of back-propagating action potentials contribute to the induction of LTP in CA1 neurons. PMID:12048251

  13. Detection and Differentiation of In Vitro-Spiked Bacteria by Real-Time PCR and Melting-Curve Analysis

    PubMed Central

    Klaschik, S.; Lehmann, L. E.; Raadts, A.; Book, M.; Gebel, J.; Hoeft, A.; Stuber, F.

    2004-01-01

    We introduce a consensus real-time PCR protocol for the detection of bacterial DNA from laboratory-prepared specimens such as water, urine, and plasma. This prototype detection system enables an exact Gram stain classification and, in particular, screening for specific species of 17 intensive care unit-relevant bacteria by means of fluorescence hybridization probes and melting-curve analysis in a one-run experiment. One strain of every species was tested at a final density of 106 CFU/ml. All bacteria examined except Staphylococcus aureus and Staphylococcus epidermidis could be differentiated successfully; S. aureus and S. epidermidis could only be classified as “Staphylococcus species.” The hands-on time for preparation of the DNA, performance of the PCR, and evaluation of the PCR results was less than 4 h. Nevertheless, this prototype detection system requires more clinical validation. PMID:14766809

  14. Reducing the magnet switching time for AMS at PRIME Lab

    NASA Astrophysics Data System (ADS)

    Perry, M.; Elmore, D.; Rickey, F.; Bhukhanwala, B.; Chong, E.

    1997-03-01

    At the Purdue Rare Isotope Measurement Laboratory, we are cycling magnets to obtain isotope ratios. Minimizing cycle time increases the precision of those measurements. The magnet change time is a significant fraction of the total cycle time since the magnets must be steady to within a few hundred ppm before measurements can begin. PRIME Lab's goal was to obtain isotope ratios to a precision of 1% or better given sufficient counting statistics. A conventional PID controller was implemented. This reduced the magnet switching time for the injector magnet by approximately 50% and the analyzer magnet by approximately 10%. A dual controller was then implemented. The dual controller consists of an optimization algorithm, called the settling-time optimizer, and a fuzzy logic controller. The settling-time optimizer reduces the settling-time with every cycle based on the history of previous cycles and the fuzzy logic controller compensates for any disturbances in the system. This controller gave a much better performance by reducing the switching times for both magnets by approximately 75%.

  15. Kinetic properties and functional dynamics of sodium channels during repetitive spiking in a slow pacemaker neuron

    PubMed Central

    Milescu, Lorin S.; Yamanishi, Tadashi; Ptak, Krzysztof; Smith, Jeffrey C.

    2010-01-01

    We examined the kinetic properties of voltage-gated Na+ channels and their contribution to the repetitive spiking activity of medullary raphé neurons, which exhibit slow pacemaking and strong spiking adaptation. The study is based on a combination of whole-cell patch clamp, modeling and real-time computation. Na+ currents were recorded from neurons in brain slices obtained from male and female neonatal rats, using voltage-clamp protocols designed to reduce space-clamp artifacts and to emphasize functionally relevant kinetic features. A detailed kinetic model was formulated to explain the broad range of transient and stationary voltage-dependent properties exhibited by Na+ currents. The model was tested by injecting via dynamic clamp a model-based current as a substitute for the native TTX-sensitive Na+ currents, which were pharmacologically blocked. The model-based current reproduced well the native spike shape and spiking frequency. The dynamics of Na+ channels during repetitive spiking were indirectly examined through this model. By comparing the spiking activities generated with different kinetic models in dynamic clamp experiments, we determined that state-dependent slow inactivation contributes significantly to spiking adaptation. Through real-time manipulation of the model-based current, we established that suprathreshold Na+ current mainly controls spike shape, whereas subthreshold Na+ current modulates spiking frequency and contributes to the pacemaking mechanism. Since the model-based current was injected in the soma, the results also suggest that somatic Na+ channels are sufficient to establish the essential spiking properties of raphé neurons in vitro. PMID:20826674

  16. Intracoronary brachytherapy following drug-eluting stent failure It's still not time to hang up the spikes{exclamation_point}

    SciTech Connect

    Angiolillo, Dominick J.; Sabate, Manel; Jimenez-Quevedo, Pilar; Alfonso, Fernando; Galvan, Carmen; Fernandez, Jose Miguel; Hernandez-Antolin, Rosana; Escaned, Javier; Banuelos, Camino; Moreno, Raul; Macaya, Carlos

    2003-12-01

    Drug-eluting stents (DES) have significantly reduced the incidence of restenosis. Although the results obtained with these novel antiproliferative devices are encouraging, recent reports have shown that DES are not completely immune from restenosis. Therefore, the broad use of DES has inevitably led to a major issue: treatment of DES failure. Intracoronary brachytherapy (IBT) represents an important advancement for treatment of in-stent restenosis (ISR) and has led to important pathophysiological insight on the restenotic process. To date, IBT, when properly used, still represents the gold standard for treatment of ISR. However, experience with IBT is for treatment of ISR occurring with bare metal stents (BMS). Whether IBT may be used with the same safety and efficacy profile as an adjunctive treatment for ISR following DES implantation is still unknown. In this article, we report the outcome of a series of patients with DES failure treated with IBT. IBT for treatment of DES failure was shown to be both safe and efficient and, therefore, until ISR exists, IBT still remains an important player in this growing and even more challenging setting.

  17. Implementing spiking neural networks for real-time signal-processing and control applications: a model-validated FPGA approach.

    PubMed

    Pearson, Martin J; Pipe, A G; Mitchinson, B; Gurney, K; Melhuish, C; Gilhespy, I; Nibouche, M

    2007-09-01

    In this paper, we present two versions of a hardware processing architecture for modeling large networks of leaky-integrate-and-fire (LIF) neurons; the second version provides performance enhancing features relative to the first. Both versions of the architecture use fixed-point arithmetic and have been implemented using a single field-programmable gate array (FPGA). They have successfully simulated networks of over 1000 neurons configured using biologically plausible models of mammalian neural systems. The neuroprocessor has been designed to be employed primarily for use on mobile robotic vehicles, allowing bio-inspired neural processing models to be integrated directly into real-world control environments. When a neuroprocessor has been designed to act as part of the closed-loop system of a feedback controller, it is imperative to maintain strict real-time performance at all times, in order to maintain integrity of the control system. This resulted in the reevaluation of some of the architectural features of existing hardware for biologically plausible neural networks (NNs). In addition, we describe a development system for rapidly porting an underlying model (based on floating-point arithmetic) to the fixed-point representation of the FPGA-based neuroprocessor, thereby allowing validation of the hardware architecture. The developmental system environment facilitates the cooperation of computational neuroscientists and engineers working on embodied (robotic) systems with neural controllers, as demonstrated by our own experience on the Whiskerbot project, in which we developed models of the rodent whisker sensory system.

  18. Neuronal communication: firing spikes with spikes.

    PubMed

    Brecht, Michael

    2012-08-21

    Spikes of single cortical neurons can exert powerful effects even though most cortical synapses are too weak to fire postsynaptic neurons. A recent study combining single-cell stimulation with population imaging has visualized in vivo postsynaptic firing in genetically identified target cells. The results confirm predictions from in vitro work and might help to understand how the brain reads single-neuron activity.

  19. Changing Times: The Use of Reduced Work Time Options in the United States.

    ERIC Educational Resources Information Center

    Olmsted, Barney

    1983-01-01

    This article addresses the increase in voluntary reduced work time arrangements that have developed in the United States in response to growing interest in alternatives to the standardized approach to scheduling. Permanent part-time employment, job sharing, and voluntary reduced work time plans are defined, described and, to a limited extent,…

  20. Theta Burst Firing Recruits BDNF Release and Signaling in Postsynaptic CA1 Neurons in Spike-Timing-Dependent LTP.

    PubMed

    Edelmann, Elke; Cepeda-Prado, Efrain; Franck, Martin; Lichtenecker, Petra; Brigadski, Tanja; Leßmann, Volkmar

    2015-05-20

    Timing-dependent LTP (t-LTP) is a physiologically relevant type of synaptic plasticity that results from repeated sequential firing of action potentials (APs) in pre- and postsynaptic neurons. t-LTP can be observed in vivo and is proposed to be a cellular correlate of memory formation. While brain-derived neurotrophic factor (BDNF) is essential to high-frequency stimulation-induced LTP in many brain areas, the role of BDNF in t-LTP is largely unknown. Here, we demonstrate a striking change in the expression mechanism of t-LTP in CA1 of the hippocampus following two distinct modes of synaptic activation. Single postsynaptic APs paired with presynaptic stimulation activated a BDNF-independent canonical t-LTP. In contrast, a theta burst of postsynaptic APs preceded by presynaptic stimulation elicited BDNF-dependent postsynaptic t-LTP that relied on postsynaptic BDNF secretion. This suggests that BDNF release during burst-like patterns of activity typically observed in vivo may play a crucial role during memory formation.

  1. Dopamine D1 and D5 Receptors Modulate Spike Timing-Dependent Plasticity at Medial Perforant Path to Dentate Granule Cell Synapses

    PubMed Central

    Yang, Kechun

    2014-01-01

    Although evidence suggests that DA modulates hippocampal function, the mechanisms underlying that dopaminergic modulation are largely unknown. Using perforated-patch electrophysiological techniques to maintain the intracellular milieu, we investigated how the activation of D1-type DA receptors regulates spike timing-dependent plasticity (STDP) of the medial perforant path (mPP) synapse onto dentate granule cells. When D1-type receptors were inhibited, a relatively mild STDP protocol induced LTP only within a very narrow timing window between presynaptic stimulation and postsynaptic response. The stimulus protocol produced timing-dependent LTP (tLTP) only when the presynaptic stimulation was followed 30 ms later by depolarization-induced postsynaptic action potentials. That is, the time between presynaptic stimulation and postsynaptic response was 30 ms (Δt = +30 ms). When D1-type receptors were activated, however, the same mild STDP protocol induced tLTP over a much broader timing window: tLTP was induced when −30 ms ≤ Δt ≤ +30 ms. The result indicated that D1-type receptor activation enabled synaptic potentiation even when postsynaptic activity preceded presynaptic stimulation within this Δt range. Results with null mice lacking the Kv4.2 potassium channel and with the potassium channel inhibitor, 4-aminopyridine, suggested that D1-type receptors enhanced tLTP induction by suppressing the transient IA-type K+ current. Results obtained with antagonists and DA receptor knock-out mice indicated that endogenous activity of both D1 and D5 receptors modulated plasticity in the mPP. The DA D5 receptors appeared particularly important in regulating plasticity of the mPP onto the dentate granule cells. PMID:25429131

  2. Rapid Analysis Model: Reducing Analysis Time without Sacrificing Quality.

    ERIC Educational Resources Information Center

    Lee, William W.; Owens, Diana

    2001-01-01

    Discusses the performance technology design process and the fact that the analysis phase is often being eliminated to speed up the process. Proposes a rapid analysis model that reduces time needed for analysis and still ensures more successful value-added solutions that focus on customer satisfaction. (LRW)

  3. Reducing current reversal time in electric motor control

    DOEpatents

    Bredemann, Michael V

    2014-11-04

    The time required to reverse current flow in an electric motor is reduced by exploiting inductive current that persists in the motor when power is temporarily removed. Energy associated with this inductive current is used to initiate reverse current flow in the motor.

  4. Consequences and mechanisms of spike broadening of R20 cells in Aplysia californica.

    PubMed

    Ma, M; Koester, J

    1995-10-01

    We studied frequency-dependent spike broadening in the two electrically coupled R20 neurons in the abdominal ganglion of Aplysia. The peptidergic R20 cells excite the R25/L25 interneurons (which trigger respiratory pumping) and inhibit the RB cells. When fired at 1-10 Hz, the duration of the falling phase of the action potential in R20 neurons increases 2-10 fold during a spike train. Spike broadening recorded from the somata of the R20 cells affected synaptic transmission to nearby follower cells. Chemically mediated synaptic output was reduced by approximately 50% when recorded trains of nonbroadened action potentials were used as command signals for a voltage-clamped R20 cell. Electrotonic EPSPs between the R20 cells, which normally facilitated by two- to fourfold during a high frequency spike train, showed no facilitation when spike broadening was prevented under voltage-clamp control. To examine the mechanism of frequency-dependent spike broadening, we applied two-electrode voltage-clamp and pharmacological techniques to the somata of R20 cells. Several voltage-gated ionic currents were isolated, including INa, a multicomponent ICa, and three K+ currents--a high threshold, fast transient A-type K+ current (IAdepol), a delayed rectifier K+ current (IK-V), and a Ca(2+)-sensitive K+ current (IK-Ca), made up of two components. The influences of different currents on spike broadening were determined by using the recorded train of gradually broadening action potentials as the command for the voltage clamp. We found the following. (1) IAdepol is the major outward current that contributes to repolarization of nonbroadened spikes. It undergoes pronounced cumulative inactivation that is a critical determinant of spike broadening. (2) Activity-dependent changes in IK-V, IK-Ca, and ICa have complex effects on the kinetics and extent of broadening. (3) The time integral of ICa during individual action potentials increases approximately threefold during spike broadening.

  5. Symmetric spike timing-dependent plasticity at CA3–CA3 synapses optimizes storage and recall in autoassociative networks

    PubMed Central

    Mishra, Rajiv K.; Kim, Sooyun; Guzman, Segundo J.; Jonas, Peter

    2016-01-01

    CA3–CA3 recurrent excitatory synapses are thought to play a key role in memory storage and pattern completion. Whether the plasticity properties of these synapses are consistent with their proposed network functions remains unclear. Here, we examine the properties of spike timing-dependent plasticity (STDP) at CA3–CA3 synapses. Low-frequency pairing of excitatory postsynaptic potentials (EPSPs) and action potentials (APs) induces long-term potentiation (LTP), independent of temporal order. The STDP curve is symmetric and broad (half-width ∼150 ms). Consistent with these STDP induction properties, AP–EPSP sequences lead to supralinear summation of spine [Ca2+] transients. Furthermore, afterdepolarizations (ADPs) following APs efficiently propagate into dendrites of CA3 pyramidal neurons, and EPSPs summate with dendritic ADPs. In autoassociative network models, storage and recall are more robust with symmetric than with asymmetric STDP rules. Thus, a specialized STDP induction rule allows reliable storage and recall of information in the hippocampal CA3 network. PMID:27174042

  6. Comparison of multiplex PCR hybridization-based and singleplex real-time PCR-based assays for detection of low prevalence pathogens in spiked samples.

    PubMed

    Hockman, Donna; Dong, Ming; Zheng, Hong; Kumar, Sanjai; Huff, Matthew D; Grigorenko, Elena; Beanan, Maureen; Duncan, Robert

    2017-01-01

    Molecular diagnostic devices are increasingly finding utility in clinical laboratories. Demonstration of the effectiveness of these devices is dependent upon comparing results from clinical samples tested with the new device to an alternative testing method. The preparation of mock clinical specimens will be necessary for the validation of molecular diagnostic devices when a sufficient number of clinical specimens is unobtainable. Examples include rare pathogens, some of which are pathogens posing a biological weapon threat. Here we describe standardized steps for developers to follow for the culture and quantification of three organisms used to spike human whole blood to create mock specimens. The three organisms chosen for this study were the Live Vaccine Strain (LVS) of Francisella tularensis, surrogate for a potential biothreat pathogen, Escherichia coli, a representative Gram-negative bacterium and Babesia microti (Franca) Reichenow Peabody strain, representing a protozoan parasite. Mock specimens were prepared with blood from both healthy donors and donors with nonspecific symptoms including fever, malaise, and flu-like symptoms. There was no significant difference in detection results between the two groups for any pathogen. Testing of the mock samples was compared on two platforms, Target Enriched Multiplex-PCR (TEM-PCR™) and singleplex real-time PCR (RT-PCR). Results were reproducible on both platforms. The reproducibility demonstrated by obtaining the same results between two testing methods and between healthy and symptomatic mock specimens, indicates the standardized methods described for creating the mock specimens are valid and effective for evaluating diagnostic devices.

  7. Effect of Spike-Timing-Dependent Plasticity on Intrinsic Coherence Resonance in Newman-Watts Stochastic Hodgkin-Huxley Neuronal Networks

    NASA Astrophysics Data System (ADS)

    Xie, Huijuan; Gong, Yubing; Wang, Qi

    2016-07-01

    In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on coherence resonance (CR) induced by channel noise in adaptive Newman-Watts stochastic Hodgkin-Huxley neuron networks. It is found that STDP can either enhance or suppress the intrinsic CR when the adjusting rate of STDP decreases or increases. STDP can alter the effects of network randomness and network size on the intrinsic CR. Under STDP, for electrical coupling there are optimal network randomness and network size by which the intrinsic CR becomes strongest, however, for chemical coupling the intrinsic CR is always enhanced as network randomness or network size increases, which are different from the results for fixed coupling. These results show that the intrinsic CR of the neuronal networks can be either enhanced or suppressed by STDP, and there are optimal network randomness and network size by which the intrinsic CR becomes strongest. These findings could provide a new insight into the role of STDP for the information processing and transmission in neural systems.

  8. Spike timing of lacunosom-moleculare targeting interneurons and CA3 pyramidal cells during high-frequency network oscillations in vitro.

    PubMed

    Spampanato, Jay; Mody, Istvan

    2007-07-01

    Network activity in the 200- to 600-Hz range termed high-frequency oscillations (HFOs) has been detected in epileptic tissue from both humans and rodents and may underlie the mechanism of epileptogenesis in experimental rodent models. Slower network oscillations including theta and gamma oscillations as well as ripples are generated by the complex spike timing and interactions between interneurons and pyramidal cells of the hippocampus. We determined the activity of CA3 pyramidal cells, stratum oriens lacunosum-moleculare (O-LM) and s. radiatum lacunosum-moleculare (R-LM) interneurons during HFO in the in vitro low-Mg(2+) model of epileptiform activity in GIN mice. In these animals, interneurons can be identified prior to cell-attached recordings by the expression of green-fluorescent protein (GFP). Simultaneous local field potential recordings from s. pyramidale and on-cell recordings of individual interneurons and principal cells revealed three primary firing behaviors of the active cells: 36% of O-LM interneurons and 60% of pyramidal cells fired action potentials at high frequencies during the HFO. R-LM interneurons were biphasic in that they fired at high frequency at the beginning of the HFO but stopped firing before its end. When considering only the highest frequency component of the oscillations most pyramidal cells fired on the rising phase of the oscillation. These data provide evidence for functional distinction during HFOs within otherwise homogeneous groups of O-LM interneurons and pyramidal cells.

  9. Statistical properties of superimposed stationary spike trains.

    PubMed

    Deger, Moritz; Helias, Moritz; Boucsein, Clemens; Rotter, Stefan

    2012-06-01

    The Poisson process is an often employed model for the activity of neuronal populations. It is known, though, that superpositions of realistic, non- Poisson spike trains are not in general Poisson processes, not even for large numbers of superimposed processes. Here we construct superimposed spike trains from intracellular in vivo recordings from rat neocortex neurons and compare their statistics to specific point process models. The constructed superimposed spike trains reveal strong deviations from the Poisson model. We find that superpositions of model spike trains that take the effective refractoriness of the neurons into account yield a much better description. A minimal model of this kind is the Poisson process with dead-time (PPD). For this process, and for superpositions thereof, we obtain analytical expressions for some second-order statistical quantities-like the count variability, inter-spike interval (ISI) variability and ISI correlations-and demonstrate the match with the in vivo data. We conclude that effective refractoriness is the key property that shapes the statistical properties of the superposition spike trains. We present new, efficient algorithms to generate superpositions of PPDs and of gamma processes that can be used to provide more realistic background input in simulations of networks of spiking neurons. Using these generators, we show in simulations that neurons which receive superimposed spike trains as input are highly sensitive for the statistical effects induced by neuronal refractoriness.

  10. Noise-induced interspike interval correlations and spike train regularization in spike-triggered adapting neurons

    NASA Astrophysics Data System (ADS)

    Urdapilleta, Eugenio

    2016-09-01

    Spike generation in neurons produces a temporal point process, whose statistics is governed by intrinsic phenomena and the external incoming inputs to be coded. In particular, spike-evoked adaptation currents support a slow temporal process that conditions spiking probability at the present time according to past activity. In this work, we study the statistics of interspike interval correlations arising in such non-renewal spike trains, for a neuron model that reproduces different spike modes in a small adaptation scenario. We found that correlations are stronger as the neuron fires at a particular firing rate, which is defined by the adaptation process. When set in a subthreshold regime, the neuron may sustain this particular firing rate, and thus induce correlations, by noise. Given that, in this regime, interspike intervals are negatively correlated at any lag, this effect surprisingly implies a reduction in the variability of the spike count statistics at a finite noise intensity.

  11. Reducing the contact time of a bouncing drop.

    PubMed

    Bird, James C; Dhiman, Rajeev; Kwon, Hyuk-Min; Varanasi, Kripa K

    2013-11-21

    Surfaces designed so that drops do not adhere to them but instead bounce off have received substantial attention because of their ability to stay dry, self-clean and resist icing. A drop striking a non-wetting surface of this type will spread out to a maximum diameter and then recoil to such an extent that it completely rebounds and leaves the solid material. The amount of time that the drop is in contact with the solid--the 'contact time'--depends on the inertia and capillarity of the drop, internal dissipation and surface-liquid interactions. And because contact time controls the extent to which mass, momentum and energy are exchanged between drop and surface, it is often advantageous to minimize it. The conventional approach has been to minimize surface-liquid interactions that can lead to contact line pinning; but even in the absence of any surface interactions, drop hydrodynamics imposes a minimum contact time that was conventionally assumed to be attained with axisymmetrically spreading and recoiling drops. Here we demonstrate that it is possible to reduce the contact time below this theoretical limit by using superhydrophobic surfaces with a morphology that redistributes the liquid mass and thereby alters the drop hydrodynamics. We show theoretically and experimentally that this approach allows us to reduce the overall contact time between a bouncing drop and a surface below what was previously thought possible.

  12. Simultaneous service approach for reducing air passenger queue time

    SciTech Connect

    Chung, C.A.; Sodeinde, T.

    2000-02-01

    Simultaneous service offers service-oriented organizations, such as the air passenger transportation industry, the opportunity to differentiate themselves by providing superior customer service. The concept of simultaneous service is analogous to the concept of simultaneous engineering. However, while simultaneous engineering strives to minimize product development time, simultaneous service strives to minimize customer processing time. Overall customer processing time is reduced by the identification and simultaneous alignment of previously sequentially executed customer service activities. A simultaneous service approach was applied to the international ticketing counter at a major international airport. This involved developing both equipment and operational policy alternatives to the normal sequencing of the ticketing and baggage check-in process. A total of five alternative simultaneous service approaches were investigated. Simulation analysis of these alternatives indicates that a 36% improvement in customer queue and servicing time was possible with this approach.

  13. Applying Lean Methodologies Reduces Emergency Department Laboratory Turnaround Times

    PubMed Central

    White, Benjamin A.; Baron, Jason M.; Dighe, Anand S.; Camargo, Carlos A.; Brown, David F.M.

    2015-01-01

    Background Increasing the value of healthcare delivery is a national priority, and providers face growing pressure to reduce cost while improving quality. Ample opportunity exists to increase efficiency and quality simultaneously through the application of systems engineering science. Objective We examined the hypothesis that Lean-based reorganization of laboratory process flow would improve laboratory turnaround times (TAT) and reduce waste in the system. Methods This study was a prospective, before-after analysis of laboratory process improvement in a teaching hospital Emergency Department (ED). The intervention included a reorganization of laboratory sample flow based in systems engineering science and Lean methodologies, with no additional resources. The primary outcome was the median TAT from sample collection to result for six tests previously performed in an ED kiosk. Results Following the intervention, median laboratory TAT decreased across most tests. The greatest decreases were found in “reflex tests” performed after an initial screening test: troponin T TAT was reduced by 33 minutes (86 to 53 min, 99%CI 30–35 min) and urine sedimentation TAT by 88 minutes (117 to 29 min, 99% CI 87–90 min). In addition, troponin I TAT was reduced by 12 minutes, urinalysis by 9 minutes, and urine HCG by 10 minutes. Microbiology rapid testing TAT, a ‘control’, did not change. Conclusions In this study, Lean-based reorganization of laboratory process flow significantly increased process efficiency. Broader application of systems engineering science might further improve healthcare quality and capacity, while reducing waste and cost. PMID:26145581

  14. Solar Decameter Spikes

    NASA Astrophysics Data System (ADS)

    Melnik, V. N.; Shevchuk, N. V.; Konovalenko, A. A.; Rucker, H. O.; Dorovskyy, V. V.; Poedts, S.; Lecacheux, A.

    2014-05-01

    We analyze and discuss the properties of decameter spikes observed in July - August 2002 by the UTR-2 radio telescope. These bursts have a short duration (about one second) and occur in a narrow frequency bandwidth (50 - 70 kHz). They are chaotically located in the dynamic spectrum. Decameter spikes are weak bursts: their fluxes do not exceed 200 - 300 s.f.u. An interesting feature of these spikes is the observed linear increase of the frequency bandwidth with frequency. This dependence can be explained in the framework of the plasma mechanism that causes the radio emission, taking into account that Langmuir waves are generated by fast electrons within a narrow angle θ≈13∘ - 18∘ along the direction of the electron propagation. In the present article we consider the problem of the short lifetime of decameter spikes and discuss why electrons generate plasma waves in limited regions.

  15. Theta phase shift in spike timing and modulation of gamma oscillation: a dynamic code for spatial alternation during fixation in rat hippocampal area CA1

    PubMed Central

    Nishida, Hiroshi; David Redish, A.; Lauwereyns, Johan

    2014-01-01

    Although hippocampus is thought to perform various memory-related functions, little is known about the underlying dynamics of neural activity during a preparatory stage before a spatial choice. Here we focus on neural activity that reflects a memory-based code for spatial alternation, independent of current sensory and motor parameters. We recorded multiple single units and local field potentials in the stratum pyramidale of dorsal hippocampal area CA1 while rats performed a delayed spatial-alternation task. This task includes a 1-s fixation in a nose-poke port between selecting alternating reward sites and so provides time-locked enter-and-leave events. At the single-unit level, we concentrated on neurons that were specifically active during the 1-s fixation period, when the rat was ready and waiting for a cue to pursue the task. These neurons showed selective activity as a function of the alternation sequence. We observed a marked shift in the phase timing of the neuronal spikes relative to the theta oscillation, from the theta peak at the beginning of fixation to the theta trough at the end of fixation. The gamma-band local field potential also changed during the fixation period: the high-gamma power (60–90 Hz) decreased and the low-gamma power (30–45 Hz) increased toward the end. These two gamma components were observed at different phases of the ongoing theta oscillation. Taken together, our data suggest a switch in the type of information processing through the fixation period, from externally cued to internally generated. PMID:24478159

  16. Millisecond Radio Spikes in the Decimetric Band

    NASA Astrophysics Data System (ADS)

    Dąbrowski, B. P.; Rudawy, P.; Karlický, M.

    We present the results of the analysis of thirteen events consisting of dm-spikes observed in Toruń between 15 March 2000 and 30 October 2001. The events were obtained with a very high time resolution (80 microseconds) radio spectrograph in the 1352 - 1490 MHz range. These data were complemented with observations from the radio spectrograph at Ondřejov in the 0.8 - 2.0 GHz band. We evaluated the basic characteristics of the individual spikes (duration, spectral width, and frequency drifts), as well as their groups and chains, the location of their emission sources, and the temporal correlations of the emissions with various phases of the associated solar flares. We found that the mean duration and spectral width of the radio spikes are equal to 0.036 s and 9.96 MHz, respectively. Distributions of the duration and spectral widths of the spikes have positive skewness for all investigated events. Each spike shows positive or negative frequency drift. The mean negative and positive drifts of the investigated spikes are equal to -776 MHz s-1 and 1608 MHz s-1, respectively. The emission sources of the dm-spikes are located mainly at disk center. We have noticed two kinds of chains, with and without frequency drifts. The mean durations of the chains vary between 0.067 s and 0.509 s, while their spectral widths vary between 7.2 MHz and 17.25 MHz. The mean duration of an individual spike observed in a chain was equal to 0.03 s. While we found some agreement between the global characteristics of the groups of spikes recorded with the two instruments located in Toruń and Ondřejov, we did not find any one-to-one relation between individual spikes.

  17. Millisecond Radio Spikes in the Decimetric Band

    NASA Astrophysics Data System (ADS)

    dąbrowski, B. P.; Rudawy, P.; Karlický, M.

    2011-11-01

    We present the results of the analysis of thirteen events consisting of dm-spikes observed in Toruń between 15 March 2000 and 30 October 2001. The events were obtained with a very high time resolution (80 microseconds) radio spectrograph in the 1352 - 1490 MHz range. These data were complemented with observations from the radio spectrograph at Ondřejov in the 0.8 - 2.0 GHz band. We evaluated the basic characteristics of the individual spikes (duration, spectral width, and frequency drifts), as well as their groups and chains, the location of their emission sources, and the temporal correlations of the emissions with various phases of the associated solar flares. We found that the mean duration and spectral width of the radio spikes are equal to 0.036 s and 9.96 MHz, respectively. Distributions of the duration and spectral widths of the spikes have positive skewness for all investigated events. Each spike shows positive or negative frequency drift. The mean negative and positive drifts of the investigated spikes are equal to -776 MHz s-1 and 1608 MHz s-1, respectively. The emission sources of the dm-spikes are located mainly at disk center. We have noticed two kinds of chains, with and without frequency drifts. The mean durations of the chains vary between 0.067 s and 0.509 s, while their spectral widths vary between 7.2 MHz and 17.25 MHz. The mean duration of an individual spike observed in a chain was equal to 0.03 s. While we found some agreement between the global characteristics of the groups of spikes recorded with the two instruments located in Toruń and Ondřejov, we did not find any one-to-one relation between individual spikes.

  18. Reducing video frame rate increases remote optimal focus time

    NASA Technical Reports Server (NTRS)

    Haines, Richard F.

    1993-01-01

    Twelve observers made best optical focus adjustments to a microscope whose high-resolution pattern was video monitored and displayed first on a National Television System Committee (NTSC) analog color monitor and second on a digitally compressed computer monitor screen at frame rates ranging (in six steps) from 1.5 to 30 frames per second (fps). This was done to determine whether reducing the frame rate affects the image focus. Reducing frame rate has been shown to be an effective and acceptable means of reducing transmission bandwidth of dynamic video imagery sent from Space Station Freedom (SSF) to ground scientists. Three responses were recorded per trial: time to complete the focus adjustment, number of changes of focus direction, and subjective rating of final image quality. It was found that: the average time to complete the focus setting increases from 4.5 sec at 30 fps to 7.9 sec at 1.5 fps (statistical probability = 1.2 x 10(exp -7)); there is no significant difference in the number of changes in the direction of focus adjustment across these frame rates; and there is no significant change in subjectively determined final image quality across these frame rates. These data can be used to help pre-plan future remote optical-focus operations on SSF.

  19. Effects of a Reduced Time-Out Interval on Compliance with the Time-Out Instruction

    ERIC Educational Resources Information Center

    Donaldson, Jeanne M.; Vollmer, Timothy R.; Yakich, Theresa M.; Van Camp, Carole

    2013-01-01

    Time-out is a negative punishment procedure that parents and teachers commonly use to reduce problem behavior; however, specific time-out parameters have not been evaluated adequately. One parameter that has received relatively little attention in the literature is the mode of administration (verbal or physical) of time-out. In this study, we…

  20. Intra-spike crosslinking overcomes antibody evasion by HIV-1

    PubMed Central

    Galimidi, Rachel P.; Klein, Joshua S.; Politzer, Maria S.; Bai, Shiyu; Seaman, Michael S.; Nussenzweig, Michel C.; West, Anthony P.; Bjorkman, Pamela J.

    2015-01-01

    SUMMARY Antibodies developed during HIV-1 infection lose efficacy as the viral spike mutates. We postulated that anti-HIV-1 antibodies primarily bind monovalently because HIV’s low spike density impedes bivalent binding through inter-spike crosslinking, and the spike structure prohibits bivalent binding through intra-spike crosslinking. Monovalent binding reduces avidity and potency, thus expanding the range of mutations permitting antibody evasion. To test this idea, we engineered antibody-based molecules capable of bivalent binding through intra-spike crosslinking. We used DNA as a “molecular ruler” to measure intra-epitope distances on virion-bound spikes and construct intra-spike crosslinking molecules. Optimal bivalent reagents exhibited up to 2.5 orders-of-magnitude increased potency (>100-fold average increases across virus panels) and identified conformational states of virion-bound spikes. The demonstration that intra-spike crosslinking lowers the concentration of antibodies required for neutralization supports the hypothesis that low spike densities facilitate antibody evasion and the use of molecules capable of intra-spike crosslinking for therapy or passive protection. PMID:25635457

  1. Prospective Coding by Spiking Neurons

    PubMed Central

    Brea, Johanni; Gaál, Alexisz Tamás; Senn, Walter

    2016-01-01

    Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their learning is not well understood. Here we propose a biologically plausible synaptic plasticity rule to learn predictions on a single neuron level on a timescale of seconds. The learning rule allows a spiking two-compartment neuron to match its current firing rate to its own expected future discounted firing rate. For instance, if an originally neutral event is repeatedly followed by an event that elevates the firing rate of a neuron, the originally neutral event will eventually also elevate the neuron’s firing rate. The plasticity rule is a form of spike timing dependent plasticity in which a presynaptic spike followed by a postsynaptic spike leads to potentiation. Even if the plasticity window has a width of 20 milliseconds, associations on the time scale of seconds can be learned. We illustrate prospective coding with three examples: learning to predict a time varying input, learning to predict the next stimulus in a delayed paired-associate task and learning with a recurrent network to reproduce a temporally compressed version of a sequence. We discuss the potential role of the learning mechanism in classical trace conditioning. In the special case that the signal to be predicted encodes reward, the neuron learns to predict the discounted future reward and learning is closely related to the temporal difference learning algorithm TD(λ). PMID:27341100

  2. Reducing the Time and Cost of Testing Engines

    NASA Technical Reports Server (NTRS)

    2004-01-01

    Producing a new aircraft engine currently costs approximately $1 billion, with 3 years of development time for a commercial engine and 10 years for a military engine. The high development time and cost make it extremely difficult to transition advanced technologies for cleaner, quieter, and more efficient new engines. To reduce this time and cost, NASA created a vision for the future where designers would use high-fidelity computer simulations early in the design process in order to resolve critical design issues before building the expensive engine hardware. To accomplish this vision, NASA's Glenn Research Center initiated a collaborative effort with the aerospace industry and academia to develop its Numerical Propulsion System Simulation (NPSS), an advanced engineering environment for the analysis and design of aerospace propulsion systems and components. Partners estimate that using NPSS has the potential to dramatically reduce the time, effort, and expense necessary to design and test jet engines by generating sophisticated computer simulations of an aerospace object or system. These simulations will permit an engineer to test various design options without having to conduct costly and time-consuming real-life tests. By accelerating and streamlining the engine system design analysis and test phases, NPSS facilitates bringing the final product to market faster. NASA's NPSS Version (V)1.X effort was a task within the Agency s Computational Aerospace Sciences project of the High Performance Computing and Communication program, which had a mission to accelerate the availability of high-performance computing hardware and software to the U.S. aerospace community for its use in design processes. The technology brings value back to NASA by improving methods of analyzing and testing space transportation components.

  3. Overlap of movement planning and movement execution reduces reaction time.

    PubMed

    Orban de Xivry, Jean-Jacques; Legrain, Valéry; Lefèvre, Philippe

    2017-01-01

    Motor planning is the process of preparing the appropriate motor commands in order to achieve a goal. This process has largely been thought to occur before movement onset and traditionally has been associated with reaction time. However, in a virtual line bisection task we observed an overlap between movement planning and execution. In this task performed with a robotic manipulandum, we observed that participants (n = 30) made straight movements when the line was in front of them (near target) but often made curved movements when the same target was moved sideways (far target, which had the same orientation) in such a way that they crossed the line perpendicular to its orientation. Unexpectedly, movements to the far targets had shorter reaction times than movements to the near targets (mean difference: 32 ms, SE: 5 ms, max: 104 ms). In addition, the curvature of the movement modulated reaction time. A larger increase in movement curvature from the near to the far target was associated with a larger reduction in reaction time. These highly curved movements started with a transport phase during which accuracy demands were not taken into account. We conclude that an accuracy demand imposes a reaction time penalty if processed before movement onset. This penalty is reduced if the start of the movement consists of a transport phase and if the movement plan can be refined with respect to accuracy demands later in the movement, hence demonstrating an overlap between movement planning and execution.

  4. Electrophysiology of connection current spikes.

    PubMed

    Fish, Raymond M; Geddes, Leslie A

    2008-12-01

    Connection to a 60-Hz or other voltage source can result in cardiac dysrhythmias, a startle reaction, muscle contractions, and a variety of other physiological responses. Such responses can lead to injury, especially if significant ventricular cardiac dysrhythmias occur, or if a person is working at some height above ground and falls as a result of a musculoskeletal response. Physiological reactions are known to relate to intensity and duration of current exposure. The connection current that flows is a function of the applied voltage at the instant of connection, and the electrical impedance encountered by the voltage source in contact with the skin or other body tissues. In this article we describe a rarely investigated phenomenon, namely a contact, or connection, current spike that is many times higher than the steady-state current. This current spike occurs when an electrical connection is made at a non-zero voltage time in a sine wave or other waveform. Such current spikes may occur when electronic or manual switching or connecting of conductors occurs in electronic instrumentation connected to a patient. These findings are relevant to medical devices and instrumentation and to electrical safety in general.

  5. Reducing neural network training time with parallel processing

    NASA Technical Reports Server (NTRS)

    Rogers, James L., Jr.; Lamarsh, William J., II

    1995-01-01

    Obtaining optimal solutions for engineering design problems is often expensive because the process typically requires numerous iterations involving analysis and optimization programs. Previous research has shown that a near optimum solution can be obtained in less time by simulating a slow, expensive analysis with a fast, inexpensive neural network. A new approach has been developed to further reduce this time. This approach decomposes a large neural network into many smaller neural networks that can be trained in parallel. Guidelines are developed to avoid some of the pitfalls when training smaller neural networks in parallel. These guidelines allow the engineer: to determine the number of nodes on the hidden layer of the smaller neural networks; to choose the initial training weights; and to select a network configuration that will capture the interactions among the smaller neural networks. This paper presents results describing how these guidelines are developed.

  6. Fuzzy logic-based spike sorting system.

    PubMed

    Balasubramanian, Karthikeyan; Obeid, Iyad

    2011-05-15

    We present a new method for autonomous real-time spike sorting using a fuzzy logic inference engine. The engine assigns each detected event a 'spikiness index' from zero to one that quantifies the extent to which the detected event is like an ideal spike. Spikes can then be sorted by simply clustering the spikiness indices. The sorter is defined in terms of natural language rules that, once defined, are static and thus require no user intervention or calibration. The sorter was tested using extracellular recordings from three animals: a macaque, an owl monkey and a rat. Simulation results show that the fuzzy sorter performed equal to or better than the benchmark principal component analysis (PCA) based sorter. Importantly, there was no degradation in fuzzy sorter performance when the spikes were not temporally aligned prior to sorting. In contrast, PCA sorter performance dropped by 27% when sorting unaligned spikes. Since the fuzzy sorter is computationally trivial and requires no spike alignment, it is suitable for scaling into large numbers of parallel channels where computational overhead and the need for operator intervention would preclude other spike sorters.

  7. Bystander cells enhance NK cytotoxic efficiency by reducing search time.

    PubMed

    Zhou, Xiao; Zhao, Renping; Schwarz, Karsten; Mangeat, Matthieu; Schwarz, Eva C; Hamed, Mohamed; Bogeski, Ivan; Helms, Volkhard; Rieger, Heiko; Qu, Bin

    2017-03-13

    Natural killer (NK) cells play a central role during innate immune responses by eliminating pathogen-infected or tumorigenic cells. In the microenvironment, NK cells encounter not only target cells but also other cell types including non-target bystander cells. The impact of bystander cells on NK killing efficiency is, however, still elusive. In this study we show that the presence of bystander cells, such as P815, monocytes or HUVEC, enhances NK killing efficiency. With bystander cells present, the velocity and persistence of NK cells were increased, whereas the degranulation of lytic granules remained unchanged. Bystander cell-derived H2O2 was found to mediate the acceleration of NK cell migration. Using mathematical diffusion models, we confirm that local acceleration of NK cells in the vicinity of bystander cells reduces their search time to locate target cells. In addition, we found that integrin β chains (β1, β2 and β7) on NK cells are required for bystander-enhanced NK migration persistence. In conclusion, we show that acceleration of NK cell migration in the vicinity of H2O2-producing bystander cells reduces target cell search time and enhances NK killing efficiency.

  8. Bystander cells enhance NK cytotoxic efficiency by reducing search time

    PubMed Central

    Zhou, Xiao; Zhao, Renping; Schwarz, Karsten; Mangeat, Matthieu; Schwarz, Eva C.; Hamed, Mohamed; Bogeski, Ivan; Helms, Volkhard; Rieger, Heiko; Qu, Bin

    2017-01-01

    Natural killer (NK) cells play a central role during innate immune responses by eliminating pathogen-infected or tumorigenic cells. In the microenvironment, NK cells encounter not only target cells but also other cell types including non-target bystander cells. The impact of bystander cells on NK killing efficiency is, however, still elusive. In this study we show that the presence of bystander cells, such as P815, monocytes or HUVEC, enhances NK killing efficiency. With bystander cells present, the velocity and persistence of NK cells were increased, whereas the degranulation of lytic granules remained unchanged. Bystander cell-derived H2O2 was found to mediate the acceleration of NK cell migration. Using mathematical diffusion models, we confirm that local acceleration of NK cells in the vicinity of bystander cells reduces their search time to locate target cells. In addition, we found that integrin β chains (β1, β2 and β7) on NK cells are required for bystander-enhanced NK migration persistence. In conclusion, we show that acceleration of NK cell migration in the vicinity of H2O2-producing bystander cells reduces target cell search time and enhances NK killing efficiency. PMID:28287155

  9. Barbed micro-spikes for micro-scale biopsy

    NASA Astrophysics Data System (ADS)

    Byun, Sangwon; Lim, Jung-Min; Paik, Seung-Joon; Lee, Ahra; Koo, Kyo-in; Park, Sunkil; Park, Jaehong; Choi, Byoung-Doo; Seo, Jong Mo; Kim, Kyung-ah; Chung, Hum; Song, Si Young; Jeon, Doyoung; Cho, Dongil

    2005-06-01

    Single-crystal silicon planar micro-spikes with protruding barbs are developed for micro-scale biopsy and the feasibility of using the micro-spike as a micro-scale biopsy tool is evaluated for the first time. The fabrication process utilizes a deep silicon etch to define the micro-spike outline, resulting in protruding barbs of various shapes. Shanks of the fabricated micro-spikes are 3 mm long, 100 µm thick and 250 µm wide. Barbs protruding from micro-spike shanks facilitate the biopsy procedure by tearing off and retaining samples from target tissues. Micro-spikes with barbs successfully extracted tissue samples from the small intestines of the anesthetized pig, whereas micro-spikes without barbs failed to obtain a biopsy sample. Parylene coating can be applied to improve the biocompatibility of the micro-spike without deteriorating the biopsy function of the micro-spike. In addition, to show that the biopsy with the micro-spike can be applied to tissue analysis, samples obtained by micro-spikes were examined using immunofluorescent staining. Nuclei and F-actin of cells which are extracted by the micro-spike from a transwell were clearly visualized by immunofluorescent staining.

  10. Statistical Complexity of Neural Spike Trains

    DTIC Science & Technology

    2014-08-28

    SECURITY CLASSIFICATION OF: We present closed-form expressions for the entropy rate, statistical complexity, and predictive information for the spike...Triangle Park, NC 27709-2211 information, entropy rate, statistical complexity, excess entropy , integrate and fire neuron REPORT DOCUMENTATION PAGE 11...for the entropy rate, statistical complexity, and predictive information for the spike train of a single neuron in terms of the first passage time

  11. Using heat to reduce blood collection time in pediatric clients.

    PubMed

    Becht, D K; Anderson, M A

    1996-11-01

    Several states now require that all children attending day care, nursery schools, and kindergarten have evidence of a blood lead screening. Obtaining the requisite blood sample can be difficult because of inherent problems with pediatric clients. The purpose of this research was to explore if the application of heat to promote vasodilation affected finger stick collection time in pediatric clients. Subjects, ages 2 through 6 years, were randomly assigned to a control or intervention group (N = 63). For the intervention group, heat was applied for 1 min by placement of an infant heel warmer on the hand selected for the finger stick. A stopwatch was used to measure the time of blood collection in both the control and intervention groups. A two-tailed t test revealed statistical significance between the two groups (p = .008). Findings suggest the application of heat to promote vasodilation does reduce collection time in pediatric clients. Nurse practitioners may use this intervention to facilitate collection of blood samples in pediatric clients.

  12. Advances in applications of spiking neuron networks

    NASA Astrophysics Data System (ADS)

    Cios, Krzysztof J.; Sala, Dorel M.

    2000-03-01

    In this paper, we present new findings in constructing and applications of artificial neural networks that use a biologically inspired spiking neuron model. The used model is a point neuron with the interaction between neurons described by postsynaptic potentials. The synaptic plasticity is achieved by using a temporal correlation learning rule, specified as a function of time difference between the firings of pre- and post-synaptic neurons. Using this rule we show how certain associations between neurons in a network of spiking neurons can be implemented. As an example we analyze the dynamic properties of networks of laterally connected spiking neurons and we show their capability to self-organize into topological maps in response to external stimulation. In another application we explore the capability networks of spiking neurons to solve graph algorithms by using temporal coding of distances in a given spatial configuration. The paper underlines the importance of temporal dimension in artificial neural network information processing.

  13. Reducing chemical vapour infiltration time for ceramic matrix composites.

    PubMed

    Timms, L. A.; Westby, W.; Prentice, C.; Jaglin, D.; Shatwell, R. A.; Binner, J. G. P.

    2001-02-01

    Conventional routes to producing ceramic matrix composites (CMCs) require the use of high temperatures to sinter the individual ceramic particles of the matrix together. Sintering temperatures are typically much higher than the upper temperature limits of the fibres. This paper details preliminary work carried out on producing a CMC via chemical vapour infiltration (CVI), a process that involves lower processing temperatures, thus avoiding fibre degradation. The CVI process has been modified and supplemented in an attempt to reduce the CVI process time and to lower the cost of this typically expensive process. To this end microwave-enhanced CVI (MECVI) has been chosen, along with two alternative pre-infiltration steps: electrophoretic infiltration and vacuum bagging. The system under investigation is based on silicon carbide fibres within a silicon carbide matrix (SiCf/SiC). The results demonstrate that both approaches result in an enhanced initial density and a consequent significant reduction in the time required for the MECVI processing step. Dual energy X-ray absorptiometry was used as a non-destructive, density evaluation technique. Initial results indicate that the presence of the SiC powder in the pre-form changes the deposition profile during the MECVI process.

  14. Millisecond solar radio spikes observed at 1420 MHz

    NASA Astrophysics Data System (ADS)

    Dabrowski, B. P.; Kus, A. J.

    We present results from observations of narrowband solar millisecond radio spikes at 1420 MHz. Observing data were collected between February 2000 and December 2001 with the 15-m radio telescope at the Centre for Astronomy Nicolaus Copernicus University in Torun, Poland, equipped with a radio spectrograph that covered the 1352-1490 MHz frequency band. The radio spectrograph has 3 MHz frequency resolution and 80 microsecond time resolution. We analyzed the individual radio spike duration, bandwidth and rate of frequency drift. A part of the observed spikes showed well-outlined subtle structures. On dynamic radio spectrograms of the investigated events we notice complex structures formed by numerous individual spikes known as chains of spikes and distinctly different structure of columns. Positions of active regions connected with radio spikes emission were investigated. It turns out that most of them are located near the center of the solar disk, suggesting strong beaming of the spikes emission.

  15. Analyzing multiple spike trains with nonparametric Granger causality.

    PubMed

    Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou

    2009-08-01

    Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

  16. Studying spike trains using a van Rossum metric with a synapse-like filter.

    PubMed

    Houghton, Conor

    2009-02-01

    Spike trains are unreliable. For example, in the primary sensory areas, spike patterns and precise spike times will vary between responses to the same stimulus. Nonetheless, information about sensory inputs is communicated in the form of spike trains. A challenge in understanding spike trains is to assess the significance of individual spikes in encoding information. One approach is to define a spike train metric, allowing a distance to be calculated between pairs of spike trains. In a good metric, this distance will depend on the information the spike trains encode. This method has been used previously to calculate the timescale over which the precision of spike times is significant. Here, a new metric is constructed based on a simple model of synaptic conductances which includes binding site depletion. Including binding site depletion in the metric means that a given individual spike has a smaller effect on the distance if it occurs soon after other spikes. The metric proves effective at classifying neuronal responses by stimuli in the sample data set of electro-physiological recordings from the primary auditory area of the zebra finch fore-brain. This shows that this is an effective metric for these spike trains suggesting that in these spike trains the significance of a spike is modulated by its proximity to previous spikes. This modulation is a putative information-coding property of spike trains.

  17. Spike-Based Population Coding and Working Memory

    PubMed Central

    Boerlin, Martin; Denève, Sophie

    2011-01-01

    Compelling behavioral evidence suggests that humans can make optimal decisions despite the uncertainty inherent in perceptual or motor tasks. A key question in neuroscience is how populations of spiking neurons can implement such probabilistic computations. In this article, we develop a comprehensive framework for optimal, spike-based sensory integration and working memory in a dynamic environment. We propose that probability distributions are inferred spike-per-spike in recurrently connected networks of integrate-and-fire neurons. As a result, these networks can combine sensory cues optimally, track the state of a time-varying stimulus and memorize accumulated evidence over periods much longer than the time constant of single neurons. Importantly, we propose that population responses and persistent working memory states represent entire probability distributions and not only single stimulus values. These memories are reflected by sustained, asynchronous patterns of activity which make relevant information available to downstream neurons within their short time window of integration. Model neurons act as predictive encoders, only firing spikes which account for new information that has not yet been signaled. Thus, spike times signal deterministically a prediction error, contrary to rate codes in which spike times are considered to be random samples of an underlying firing rate. As a consequence of this coding scheme, a multitude of spike patterns can reliably encode the same information. This results in weakly correlated, Poisson-like spike trains that are sensitive to initial conditions but robust to even high levels of external neural noise. This spike train variability reproduces the one observed in cortical sensory spike trains, but cannot be equated to noise. On the contrary, it is a consequence of optimal spike-based inference. In contrast, we show that rate-based models perform poorly when implemented with stochastically spiking neurons. PMID:21379319

  18. Test Statistics for the Identification of Assembly Neurons in Parallel Spike Trains

    PubMed Central

    Picado Muiño, David; Borgelt, Christian

    2015-01-01

    In recent years numerous improvements have been made in multiple-electrode recordings (i.e., parallel spike-train recordings) and spike sorting to the extent that nowadays it is possible to monitor the activity of up to hundreds of neurons simultaneously. Due to these improvements it is now potentially possible to identify assembly activity (roughly understood as significant synchronous spiking of a group of neurons) from these recordings, which—if it can be demonstrated reliably—would significantly improve our understanding of neural activity and neural coding. However, several methodological problems remain when trying to do so and, among them, a principal one is the combinatorial explosion that one faces when considering all potential neuronal assemblies, since in principle every subset of the recorded neurons constitutes a candidate set for an assembly. We present several statistical tests to identify assembly neurons (i.e., neurons that participate in a neuronal assembly) from parallel spike trains with the aim of reducing the set of neurons to a relevant subset of them and this way ease the task of identifying neuronal assemblies in further analyses. These tests are an improvement of those introduced in the work by Berger et al. (2010) based on additional features like spike weight or pairwise overlap and on alternative ways to identify spike coincidences (e.g., by avoiding time binning, which tends to lose information). PMID:25866503

  19. Macroscopic Description for Networks of Spiking Neurons

    NASA Astrophysics Data System (ADS)

    Montbrió, Ernest; Pazó, Diego; Roxin, Alex

    2015-04-01

    A major goal of neuroscience, statistical physics, and nonlinear dynamics is to understand how brain function arises from the collective dynamics of networks of spiking neurons. This challenge has been chiefly addressed through large-scale numerical simulations. Alternatively, researchers have formulated mean-field theories to gain insight into macroscopic states of large neuronal networks in terms of the collective firing activity of the neurons, or the firing rate. However, these theories have not succeeded in establishing an exact correspondence between the firing rate of the network and the underlying microscopic state of the spiking neurons. This has largely constrained the range of applicability of such macroscopic descriptions, particularly when trying to describe neuronal synchronization. Here, we provide the derivation of a set of exact macroscopic equations for a network of spiking neurons. Our results reveal that the spike generation mechanism of individual neurons introduces an effective coupling between two biophysically relevant macroscopic quantities, the firing rate and the mean membrane potential, which together govern the evolution of the neuronal network. The resulting equations exactly describe all possible macroscopic dynamical states of the network, including states of synchronous spiking activity. Finally, we show that the firing-rate description is related, via a conformal map, to a low-dimensional description in terms of the Kuramoto order parameter, called Ott-Antonsen theory. We anticipate that our results will be an important tool in investigating how large networks of spiking neurons self-organize in time to process and encode information in the brain.

  20. Shaping and timing gradient pulses to reduce MRI acoustic noise.

    PubMed

    Segbers, Marcel; Rizzo Sierra, Carlos V; Duifhuis, Hendrikus; Hoogduin, Johannes M

    2010-08-01

    A method to reduce the acoustic noise generated by gradient systems in MRI has been recently proposed; such a method is based on the linear response theory. Since the physical cause of MRI acoustic noise is the time derivative of the gradient current, a common trapezoid current shape produces an acoustic gradient coil response mainly during the rising and falling edge. In the falling edge, the coil acoustic response presents a 180 degrees phase difference compared to the rising edge. Therefore, by varying the width of the trapezoid and keeping the ramps constant, it is possible to suppress one selected frequency and its higher harmonics. This value is matched to one of the prominent resonance frequencies of the gradient coil system. The idea of cancelling a single frequency is extended to a second frequency, using two successive trapezoid-shaped pulses presented at a selected interval. Overall sound pressure level reduction of 6 and 10 dB is found for the two trapezoid shapes and a single pulse shape, respectively. The acoustically optimized pulse shape proposed is additionally tested in a simulated echo planar imaging readout train, obtaining a sound pressure level reduction of 12 dB for the best case.

  1. Effects of surfactants on extraction of phenanthrene in spiked sand.

    PubMed

    Chang, M C; Huang, C R; Shu, H Y

    2000-10-01

    Problems associated with polynuclear aromatic hydrocarbon (PAH) contaminated site in environmental media have received increasing attention. To resolve such problems, innovative in situ methods are urgently required. This work investigated the feasibility of using surfactants to extract phenanthrene on spiked sand in a batch system. Phenanthrene was spiked into Ottawa sand to simulate contaminated soil. Six surfactants, Brij 30 (BR), Triton X-100 (TR), Tergitol NP-10 (TE), Igepal CA-720 (IG), sodium dodecyl sulfate (SDS) and hexadecyl trimethyl ammonium bromide (HTAB) were used. Adjusting the extraction time, mixing speed and surfactant concentration yielded the optimum extracting conditions. The concentration of phenanthrene was identified with HPLC. Under the experimental conditions, results indicated that those surfactants were highly promising on site remediation since the residual phenanthrene concentration was effectively reduced. The optimum operating conditions were obtained at 30 min, 125 rpm and surfactant concentrations in 4%.

  2. Simultaneous recording of brain extracellular glucose, spike and local field potential in real time using an implantable microelectrode array with nano-materials

    NASA Astrophysics Data System (ADS)

    Wei, Wenjing; Song, Yilin; Fan, Xinyi; Zhang, Song; Wang, Li; Xu, Shengwei; Wang, Mixia; Cai, Xinxia

    2016-03-01

    Glucose is the main substrate for neurons in the central nervous system. In order to efficiently characterize the brain glucose mechanism, it is desirable to determine the extracellular glucose dynamics as well as the corresponding neuroelectrical activity in vivo. In the present study, we fabricated an implantable microelectrode array (MEA) probe composed of platinum electrochemical and electrophysiology microelectrodes by standard micro electromechanical system (MEMS) processes. The MEA probe was modified with nano-materials and implanted in a urethane-anesthetized rat for simultaneous recording of striatal extracellular glucose, local field potential (LFP) and spike on the same spatiotemporal scale when the rat was in normoglycemia, hypoglycemia and hyperglycemia. During these dual-mode recordings, we observed that increase of extracellular glucose enhanced the LFP power and spike firing rate, while decrease of glucose had an opposite effect. This dual mode MEA probe is capable of examining specific spatiotemporal relationships between electrical and chemical signaling in the brain, which will contribute significantly to improve our understanding of the neuron physiology.

  3. Spatio-Temporal Tolerance of Visuo-Tactile Illusions in Artificial Skin by Recurrent Neural Network with Spike-Timing-Dependent Plasticity

    PubMed Central

    Pitti, Alexandre; Pugach, Ganna; Gaussier, Philippe; Shimada, Sotaro

    2017-01-01

    Perceptual illusions across multiple modalities, such as the rubber-hand illusion, show how dynamic the brain is at adapting its body image and at determining what is part of it (the self) and what is not (others). Several research studies showed that redundancy and contingency among sensory signals are essential for perception of the illusion and that a lag of 200–300 ms is the critical limit of the brain to represent one’s own body. In an experimental setup with an artificial skin, we replicate the visuo-tactile illusion within artificial neural networks. Our model is composed of an associative map and a recurrent map of spiking neurons that learn to predict the contingent activity across the visuo-tactile signals. Depending on the temporal delay incidentally added between the visuo-tactile signals or the spatial distance of two distinct stimuli, the two maps detect contingency differently. Spiking neurons organized into complex networks and synchrony detection at different temporal interval can well explain multisensory integration regarding self-body. PMID:28106139

  4. Generalized activity equations for spiking neural network dynamics

    PubMed Central

    Buice, Michael A.; Chow, Carson C.

    2013-01-01

    Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales—the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances. PMID:24298252

  5. Mikkelson sweep/spike chisel plow shovel

    SciTech Connect

    Not Available

    1992-01-01

    Profitability comparisons are reported between the Mikkelson Sweep/Spike Chisel Plow Shovel standard sweeps. This evaluation covers the first year of testing of the new Sweep/Spike design. The data are not averaged over treatments due to significant interaction between treatments and environmental factors. The cost of fuel, fall and spring, to perform the various treatments ranged from $1.27 to $3.36 per acre. Use of the sweep/spike shovel always reduced total fuel cost. Savings varied from $0.11 to $0.71 per acre depending on prior treatment. This means there will be money saved, to off-set expenses, when converting present chisel plows or for special options on new chisel plows, needed for use of the sweep/spike shovel. A summary of 1991--1992 energy measurements. They indicate that more power will be required to pull a chisel plow equipped with the sweep/spike shovel. A larger tractor, narrower chisel plow and/or slower speed will be required to avoid the wheel slippage problems encountered on soft or wet field surfaces.

  6. Multimodal imaging of spike propagation: a technical case report.

    PubMed

    Tanaka, N; Grant, P E; Suzuki, N; Madsen, J R; Bergin, A M; Hämäläinen, M S; Stufflebeam, S M

    2012-06-01

    We report an 11-year-old boy with intractable epilepsy, who had cortical dysplasia in the right superior frontal gyrus. Spatiotemporal source analysis of MEG and EEG spikes demonstrated a similar time course of spike propagation from the superior to inferior frontal gyri, as observed on intracranial EEG. The tractography reconstructed from DTI showed a fiber connection between these areas. Our multimodal approach demonstrates spike propagation and a white matter tract guiding the propagation.

  7. Multimodal Imaging of Spike Propagation: A Technical Case Report

    PubMed Central

    Tanaka, N.; Grant, P.E.; Suzuki, N.; Madsen, J.R.; Bergin, A.M.; Hämäläinen, M.S; Stufflebeam, S.M.

    2012-01-01

    SUMMARY We report an 11-year-old boy with intractable epilepsy, who had cortical dysplasia in the right superior frontal gyrus. Spatiotemporal source analysis of MEG and EEG spikes demonstrated a similar time course of spike propagation from the superior to inferior frontal gyri, as observed on intracranial EEG. The tractography reconstructed from DTI showed a fiber connection between these areas. Our multimodal approach demonstrates spike propagation and a white matter tract guiding the propagation. PMID:21960488

  8. The Role of Spike Temporal Latencies in Artificial Olfaction

    NASA Astrophysics Data System (ADS)

    Polese, D.; Martinelli, E.; Dini, F.; Paolesse, R.; Filippini, D.; Lundström, I.; Di Natale, C.

    2011-09-01

    In this paper we investigate the recognition power of spike time latencies in an artificial olfactory system. For the scope we used a recently introduced platform for artificial olfaction implementing an artificial olfactory epithelium, formed by thousands sensors, and an abstract olfactory bulb1. Results show that correct volatile compounds classification can be achieved considering only the first two spikes of the neural network output evidencing that the latency of the first spikes contains actually enough information for odor identification.

  9. [Quantitative evaluation of inhibitory effects of epileptic spikes on theta rhythms in the network of hippocampal CA3 and entorhinal cortex in patients with temporal lobe epilepsy].

    PubMed

    Ge, Man-Ling; Guo, Jun-Dan; Chen, Sheng-Hua; Zhang, Ji-Chang; Fu, Xiao-Xuan; Chen, Yu-Min

    2017-02-25

    Epileptic spike is an indicator of hyper-excitability and hyper-synchrony in the neural networks. The inhibitory effects of spikes on theta rhythms (4-8 Hz) might be helpful to understand the mechanism of epileptic damage on the cognitive functions. To quantitatively evaluate the inhibitory effects of spikes on theta rhythms, intracerebral electroencephalogram (EEG) recordings with both sporadic spikes (SSs) and spike-free transient period between adjacent spikes were selected in 4 patients in the status of rapid eyes movement (REM) sleep with temporal lobe epilepsy (TLE) under the pre-surgical monitoring. The electrodes of hippocampal CA3 and entorhinal cortex (EC) were employed, since CA3 and EC built up one of key loops to investigate cognition and epilepsy. These SSs occurred only in CA3, only in EC, or in both CA3 and EC synchronously. Theta power was respectively estimated around SSs and during the spike-free transient period by Gabor wavelet transform and Hilbert transform. The intermittent extent was then estimated to represent for the loss of theta rhythms during the spike-free transient period. The following findings were obtained: (1) The prominent rhythms were in theta frequency band; (2) The spikes could transiently reduce theta power, and the inhibitory effect was severer around SSs in both CA3 and EC synchronously than that around either SSs only in EC or SSs only in CA3; (3) During the spike-free transient period, theta rhythms were interrupted with the intermittent theta rhythms left and theta power level continued dropping, implying the inhibitory effect was sustained. Additionally, the intermittent extent of theta rhythms was converged to the inhibitory extent around SSs; (4) The average theta power level during the spike-free transient period might not be in line with the inhibitory extent of theta rhythms around SSs. It was concluded that the SSs had negative effects on theta rhythms transiently and directly, the inhibitory effects aroused by

  10. 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

  11. Lyondell outage spikes prices

    SciTech Connect

    1996-08-07

    Methanol spot markets in the US Gulf Coast cooled a bit late last week from their Monday spike in the wake of a pipeline rupture and fire that shut down Lyondell Petrochemical`s Channelview, TX complex and its 248-million gal/year methanol plant. The unit resumed production last week and was expected to return to full service by August 3. Offering prices shot up at least 10% over the pre-accident level of about 50 cts/gal fob. No actual business could be confirmed at a price of more than 52 cts-53 cts/gal, however.

  12. Spike Code Flow in Cultured Neuronal Networks.

    PubMed

    Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei

    2016-01-01

    We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.

  13. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons

    PubMed Central

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply—unlike processors in our current generation of computer hardware—an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785

  14. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.

    PubMed

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling.

  15. Radioxenon spiked air

    DOE PAGES

    Watrous, Matthew G.; Delmore, James E.; Hague, Robert K.; ...

    2015-08-27

    Four of the radioactive xenon isotopes (131mXe, 133mXe, 133Xe and 135Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The Internationalmore » Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This study focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities.« less

  16. The time course of natural scene perception with reduced attention.

    PubMed

    Groen, Iris I A; Ghebreab, Sennay; Lamme, Victor A F; Scholte, H Steven

    2016-02-01

    Attention is thought to impose an informational bottleneck on vision by selecting particular information from visual scenes for enhanced processing. Behavioral evidence suggests, however, that some scene information is extracted even when attention is directed elsewhere. Here, we investigated the neural correlates of this ability by examining how attention affects electrophysiological markers of scene perception. In two electro-encephalography (EEG) experiments, human subjects categorized real-world scenes as manmade or natural (full attention condition) or performed tasks on unrelated stimuli in the center or periphery of the scenes (reduced attention conditions). Scene processing was examined in two ways: traditional trial averaging was used to assess the presence of a categorical manmade/natural distinction in event-related potentials, whereas single-trial analyses assessed whether EEG activity was modulated by scene statistics that are diagnostic of naturalness of individual scenes. The results indicated that evoked activity up to 250 ms was unaffected by reduced attention, showing intact categorical differences between manmade and natural scenes and strong modulations of single-trial activity by scene statistics in all conditions. Thus initial processing of both categorical and individual scene information remained intact with reduced attention. Importantly, however, attention did have profound effects on later evoked activity; full attention on the scene resulted in prolonged manmade/natural differences, increased neural sensitivity to scene statistics, and enhanced scene memory. These results show that initial processing of real-world scene information is intact with diminished attention but that the depth of processing of this information does depend on attention.

  17. Nonsmooth dynamics in spiking neuron models

    NASA Astrophysics Data System (ADS)

    Coombes, S.; Thul, R.; Wedgwood, K. C. A.

    2012-11-01

    Large scale studies of spiking neural networks are a key part of modern approaches to understanding the dynamics of biological neural tissue. One approach in computational neuroscience has been to consider the detailed electrophysiological properties of neurons and build vast computational compartmental models. An alternative has been to develop minimal models of spiking neurons with a reduction in the dimensionality of both parameter and variable space that facilitates more effective simulation studies. In this latter case the single neuron model of choice is often a variant of the classic integrate-and-fire model, which is described by a nonsmooth dynamical system. In this paper we review some of the more popular spiking models of this class and describe the types of spiking pattern that they can generate (ranging from tonic to burst firing). We show that a number of techniques originally developed for the study of impact oscillators are directly relevant to their analysis, particularly those for treating grazing bifurcations. Importantly we highlight one particular single neuron model, capable of generating realistic spike trains, that is both computationally cheap and analytically tractable. This is a planar nonlinear integrate-and-fire model with a piecewise linear vector field and a state dependent reset upon spiking. We call this the PWL-IF model and analyse it at both the single neuron and network level. The techniques and terminology of nonsmooth dynamical systems are used to flesh out the bifurcation structure of the single neuron model, as well as to develop the notion of Lyapunov exponents. We also show how to construct the phase response curve for this system, emphasising that techniques in mathematical neuroscience may also translate back to the field of nonsmooth dynamical systems. The stability of periodic spiking orbits is assessed using a linear stability analysis of spiking times. At the network level we consider linear coupling between voltage

  18. Increased commuting to school time reduces sleep duration in adolescents.

    PubMed

    Pereira, Erico Felden; Moreno, Claudia; Louzada, Fernando Mazzilli

    2014-02-01

    Active travel to school has been referred to as one way of increasing the level of daily physical exercise, but the actual impacts on student's general health are not clear. Recently, a possible association between active travel to school and the duration of sleep was suggested. Thus, the aim was of this study to investigate the associations between the type of transportation and travel time to school, the time in bed and sleepiness in the classroom of high school students. Information on sleeping habits and travel to school of 1126 high school students were analyzed, where 55.1% were girls with an average age of 16.24 (1.39) years old, in Santa Maria Municipality, Rio Grande do Sul, Brazil. Multiple linear regression and adjusted prevalence rates analyses were carried out. The frequency of active travel found was 61.8%. Associations between time in bed, sleepiness in the classroom and the type of transportation (active or passive) were not identified. Nevertheless, the time in bed was inversely associated with the travel time (p = 0.036) and with a phase delay. In the adjusted analysis, active travel was more incident for the students of schools in the suburbs (PR: 1.68; CI: 1.40-2.01) in comparison with the students of schools in the center. Therefore, longer trips were associated with a reduction of sleep duration of morning and night groups. Interventions concerning active travel to school must be carried out cautiously in order not to cause a reduction of the sleeping time.

  19. The Structure and Precision of Retinal Spike Trains

    NASA Astrophysics Data System (ADS)

    Berry, Michael J.; Warland, David K.; Meister, Markus

    1997-05-01

    Assessing the reliability of neuronal spike trains is fundamental to an understanding of the neural code. We measured the reproducibility of retinal responses to repeated visual stimuli. In both tiger salamander and rabbit, the retinal ganglion cells responded to random flicker with discrete, brief periods of firing. For any given cell, these firing events covered only a small fraction of the total stimulus time, often less than 5%. Firing events were very reproducible from trial to trial: the timing jitter of individual spikes was as low as 1 msec, and the standard deviation in spike count was often less than 0.5 spikes. Comparing the precision of spike timing to that of the spike count showed that the timing of a firing event conveyed several times more visual information than its spike count. This sparseness and precision were general characteristics of ganglion cell responses, maintained over the broad ensemble of stimulus waveforms produced by random flicker, and over a range of contrasts. Thus, the responses of retinal ganglion cells are not properly described by a firing probability that varies continuously with the stimulus. Instead, these neurons elicit discrete firing events that may be the fundamental coding symbols in retinal spike trains.

  20. Filter based phase distortions in extracellular spikes

    PubMed Central

    Yael, Dorin

    2017-01-01

    Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results. PMID:28358895

  1. On the simulation of nonlinear bidimensional spiking neuron models.

    PubMed

    Touboul, Jonathan

    2011-07-01

    Bidimensional spiking models are garnering a lot of attention for their simplicity and their ability to reproduce various spiking patterns of cortical neurons and are used particularly for large network simulations. These models describe the dynamics of the membrane potential by a nonlinear differential equation that blows up in finite time, coupled to a second equation for adaptation. Spikes are emitted when the membrane potential blows up or reaches a cutoff θ. The precise simulation of the spike times and of the adaptation variable is critical, for it governs the spike pattern produced and is hard to compute accurately because of the exploding nature of the system at the spike times. We thoroughly study the precision of fixed time-step integration schemes for this type of model and demonstrate that these methods produce systematic errors that are unbounded, as the cutoff value is increased, in the evaluation of the two crucial quantities: the spike time and the value of the adaptation variable at this time. Precise evaluation of these quantities therefore involves very small time steps and long simulation times. In order to achieve a fixed absolute precision in a reasonable computational time, we propose here a new algorithm to simulate these systems based on a variable integration step method that either integrates the original ordinary differential equation or the equation of the orbits in the phase plane, and compare this algorithm with fixed time-step Euler scheme and other more accurate simulation algorithms.

  2. Kv7 channels regulate pairwise spiking covariability in health and disease.

    PubMed

    Ocker, Gabriel Koch; Doiron, Brent

    2014-07-15

    Low-threshold M currents are mediated by the Kv7 family of potassium channels. Kv7 channels are important regulators of spiking activity, having a direct influence on the firing rate, spike time variability, and filter properties of neurons. How Kv7 channels affect the joint spiking activity of populations of neurons is an important and open area of study. Using a combination of computational simulations and analytic calculations, we show that the activation of Kv7 conductances reduces the covariability between spike trains of pairs of neurons driven by common inputs. This reduction is beyond that explained by the lowering of firing rates and involves an active cancellation of common fluctuations in the membrane potentials of the cell pair. Our theory shows that the excess covariance reduction is due to a Kv7-induced shift from low-pass to band-pass filtering of the single neuron spike train response. Dysfunction of Kv7 conductances is related to a number of neurological diseases characterized by both elevated firing rates and increased network-wide correlations. We show how changes in the activation or strength of Kv7 conductances give rise to excess correlations that cannot be compensated for by synaptic scaling or homeostatic modulation of passive membrane properties. In contrast, modulation of Kv7 activation parameters consistent with pharmacological treatments for certain hyperactivity disorders can restore normal firing rates and spiking correlations. Our results provide key insights into how regulation of a ubiquitous potassium channel class can control the coordination of population spiking activity.

  3. Reducing door to needle time for stroke thrombolysis.

    PubMed

    Gill, Sumanjit

    2014-01-01

    Better outcomes are obtained with stroke thrombolysis the more rapidly it is given, both in terms of the patient's level of functional ability and also mortality. Current UK performance targets (outside London) aim for a time of 45 minutes or less. Thrombolysis pathways involve multidisciplinary working across departmental boundaries as well as senior level decision making. Our system used telemedicine out of hours adding additional complexity to the pathway. The initial planning stages started by auditing current practice and mapping the existing pathway. The figures for door to needle times were held on a database on the stroke unit and collected in detail for the purposes of national reporting. The pathway was mapped by combining personal experience of working within the stroke service with the experiences of the general medical registrars who worked the system out of hours. The initial action was to present this information throughout the hospital at departmental meetings. Opinions were canvassed at these meetings on where the biggest barriers were within the pathway and how we could address them. An awareness campaign was held by advertising over the intranet. An intervention comprising the following elements was introduced over the period of a year: introduction of an ambulance pre-alert, revision of the existing pathway, and education to all those involved in thrombolysis. The cases where particularly long delays were noted were audited in more depth to identify barriers to flow through the system. This was reported in ward meetings for staff to contribute experience and to offer solutions. We went to commissioning group meetings to gain the support of the local ambulance service, and talked to A&E seniors about the project and the ways in which they could help. Median times were calculated from a stroke database. There was a fall in median door to needle time of 65.5 to 49 minutes over a period of 18 months. A complex intervention to improve door to needle

  4. Oxytocin enhances hippocampal spike transmission by modulating fast-spiking interneurons.

    PubMed

    Owen, Scott F; Tuncdemir, Sebnem N; Bader, Patrick L; Tirko, Natasha N; Fishell, Gord; Tsien, Richard W

    2013-08-22

    Neuromodulatory control by oxytocin is essential to a wide range of social, parental and stress-related behaviours. Autism spectrum disorders (ASD) are associated with deficiencies in oxytocin levels and with genetic alterations of the oxytocin receptor (OXTR). Thirty years ago, Mühlethaler et al. found that oxytocin increases the firing of inhibitory hippocampal neurons, but it remains unclear how elevated inhibition could account for the ability of oxytocin to improve information processing in the brain. Here we describe in mammalian hippocampus a simple yet powerful mechanism by which oxytocin enhances cortical information transfer while simultaneously lowering background activity, thus greatly improving the signal-to-noise ratio. Increased fast-spiking interneuron activity not only suppresses spontaneous pyramidal cell firing, but also enhances the fidelity of spike transmission and sharpens spike timing. Use-dependent depression at the fast-spiking interneuron-pyramidal cell synapse is both necessary and sufficient for the enhanced spike throughput. We show the generality of this novel circuit mechanism by activation of fast-spiking interneurons with cholecystokinin or channelrhodopsin-2. This provides insight into how a diffusely delivered neuromodulator can improve the performance of neural circuitry that requires synapse specificity and millisecond precision.

  5. Performing four basic arithmetic operations with spiking neural P systems.

    PubMed

    Zeng, Xiangxiang; Song, Tao; Zhang, Xingyi; Pan, Linqiang

    2012-12-01

    Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bio-inspired computing devices-spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information is encoded as the time interval between spikes). In this work, four SN P systems are constructed as adder, subtracter, multiplier, and divider, respectively. In these systems, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes.

  6. Finding the event structure of neuronal spike trains.

    PubMed

    Toups, J Vincent; Fellous, Jean-Marc; Thomas, Peter J; Sejnowski, Terrence J; Tiesinga, Paul H

    2011-09-01

    Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004 ). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.

  7. A robust and biologically plausible spike pattern recognition network.

    PubMed

    Larson, Eric; Perrone, Ben P; Sen, Kamal; Billimoria, Cyrus P

    2010-11-17

    The neural mechanisms that enable recognition of spiking patterns in the brain are currently unknown. This is especially relevant in sensory systems, in which the brain has to detect such patterns and recognize relevant stimuli by processing peripheral inputs; in particular, it is unclear how sensory systems can recognize time-varying stimuli by processing spiking activity. Because auditory stimuli are represented by time-varying fluctuations in frequency content, it is useful to consider how such stimuli can be recognized by neural processing. Previous models for sound recognition have used preprocessed or low-level auditory signals as input, but complex natural sounds such as speech are thought to be processed in auditory cortex, and brain regions involved in object recognition in general must deal with the natural variability present in spike trains. Thus, we used neural recordings to investigate how a spike pattern recognition system could deal with the intrinsic variability and diverse response properties of cortical spike trains. We propose a biologically plausible computational spike pattern recognition model that uses an excitatory chain of neurons to spatially preserve the temporal representation of the spike pattern. Using a single neural recording as input, the model can be trained using a spike-timing-dependent plasticity-based learning rule to recognize neural responses to 20 different bird songs with >98% accuracy and can be stimulated to evoke reverse spike pattern playback. Although we test spike train recognition performance in an auditory task, this model can be applied to recognize sufficiently reliable spike patterns from any neuronal system.

  8. Spike sorting for polytrodes: a divide and conquer approach

    PubMed Central

    Swindale, Nicholas V.; Spacek, Martin A.

    2014-01-01

    In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted) with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be recorded on several adjacent sites. Although this offers a better chance of distinguishing neurons with similarly shaped spikes, sorting is difficult in such cases because of the high dimensionality of the space in which the signals must be classified. This report details a method for spike sorting based on a divide and conquer approach. Clusters are initially formed by assigning each event to the channel on which it is largest. Each channel-based cluster is then sub-divided into as many distinct clusters as possible. These are then recombined on the basis of pairwise tests into a final set of clusters. Pairwise tests are also performed to establish how distinct each cluster is from the others. A modified gradient ascent clustering (GAC) algorithm is used to do the clustering. The method can sort spikes with minimal user input in times comparable to real time for recordings lasting up to 45 min. Our results illustrate some of the difficulties inherent in spike sorting, including changes in spike shape over time. We show that some physiologically distinct units may have very similar spike shapes. We show that RMS measures of spike shape similarity are not sensitive enough to discriminate clusters that can otherwise be separated by principal components analysis (PCA). Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scalable to larger multi-electrode arrays (MEAs). PMID:24574979

  9. Radioxenon spiked air

    SciTech Connect

    Watrous, Matthew G.; Delmore, James E.; Hague, Robert K.; Houghton, Tracy P.; Jenson, Douglas D.; Mann, Nick R.

    2015-08-27

    Four of the radioactive xenon isotopes (131mXe, 133mXe, 133Xe and 135Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The International Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This study focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities.

  10. A new class of metrics for spike trains.

    PubMed

    Rusu, Cătălin V; Florian, Răzvan V

    2014-02-01

    The distance between a pair of spike trains, quantifying the differences between them, can be measured using various metrics. Here we introduce a new class of spike train metrics, inspired by the Pompeiu-Hausdorff distance, and compare them with existing metrics. Some of our new metrics (the modulus-metric and the max-metric) have characteristics that are qualitatively different from those of classical metrics like the van Rossum distance or the Victor and Purpura distance. The modulus-metric and the max-metric are particularly suitable for measuring distances between spike trains where information is encoded in bursts, but the number and the timing of spikes inside a burst do not carry information. The modulus-metric does not depend on any parameters and can be computed using a fast algorithm whose time depends linearly on the number of spikes in the two spike trains. We also introduce localized versions of the new metrics, which could have the biologically relevant interpretation of measuring the differences between spike trains as they are perceived at a particular moment in time by a neuron receiving these spike trains.

  11. 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

  12. 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

  13. Separation of synaptic and spike activity in intracellular recordings for selective analysis.

    PubMed

    Hedwig, B; Knepper, M

    1992-04-01

    A software spike filter has been developed which allows the separation of synaptic activity and action potentials in intracellular recordings. The algorithm uses the different velocities of the membrane potential during synaptic and spike activity and a time window to identify action potentials. When spikes are recognized, they are removed and the membrane potential is substituted by interpolated values. The spike filter makes possible a separate quantitative evaluation of postsynaptic potentials and spike activity. Thus a comprehensive characterization of neuron activity can be obtained. The spike filter is part of a modular software package designed for the evaluation of neurobiological data.

  14. From spiking neuron models to linear-nonlinear models.

    PubMed

    Ostojic, Srdjan; Brunel, Nicolas

    2011-01-20

    Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

  15. A unified description of cerebellar inter-spike interval distributions and variabilities using summation of Gaussians.

    PubMed

    Chen, Yanqing; Nitz, Douglas A

    2011-01-01

    Neuronal inter-spike intervals (ISIs) have previously been described as Poisson, Gamma, inverse Gaussian or other unimodal distributions. We analyzed ISIs of rhythmic and arrhythmic neuronal spike trains in cerebellum recorded from freely behaving rats, and found that their distributions can be described as the summation or integration of multiple Gaussian distributions. The ISIs of rhythmic cerebellar Purkinje cells have a main Gaussian peak at a basic firing interval and exponentially reduced peaks at multiples of this firing period. ISIs of arrhythmic Purkinje cells can be modeled as the integration of multiple Gaussian distributions centered at continuous intervals with exponentially reduced peak amplitudes. The sources of variability are directly related to the relative timing of action potentials between neighboring cells since we show that irregularities of discharge in one cell are associated with the previous history of its discharge in time relative to another cell. Through relative phase analyses, we demonstrate that the shape and the mathematical form of the ISI distributions in cerebellum are direct result of dynamic interactions in the nearby neuronal network, in addition to intrinsic firing properties. The analysis in this paper provides a unified description of cerebellar inter-spike interval distributions which deviate from the usual Poisson assumptions. Our results suggest the existence of an intrinsic rhythmicity in cells exhibiting arrhythmic spike trains in cerebellum, and may identify an important source of variability in neuronal firing patterns that is relevant to the mechanism of neural computation in cerebellum.

  16. Causal Inference and Explaining Away in a Spiking Network

    PubMed Central

    Moreno-Bote, Rubén; Drugowitsch, Jan

    2015-01-01

    While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

  17. 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.

  18. 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.

  19. 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

  20. Mapping Spikes to Sensations

    PubMed Central

    Stüttgen, Maik C.; Schwarz, Cornelius; Jäkel, Frank

    2011-01-01

    Single-unit recordings conducted during perceptual decision-making tasks have yielded tremendous insights into the neural coding of sensory stimuli. In such experiments, detection or discrimination behavior (the psychometric data) is observed in parallel with spike trains in sensory neurons (the neurometric data). Frequently, candidate neural codes for information read-out are pitted against each other by transforming the neurometric data in some way and asking which code’s performance most closely approximates the psychometric performance. The code that matches the psychometric performance best is retained as a viable candidate and the others are rejected. In following this strategy, psychometric data is often considered to provide an unbiased measure of perceptual sensitivity. It is rarely acknowledged that psychometric data result from a complex interplay of sensory and non-sensory processes and that neglect of these processes may result in misestimating psychophysical sensitivity. This again may lead to erroneous conclusions regarding the adequacy of candidate neural codes. In this review, we first discuss requirements on the neural data for a subsequent neurometric-psychometric comparison. We then focus on different psychophysical tasks for the assessment of detection and discrimination performance and the cognitive processes that may underlie their execution. We discuss further factors that may compromise psychometric performance and how they can be detected or avoided. We believe that these considerations point to shortcomings in our understanding of the processes underlying perceptual decisions, and therefore offer potential for future research. PMID:22084627

  1. Dendritic Spikes in Sensory Perception

    PubMed Central

    Manita, Satoshi; Miyakawa, Hiroyoshi; Kitamura, Kazuo; Murayama, Masanori

    2017-01-01

    What is the function of dendritic spikes? One might argue that they provide conditions for neuronal plasticity or that they are essential for neural computation. However, despite a long history of dendritic research, the physiological relevance of dendritic spikes in brain function remains unknown. This could stem from the fact that most studies on dendrites have been performed in vitro. Fortunately, the emergence of novel techniques such as improved two-photon microscopy, genetically encoded calcium indicators (GECIs), and optogenetic tools has provided the means for vital breakthroughs in in vivo dendritic research. These technologies enable the investigation of the functions of dendritic spikes in behaving animals, and thus, help uncover the causal relationship between dendritic spikes, and sensory information processing and synaptic plasticity. Understanding the roles of dendritic spikes in brain function would provide mechanistic insight into the relationship between the brain and the mind. In this review article, we summarize the results of studies on dendritic spikes from a historical perspective and discuss the recent advances in our understanding of the role of dendritic spikes in sensory perception. PMID:28261060

  2. Perineuronal Nets Enhance the Excitability of Fast-Spiking Neurons

    PubMed Central

    2016-01-01

    Perineuronal nets (PNNs) are specialized complexes of extracellular matrix molecules that surround the somata of fast-spiking neurons throughout the vertebrate brain. PNNs are particularly prevalent throughout the auditory brainstem, which transmits signals with high speed and precision. It is unknown whether PNNs contribute to the fast-spiking ability of the neurons they surround. Whole-cell recordings were made from medial nucleus of the trapezoid body (MNTB) principal neurons in acute brain slices from postnatal day 21 (P21) to P27 mice. PNNs were degraded by incubating slices in chondroitinase ABC (ChABC) and were compared to slices that were treated with a control enzyme (penicillinase). ChABC treatment did not affect the ability of MNTB neurons to fire at up to 1000 Hz when driven by current pulses. However, f–I (frequency–intensity) curves constructed by injecting Gaussian white noise currents superimposed on DC current steps showed that ChABC treatment reduced the gain of spike output. An increase in spike threshold may have contributed to this effect, which is consistent with the observation that spikes in ChABC-treated cells were delayed relative to control-treated cells. In addition, parvalbumin-expressing fast-spiking cortical neurons in >P70 slices that were treated with ChABC also had reduced excitability and gain. The development of PNNs around somata of fast-spiking neurons may be essential for fast and precise sensory transmission and synaptic inhibition in the brain. PMID:27570824

  3. A neural network model of reliably optimized spike transmission.

    PubMed

    Samura, Toshikazu; Ikegaya, Yuji; Sato, Yasuomi D

    2015-06-01

    We studied the detailed structure of a neuronal network model in which the spontaneous spike activity is correctly optimized to match the experimental data and discuss the reliability of the optimized spike transmission. Two stochastic properties of the spontaneous activity were calculated: the spike-count rate and synchrony size. The synchrony size, expected to be an important factor for optimization of spike transmission in the network, represents a percentage of observed coactive neurons within a time bin, whose probability approximately follows a power-law. We systematically investigated how these stochastic properties could matched to those calculated from the experimental data in terms of the log-normally distributed synaptic weights between excitatory and inhibitory neurons and synaptic background activity induced by the input current noise in the network model. To ensure reliably optimized spike transmission, the synchrony size as well as spike-count rate were simultaneously optimized. This required changeably balanced log-normal distributions of synaptic weights between excitatory and inhibitory neurons and appropriately amplified synaptic background activity. Our results suggested that the inhibitory neurons with a hub-like structure driven by intensive feedback from excitatory neurons were a key factor in the simultaneous optimization of the spike-count rate and synchrony size, regardless of different spiking types between excitatory and inhibitory neurons.

  4. Spikes removal in surface measurement

    NASA Astrophysics Data System (ADS)

    Podulka, P.; Pawlus, P.; Dobrzański, P.; Lenart, A.

    2014-03-01

    Several cylinder surface topographies made from grey cast iron were measured by Talysurf CCI white light interferometer with and without use of spikes filter. They were plateau honed by abrasive stones. Measured area was 3.3 mm × 3.3 mm, height resolution was 0.01 nm. The forms were eliminated using polynomial of the 3rd degree. After it, spikes were removed using four methods. These approaches were compared. Parameters of the smaller and highest sensitivity on spikes presence were selected.

  5. Spike synchronization of chaotic oscillators as a phase transition.

    PubMed

    Ciszak, M; Montina, A; Arecchi, F T

    2009-02-01

    We study how a locally coupled array of spiking chaotic systems synchronizes to an external driving in a short time. Synchronization means spike separation at adjacent sites much shorter than the average inter-spike interval; a local lack of synchronization is called a defect. The system displays sudden spontaneous defect disappearance at a critical coupling strength suggesting an existence of a phase transition. Below critical coupling, the system reaches order at a definite amplitude of an external input; this order persists for a fixed time slot. Thus, the array behaves as an excitable-like system, even though the single element lacks such a property.

  6. Analysis of spike waves in epilepsy using Hilbert-Huang transform.

    PubMed

    Zhu, Jin-De; Lin, Chin-Feng; Chang, Shun-Hsyung; Wang, Jung-Hua; Peng, Tsung-Ii; Chien, Yu-Yi

    2015-01-01

    In this paper, we used the Hilbert-Huang transform (HHT) analysis method to examine the time-frequency characteristics of spike waves for detecting epilepsy symptoms. We obtained a sample of spike waves and nonspike waves for HHT decomposition by using numerous intrinsic mode functions (IMFs) of the Hilbert transform (HT) to determine the instantaneous, marginal, and Hilbert energy spectra. The Pearson correlation coefficients of the IMFs, and energy-IMF distributions for the electroencephalogram (EEG) signal without spike waves, Spike I, Spike II and Spike III sample waves were determined. The analysis results showed that the ratios of the referred wave and Spike III wave to the referred total energy for IMF1, IMF2, and the residual function exceeded 10%. Furthermore, the energy ratios for IMF1, IMF2, IMF3 and the residual function of Spike I, Spike II to their total energy exceeded 10%. The Pearson correlation coefficients of the IMF3 of the EEG signal without spike waves and Spike I wave, EEG signal without spike waves and Spike II wave, EEG signal without spike waves and Spike III wave, Spike I and II waves, Spike I and III waves, and Spike II and III waves were 0.002, 0.06, 0.01, 0.17, 0.03, and 0.3, respectively. The energy ratios of IMF3 in the δ band to its referred total energy for the EEG signal without spike waves, and of the Spike I, II, and III waves were 4.72, 6.75, 5.41, and 5.55%, respectively. The weighted average frequency of the IMF1, IMF2, and IMF3 of the EEG signal without spike waves was lower than that of the IMF1, IMF2, and IMF3 of the spike waves, respectively. The weighted average magnitude of the IMF3, IMF4, and IMF5 of the EEG signal without spike waves was lower than that of the IMF1, IMF2, and IMF3 of spike waves, respectively.

  7. Women's constructions of the 'right time' to consider decisions about risk-reducing mastectomy and risk-reducing oophorectomy

    PubMed Central

    2010-01-01

    Background Women who are notified they carry a BRCA1/2 mutation are presented with surgical options to reduce their risk of breast and ovarian cancer, including risk-reducing mastectomy (RRM) and risk-reducing oophorectomy (RRO). Growing evidence suggests that a sub-group of women do not make decisions about RRM and RRO immediately following genetic testing, but rather, consider these decisions years later. Women's perspectives on the timing of these decisions are not well understood. Accordingly, the purpose of this research was to describe how women construct the 'right time' to consider decisions about RRM and RRO. Methods In-depth interviews were conducted with 22 BRCA1/2 carrier women and analyzed using qualitative, constant comparative methods. Results The time that lapsed between receipt of genetic test results and receipt of RRM or RRO ranged from three months to nine years. The findings highlighted the importance of considering decisions about RRM and RRO one at a time. The women constructed the 'right time' to consider these decisions to be when: (1) decisions fit into their lives, (2) they had enough time to think about decisions, (3) they were ready emotionally to deal with the decisions and the consequences, (4) all the issues and conflicts were sorted out, (5) there were better options available, and (6) the health care system was ready for them. Conclusions These findings offer novel insights relevant to health care professionals who provide decision support to women considering RRM and RRO. PMID:20687957

  8. SPIKE PENETRATION IN BLAST-WAVE-DRIVEN INSTABILITIES

    SciTech Connect

    Drake, R. P.

    2012-01-10

    The problem of interest is the unstable growth of structure at density transitions affected by blast waves, which arise in natural environments such as core-collapse supernovae and in laboratory experiments. The resulting spikes of dense material, which penetrate the less dense material, develop broadened tips, but the degree of broadening varies substantially across both experiments and simulations. The variable broadening presumably produces variations in the drag experienced by the spike tips as they penetrate the less dense material. The present work has used semianalytic theory to address the question of how the variation in drag might affect the spike penetration, for cases in which the post-shock interface deceleration can be described by a power law in a normalized time variable. It did so by following the evolution of structure on the interface through the initial shock passage, the subsequent small-amplitude phase of Rayleigh-Taylor instability growth, and the later phase in which the spike growth involves the competition of buoyancy and drag. In all phases, the expansion of the system during its evolution was accounted for and was important. The calculated spike length is strongly affected by the drag attributed to spike tip broadening. One finds from such a calculation that it is not unreasonable for narrow spikes to keep up with the shock front of the blast wave. The implication is that the accuracy of prediction of spike penetration and consequent structure by simulations very likely depends on how accurately they treat the broadening of the spike tips and the associated drag. Experimental validation of spike morphology in simulations would be useful.

  9. Regular Patterns in Cerebellar Purkinje Cell Simple Spike Trains

    PubMed Central

    Shin, Soon-Lim; Hoebeek, Freek E.; Schonewille, Martijn; De Zeeuw, Chris I.; Aertsen, Ad; De Schutter, Erik

    2007-01-01

    Background Cerebellar Purkinje cells (PC) in vivo are commonly reported to generate irregular spike trains, documented by high coefficients of variation of interspike-intervals (ISI). In strong contrast, they fire very regularly in the in vitro slice preparation. We studied the nature of this difference in firing properties by focusing on short-term variability and its dependence on behavioral state. Methodology/Principal Findings Using an analysis based on CV2 values, we could isolate precise regular spiking patterns, lasting up to hundreds of milliseconds, in PC simple spike trains recorded in both anesthetized and awake rodents. Regular spike patterns, defined by low variability of successive ISIs, comprised over half of the spikes, showed a wide range of mean ISIs, and were affected by behavioral state and tactile stimulation. Interestingly, regular patterns often coincided in nearby Purkinje cells without precise synchronization of individual spikes. Regular patterns exclusively appeared during the up state of the PC membrane potential, while single ISIs occurred both during up and down states. Possible functional consequences of regular spike patterns were investigated by modeling the synaptic conductance in neurons of the deep cerebellar nuclei (DCN). Simulations showed that these regular patterns caused epochs of relatively constant synaptic conductance in DCN neurons. Conclusions/Significance Our findings indicate that the apparent irregularity in cerebellar PC simple spike trains in vivo is most likely caused by mixing of different regular spike patterns, separated by single long intervals, over time. We propose that PCs may signal information, at least in part, in regular spike patterns to downstream DCN neurons. PMID:17534435

  10. Reliability of spike and burst firing in thalamocortical relay cells.

    PubMed

    Zeldenrust, Fleur; Chameau, Pascal J P; Wadman, Wytse J

    2013-12-01

    The reliability and precision of the timing of spikes in a spike train is an important aspect of neuronal coding. We investigated reliability in thalamocortical relay (TCR) cells in the acute slice and also in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected into the TCR cell soma. The neuron responded with spike trains that showed trial-to-trial variability, due to amongst others slow changes in its internal state and the experimental setup. The DC current allowed to bring the neuron in different states, characterized by a well defined membrane voltage (between -80 and -50 mV) and by a specific firing regime that on depolarization gradually shifted from a predominantly bursting regime to a tonic spiking regime. The filtered frozen white noise generated a spike pattern output with a broad spike interval distribution. The coincidence factor and the Hunter and Milton measure were used as reliability measures of the output spike train. In the experimental TCR cell as well as the Morris-Lecar model cell the reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates. The reliability of the output of the TCR cell increases with depolarization. In hyperpolarized states bursts are more reliable than single spikes. The analytically derived relations were capable to predict several of the experimentally recorded spike features.

  11. Dopaminergic modulation of short-term synaptic plasticity in fast-spiking interneurons of primate dorsolateral prefrontal cortex.

    PubMed

    Gonzalez-Burgos, G; Kroener, S; Seamans, J K; Lewis, D A; Barrionuevo, G

    2005-12-01

    Dopaminergic regulation of primate dorsolateral prefrontal cortex (PFC) activity is essential for cognitive functions such as working memory. However, the cellular mechanisms of dopamine neuromodulation in PFC are not well understood. We have studied the effects of dopamine receptor activation during persistent stimulation of excitatory inputs onto fast-spiking GABAergic interneurons in monkey PFC. Stimulation at 20 Hz induced short-term excitatory postsynaptic potential (EPSP) depression. The D1 receptor agonist SKF81297 (5 microM) significantly reduced the amplitude of the first EPSP but not of subsequent responses in EPSP trains, which still displayed significant depression. Dopamine (DA; 10 microM) effects were similar to those of SKF81297 and were abolished by the D1 antagonist SCH23390 (5 microM), indicating a D1 receptor-mediated effect. DA did not alter miniature excitatory postsynaptic currents, suggesting that its effects were activity dependent and presynaptic action potential dependent. In contrast to previous findings in pyramidal neurons, in fast-spiking cells, contribution of N-methyl-D-aspartate receptors to EPSPs at subthreshold potentials was not significant and fast-spiking cell depolarization decreased EPSP duration. In addition, DA had no significant effects on temporal summation. The selective decrease in the amplitude of the first EPSP in trains delivered every 10 s suggests that in fast-spiking neurons, DA reduces the amplitude of EPSPs evoked at low frequency but not of EPSPs evoked by repetitive stimulation. DA may therefore improve detection of EPSP bursts above background synaptic activity. EPSP bursts displaying short-term depression may transmit spike-timing-dependent temporal codes contained in presynaptic spike trains. Thus DA neuromodulation may increase the signal-to-noise ratio at fast-spiking cell inputs.

  12. Generalized analog thresholding for spike acquisition at ultralow sampling rates

    PubMed Central

    He, Bryan D.; Wein, Alex; Varshney, Lav R.; Kusuma, Julius; Richardson, Andrew G.

    2015-01-01

    Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology. PMID:25904712

  13. On the continuous differentiability of inter-spike intervals of synaptically connected cortical spiking neurons in a neuronal network.

    PubMed

    Kumar, Gautam; Kothare, Mayuresh V

    2013-12-01

    We derive conditions for continuous differentiability of inter-spike intervals (ISIs) of spiking neurons with respect to parameters (decision variables) of an external stimulating input current that drives a recurrent network of synaptically connected neurons. The dynamical behavior of individual neurons is represented by a class of discontinuous single-neuron models. We report here that ISIs of neurons in the network are continuously differentiable with respect to decision variables if (1) a continuously differentiable trajectory of the membrane potential exists between consecutive action potentials with respect to time and decision variables and (2) the partial derivative of the membrane potential of spiking neurons with respect to time is not equal to the partial derivative of their firing threshold with respect to time at the time of action potentials. Our theoretical results are supported by showing fulfillment of these conditions for a class of known bidimensional spiking neuron models.

  14. A method for decoding the neurophysiological spike-response transform.

    PubMed

    Stern, Estee; García-Crescioni, Keyla; Miller, Mark W; Peskin, Charles S; Brezina, Vladimir

    2009-11-15

    Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.

  15. Kernel bandwidth optimization in spike rate estimation.

    PubMed

    Shimazaki, Hideaki; Shinomoto, Shigeru

    2010-08-01

    Kernel smoother and a time-histogram are classical tools for estimating an instantaneous rate of spike occurrences. We recently established a method for selecting the bin width of the time-histogram, based on the principle of minimizing the mean integrated square error (MISE) between the estimated rate and unknown underlying rate. Here we apply the same optimization principle to the kernel density estimation in selecting the width or "bandwidth" of the kernel, and further extend the algorithm to allow a variable bandwidth, in conformity with data. The variable kernel has the potential to accurately grasp non-stationary phenomena, such as abrupt changes in the firing rate, which we often encounter in neuroscience. In order to avoid possible overfitting that may take place due to excessive freedom, we introduced a stiffness constant for bandwidth variability. Our method automatically adjusts the stiffness constant, thereby adapting to the entire set of spike data. It is revealed that the classical kernel smoother may exhibit goodness-of-fit comparable to, or even better than, that of modern sophisticated rate estimation methods, provided that the bandwidth is selected properly for a given set of spike data, according to the optimization methods presented here.

  16. Parameter Estimation of a Spiking Silicon Neuron

    PubMed Central

    Russell, Alexander; Mazurek, Kevin; Mihalaş, Stefan; Niebur, Ernst; Etienne-Cummings, Ralph

    2012-01-01

    Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the model’s output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neuron’s parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neuron’s output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neuron’s parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed. PMID:23852978

  17. Mechanism of frequency-dependent broadening of molluscan neurone soma spikes.

    PubMed

    Aldrich, R W; Getting, P A; Thompson, S H

    1979-06-01

    1. Action potentials recorded from isolated dorid neurone somata increase in duration, i.e. broaden, during low frequency repetitive firing. Spike broadening is substantially reduced by external Co ions and implicates an inward Ca current. 2. During repetitive voltage clamp steps at frequencies slower than 1 Hz, in 100 mM-tetraethyl ammonium ions (TEA) inward Ca currents do not increase in amplitude. 3. Repetitive action potentials result in inactivation of delayed outward current. Likewise, repetitive voltage clamp steps which cause inactivation of delayed outward current also result in longer duration action potentials. 4. The frequency dependence of spike broadening and inactivation of the voltage dependent component (IK) of delayed outward current are similar. 5. Inactivation of IK is observed in all cells, however, only cells with relative large inward Ca currents show significant spike broadening. Spike broadening apparently results from the frequency dependent inactivation of IK which increases the expression of inward Ca current as a prominent shoulder on the repolarizing phase of the action potential. In addition, the presence of a prolonged Ca current increases the duration of the first action potential thereby allowing sufficient time for inactivation of IK.

  18. Stationary transmission distribution of random spike trains by dynamical synapses

    NASA Astrophysics Data System (ADS)

    Hahnloser, Richard H.

    2003-02-01

    Many nonlinearities in neural media are strongly dependent on spike timing jitter and intrinsic dynamics of synaptic transmission. Here we are interested in the stationary density of evoked postsynaptic potentials transmitted by depressing synapses for Poisson spike trains of fixed mean rates. We present a nonperturbative iterative method for computing the stationary density over increasing intervals. We conclude by showing how this method generalizes to other types of synapses, such as facilitating and hybrid synapses.

  19. Sediment spiking for toxicity testing

    SciTech Connect

    Murdoch, M.H.; Norman, D.M.; Chapman, P.M.; Norman, D.M.; Quintino, V.M.

    1994-12-31

    Sediment toxicity testing integrates responses to sediment variables and hence does not directly indicate cause-and-effect. One tool for determining cause-and-effect is sediment spiking in which relatively uncontaminated sediment is amended with known amounts of contaminants, then tested for toxicity. Based on the concentration-response relationship(s), the relative toxicity of the spiked contaminants and their significance in sediment mixtures can be assessed. However, sediment spiking methods vary considerably. The present study details an appropriate methodology for amending sediments with a range of organic contaminant concentrations including different solvent schemes and an equilibration period. This methodology is described as appropriate because predicted and actual concentrations were similar, and responses in an acute 10-d amphipod test matched predictions and other data.

  20. Making Time for Nature: Visual Exposure to Natural Environments Lengthens Subjective Time Perception and Reduces Impulsivity.

    PubMed

    Berry, Meredith S; Repke, Meredith A; Nickerson, Norma P; Conway, Lucian G; Odum, Amy L; Jordan, Kerry E

    2015-01-01

    Impulsivity in delay discounting is associated with maladaptive behaviors such as overeating and drug and alcohol abuse. Researchers have recently noted that delay discounting, even when measured by a brief laboratory task, may be the best predictor of human health related behaviors (e.g., exercise) currently available. Identifying techniques to decrease impulsivity in delay discounting, therefore, could help improve decision-making on a global scale. Visual exposure to natural environments is one recent approach shown to decrease impulsive decision-making in a delay discounting task, although the mechanism driving this result is currently unknown. The present experiment was thus designed to evaluate not only whether visual exposure to natural (mountains, lakes) relative to built (buildings, cities) environments resulted in less impulsivity, but also whether this exposure influenced time perception. Participants were randomly assigned to either a natural environment condition or a built environment condition. Participants viewed photographs of either natural scenes or built scenes before and during a delay discounting task in which they made choices about receiving immediate or delayed hypothetical monetary outcomes. Participants also completed an interval bisection task in which natural or built stimuli were judged as relatively longer or shorter presentation durations. Following the delay discounting and interval bisection tasks, additional measures of time perception were administered, including how many minutes participants thought had passed during the session and a scale measurement of whether time "flew" or "dragged" during the session. Participants exposed to natural as opposed to built scenes were less impulsive and also reported longer subjective session times, although no differences across groups were revealed with the interval bisection task. These results are the first to suggest that decreased impulsivity from exposure to natural as opposed to built

  1. Making Time for Nature: Visual Exposure to Natural Environments Lengthens Subjective Time Perception and Reduces Impulsivity

    PubMed Central

    Berry, Meredith S.; Repke, Meredith A.; Nickerson, Norma P.; Conway, Lucian G.; Odum, Amy L.; Jordan, Kerry E.

    2015-01-01

    Impulsivity in delay discounting is associated with maladaptive behaviors such as overeating and drug and alcohol abuse. Researchers have recently noted that delay discounting, even when measured by a brief laboratory task, may be the best predictor of human health related behaviors (e.g., exercise) currently available. Identifying techniques to decrease impulsivity in delay discounting, therefore, could help improve decision-making on a global scale. Visual exposure to natural environments is one recent approach shown to decrease impulsive decision-making in a delay discounting task, although the mechanism driving this result is currently unknown. The present experiment was thus designed to evaluate not only whether visual exposure to natural (mountains, lakes) relative to built (buildings, cities) environments resulted in less impulsivity, but also whether this exposure influenced time perception. Participants were randomly assigned to either a natural environment condition or a built environment condition. Participants viewed photographs of either natural scenes or built scenes before and during a delay discounting task in which they made choices about receiving immediate or delayed hypothetical monetary outcomes. Participants also completed an interval bisection task in which natural or built stimuli were judged as relatively longer or shorter presentation durations. Following the delay discounting and interval bisection tasks, additional measures of time perception were administered, including how many minutes participants thought had passed during the session and a scale measurement of whether time "flew" or "dragged" during the session. Participants exposed to natural as opposed to built scenes were less impulsive and also reported longer subjective session times, although no differences across groups were revealed with the interval bisection task. These results are the first to suggest that decreased impulsivity from exposure to natural as opposed to built

  2. Muddy conditions reduce hygiene and lying time in dairy cattle and increase time spent on concrete.

    PubMed

    Chen, Jennifer M; Stull, Carolyn L; Ledgerwood, David N; Tucker, Cassandra B

    2017-03-01

    Dairy cattle spend less time lying and show signs of increased stress when housed in rainy and windy conditions, but no work has separated the effects of exposure to inclement weather from muddy conditions underfoot. Our objective was to evaluate the effects of muddy conditions alone on lying behavior, hygiene, and physiological responses. We housed pairs of pregnant, nonlactating dairy cattle (n = 12; 6 primigravid heifers, 6 multiparous cows) in enclosed pens with dirt floors and a concrete feed apron. Cattle were exposed to 3 levels of soil moisture: 90 (dry), 74 (muddy), or 67% (very muddy) dry matter for 5 d each in a replicated 3 × 3 Latin square design. Lying time was measured on all days with data loggers, and lying locations and postures were recorded on the final day of each treatment. Before and after each treatment, blood samples were collected, and the percentage of dirty surface area was measured on the udder, hind leg, and side of each animal. Cattle spent less time lying down in muddier conditions, especially in the first 24 h of exposure, when cows and heifers spent only 3.2 and 5.8 h, respectively, lying down in the muddiest treatment compared with 12.5 and 12.7 h on dry soil. When the soil was dry, cattle never chose to lie down on concrete, but in muddier conditions they spent a greater proportion of their lying time on concrete (mean ± SE: 56 ± 14 and 10 ± 8% in the very muddy and muddy treatments, respectively). The shift in lying location was more marked for heifers, and all 6 spent ≥87% of their lying time on concrete in the muddiest treatment. When cattle chose to lie down on wetter soil, they limited the surface area exposed to their surroundings by tucking their legs beneath their bodies (mean ± SE: 30 ± 11, 15 ± 4, and 5 ± 2% of lying observations in the very muddy, muddy, and dry treatments, respectively). Despite cattle spending less time on wetter soil, all 3 measured body parts became dirtier in muddier conditions (1.4-, 1

  3. 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

  4. The microwave spectrum of solar millisecond spikes

    NASA Technical Reports Server (NTRS)

    Staehli, M.; Magun, A.

    1986-01-01

    The microwave radiation from solar flares sometimes shows short and intensive spikes which are superimposed on the burst continuum. In order to determine the upper frequency limit of their occurrence and the circular polarization, a statistical analysis was performed on digital microwave observations from 3.2 to 92.5 GHz. Additionally, fine structures were investigated with a fast 32-channel spectrometer at 3.47 GHz. It was found that about 10 percent of the bursts show fine structures at 3.2 and 5.2 GHz, whereas none occurred above 8.4 GHz. Most of the observed spikes were very short and their bandwidth varied from below 0.5 MHz to more than 200 MHz. Simultaneous observations at two further frequencies showed no coincident spikes at the second and third harmonic. The observations can be explained by the theory of electron cyclotron masering if the observed bandwidths are determined by magnetic field inhomogeneities or if the rise times are independent of the source diameters. The latter would imply source sizes between 50 and 100 km.

  5. Reducing acquisition times in multidimensional NMR with a time-optimized Fourier encoding algorithm.

    PubMed

    Zhang, Zhiyong; Smith, Pieter E S; Frydman, Lucio

    2014-11-21

    Speeding up the acquisition of multidimensional nuclear magnetic resonance (NMR) spectra is an important topic in contemporary NMR, with central roles in high-throughput investigations and analyses of marginally stable samples. A variety of fast NMR techniques have been developed, including methods based on non-uniform sampling and Hadamard encoding, that overcome the long sampling times inherent to schemes based on fast-Fourier-transform (FFT) methods. Here, we explore the potential of an alternative fast acquisition method that leverages a priori knowledge, to tailor polychromatic pulses and customized time delays for an efficient Fourier encoding of the indirect domain of an NMR experiment. By porting the encoding of the indirect-domain to the excitation process, this strategy avoids potential artifacts associated with non-uniform sampling schemes and uses a minimum number of scans equal to the number of resonances present in the indirect dimension. An added convenience is afforded by the fact that a usual 2D FFT can be used to process the generated data. Acquisitions of 2D heteronuclear correlation NMR spectra on quinine and on the anti-inflammatory drug isobutyl propionic phenolic acid illustrate the new method's performance. This method can be readily automated to deal with complex samples such as those occurring in metabolomics, in in-cell as well as in in vivo NMR applications, where speed and temporal stability are often primary concerns.

  6. Reducing acquisition times in multidimensional NMR with a time-optimized Fourier encoding algorithm

    SciTech Connect

    Zhang, Zhiyong; Smith, Pieter E. S.; Frydman, Lucio

    2014-11-21

    Speeding up the acquisition of multidimensional nuclear magnetic resonance (NMR) spectra is an important topic in contemporary NMR, with central roles in high-throughput investigations and analyses of marginally stable samples. A variety of fast NMR techniques have been developed, including methods based on non-uniform sampling and Hadamard encoding, that overcome the long sampling times inherent to schemes based on fast-Fourier-transform (FFT) methods. Here, we explore the potential of an alternative fast acquisition method that leverages a priori knowledge, to tailor polychromatic pulses and customized time delays for an efficient Fourier encoding of the indirect domain of an NMR experiment. By porting the encoding of the indirect-domain to the excitation process, this strategy avoids potential artifacts associated with non-uniform sampling schemes and uses a minimum number of scans equal to the number of resonances present in the indirect dimension. An added convenience is afforded by the fact that a usual 2D FFT can be used to process the generated data. Acquisitions of 2D heteronuclear correlation NMR spectra on quinine and on the anti-inflammatory drug isobutyl propionic phenolic acid illustrate the new method's performance. This method can be readily automated to deal with complex samples such as those occurring in metabolomics, in in-cell as well as in in vivo NMR applications, where speed and temporal stability are often primary concerns.

  7. Tile Drainage Density Reduces Groundwater Travel Times and Compromises Riparian Buffer Effectiveness.

    PubMed

    Schilling, Keith E; Wolter, Calvin F; Isenhart, Thomas M; Schultz, Richard C

    2015-11-01

    Strategies to reduce nitrate-nitrogen (nitrate) pollution delivered to streams often seek to increase groundwater residence time to achieve measureable results, yet the effects of tile drainage on residence time have not been well documented. In this study, we used a geographic information system groundwater travel time model to quantify the effects of artificial subsurface drainage on groundwater travel times in the 7443-ha Bear Creek watershed in north-central Iowa. Our objectives were to evaluate how mean groundwater travel times changed with increasing drainage intensity and to assess how tile drainage density reduces groundwater contributions to riparian buffers. Results indicate that mean groundwater travel times are reduced with increasing degrees of tile drainage. Mean groundwater travel times decreased from 5.6 to 1.1 yr, with drainage densities ranging from 0.005 m (7.6 mi) to 0.04 m (62 mi), respectively. Model simulations indicate that mean travel times with tile drainage are more than 150 times faster than those that existed before settlement. With intensive drainage, less than 2% of the groundwater in the basin appears to flow through a perennial stream buffer, thereby reducing the effectiveness of this practice to reduce stream nitrate loads. Hence, strategies, such as reconnecting tile drainage to buffers, are promising because they increase groundwater residence times in tile-drained watersheds.

  8. 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

  9. Propagation of epileptic spikes reconstructed from spatiotemporal magnetoencephalographic and electroencephalographic source analysis

    PubMed Central

    Tanaka, Naoaki; Hämäläinen, Matti S; Ahlfors, Seppo P.; Liu, Hesheng; Madsen, Joseph R.; Bourgeois, Blaise F.; Lee, Jong Woo; Dworetzky, Barbara A.; Belliveau, John W.; Stufflebeam, Steven M.

    2009-01-01

    The purpose of this study is to assess the accuracy of spatiotemporal source analysis of magnetoencephalography (MEG) and scalp electroencephalography (EEG) for representing the propagation of frontotemporal spikes in patients with partial epilepsy. This study focuses on frontotemporal spikes, which are typically characterized by a preceding anterior temporal peak followed by an ipsilateral inferior frontal peak. Ten patients with frontotemporal spikes on MEG/EEG were studied. We analyzed the propagation of temporal to frontal epileptic spikes on both MEG and EEG independently by using a cortically-constrained minimum norm estimate (MNE). Spatiotemporal source distribution of each spike was obtained on the cortical surface derived from the patient’s MRI. All patients underwent an extraoperative intracranial EEG (IEEG) recording covering temporal and frontal lobes after presurgical evaluation. We extracted source waveforms of MEG and EEG from the source distribution of interictal spikes at the sites corresponding to the location of intracranial electrodes. The time differences of the ipsilateral temporal and frontal peaks as obtained by MEG, EEG and IEEG were statistically compared in each patient. In all patients, MEG and IEEG showed similar time differences between temporal and frontal peaks. The time differences of EEG spikes were significantly smaller than those of IEEG in nine of ten patients. Spatiotemporal analysis of MEG spikes models the time course of frontotemporal spikes as observed on IEEG more adequately than EEG in our patients. Spatiotemporal source analysis may be useful for planning epilepsy surgery, by predicting the pattern of IEEG spikes. PMID:20006721

  10. Propagation of epileptic spikes reconstructed from spatiotemporal magnetoencephalographic and electroencephalographic source analysis.

    PubMed

    Tanaka, Naoaki; Hämäläinen, Matti S; Ahlfors, Seppo P; Liu, Hesheng; Madsen, Joseph R; Bourgeois, Blaise F; Lee, Jong Woo; Dworetzky, Barbara A; Belliveau, John W; Stufflebeam, Steven M

    2010-03-01

    The purpose of this study is to assess the accuracy of spatiotemporal source analysis of magnetoencephalography (MEG) and scalp electroencephalography (EEG) for representing the propagation of frontotemporal spikes in patients with partial epilepsy. This study focuses on frontotemporal spikes, which are typically characterized by a preceding anterior temporal peak followed by an ipsilateral inferior frontal peak. Ten patients with frontotemporal spikes on MEG/EEG were studied. We analyzed the propagation of temporal to frontal epileptic spikes on both MEG and EEG independently by using a cortically constrained minimum norm estimate (MNE). Spatiotemporal source distribution of each spike was obtained on the cortical surface derived from the patient's MRI. All patients underwent an extraoperative intracranial EEG (IEEG) recording covering temporal and frontal lobes after presurgical evaluation. We extracted source waveforms of MEG and EEG from the source distribution of interictal spikes at the sites corresponding to the location of intracranial electrodes. The time differences of the ipsilateral temporal and frontal peaks as obtained by MEG, EEG and IEEG were statistically compared in each patient. In all patients, MEG and IEEG showed similar time differences between temporal and frontal peaks. The time differences of EEG spikes were significantly smaller than those of IEEG in nine of ten patients. Spatiotemporal analysis of MEG spikes models the time course of frontotemporal spikes as observed on IEEG more adequately than EEG in our patients. Spatiotemporal source analysis may be useful for planning epilepsy surgery, by predicting the pattern of IEEG spikes.

  11. Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks.

    PubMed

    de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A; Navas, Adrian; Villacorta-Atienza, Jose A

    2015-01-01

    Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

  12. Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

    PubMed Central

    de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A.; Navas, Adrian; Villacorta-Atienza, Jose A.

    2015-01-01

    Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh–Rose neurons. PMID:26648863

  13. 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

  14. A stimulus-dependent spike threshold is an optimal neural coder

    PubMed Central

    Jones, Douglas L.; Johnson, Erik C.; Ratnam, Rama

    2015-01-01

    A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code. PMID:26082710

  15. Improved similarity measures for small sets of spike trains.

    PubMed

    Naud, Richard; Gerhard, Felipe; Mensi, Skander; Gerstner, Wulfram

    2011-12-01

    Multiple measures have been developed to quantify the similarity between two spike trains. These measures have been used for the quantification of the mismatch between neuron models and experiments as well as for the classification of neuronal responses in neuroprosthetic devices and electrophysiological experiments. Frequently only a few spike trains are available in each class. We derive analytical expressions for the small-sample bias present when comparing estimators of the time-dependent firing intensity. We then exploit analogies between the comparison of firing intensities and previously used spike train metrics and show that improved spike train measures can be successfully used for fitting neuron models to experimental data, for comparisons of spike trains, and classification of spike train data. In classification tasks, the improved similarity measures can increase the recovered information. We demonstrate that when similarity measures are used for fitting mathematical models, all previous methods systematically underestimate the noise. Finally, we show a striking implication of this deterministic bias by reevaluating the results of the single-neuron prediction challenge.

  16. Fast reducers, slow augmenters: a psychophysiological analysis of temperament-related differences in reaction time.

    PubMed

    Schwerdtfeger, Andreas; Getzmann, Stephan; Baltissen, Rüdiger

    2004-05-01

    Augmenting-reducing theory describes temperament-related differences in the modulation of stimulation. Reducers are supposed to need more stimulation than augmenters because of a cortical attenuation of incoming stimuli. The study investigated differences between augmenters and reducers (classified by questionnaire) in performance and information processing strategies in a simple reaction time task. Fifty-two subjects (27 augmenters, 25 reducers) took part in a visual reaction time task with go- and catch-trials (30%). Reaction times, commission errors, and psychophysiological indicators of information processing (N1, P300, EMG) were recorded. Reducers were faster and exhibited more commission errors than augmenters. Moreover, reducers exhibited higher N1-amplitudes, faster EMG-onsets and higher EMG-amplitudes than augmenters. An additional pain tolerance test revealed that male reducers were more pain tolerant than the other participants. These results are consistent with the proposition that reducers have a higher need for stimulation than augmenters. Probably, they utilize locomotor activity in order to compensate for their attenuated arousal.

  17. Fusarium graminearum infection and deoxynivalenol concentrations during development of wheat spikes.

    PubMed

    Cowger, Christina; Arellano, Consuelo

    2013-05-01

    Fusarium head blight (FHB) affects whole spikes of small grain plants, yet little is known about how FHB develops following infection, or about the concentration or progression of the mycotoxin deoxynivalenol (DON) in non-grain spike tissues. Fusarium mycotoxin levels in whole small-grain spikes are of concern to producers of whole-crop silage, as well as users of straw containing chaff for animal bedding or winter livestock rations. A 2-year field experiment was performed in Kinston, NC to reveal the time course of FHB development. Eight winter wheat cultivars with varying levels of FHB resistance were used in the 2006 experiment, and four of them were used in 2007. Plots were spray-inoculated with Fusarium graminearum macroconidia at mid-anthesis. Four durations of post-anthesis mist were applied: 0, 10, 20, or 30 days. Spike samples were collected and bulked by plot at 15, 25, 35, 45, 55, and 65 days after anthesis (daa); samples were separated into grain, glume, and rachis fractions. Increasing durations of post-anthesis moisture elevated grain DON and reduced the effect of cultivar on DON, presumably by affecting the expression of resistance, in all spike tissues. Fusarium-damaged kernels increased from early kernel-hard to harvest-ripe in both years. Percent infected kernels increased from medium-milk to harvest-ripe. During grainfill, DON concentrations declined in grain but increased in rachises and glumes, peaking at early kernel-hard, before declining. Higher mean and maximum DON levels were observed in rachises and glumes than in grain. Estimated whole-spike DON peaked at early kernel-hard. In a high-FHB year, whole-plant harvest for forage should be conducted as early as possible. Straw that may be consumed by livestock could contain significant amounts of DON in chaff, and DON can be minimized if straw is sourced from low-symptom crops. Cultivar FHB resistance ratings and disease data should be useful in predicting whole-spike DON levels. Overall

  18. Reducing bias and analyzing variability in the time-left procedure.

    PubMed

    Trujano, R Emmanuel; Orduña, Vladimir

    2015-04-01

    The time-left procedure was designed to evaluate the psychophysical function for time. Although previous results indicated a linear relationship, it is not clear what role the observed bias toward the time-left option plays in this procedure and there are no reports of how variability changes with predicted indifference. The purposes of this experiment were to reduce bias experimentally, and to contrast the difference limen (a measure of variability around indifference) with predictions from scalar expectancy theory (linear timing) and behavioral economic model (logarithmic timing). A control group of 6 rats performed the original time-left procedure with C=60 s and S=5, 10,…, 50, 55 s, whereas a no-bias group of 6 rats performed the same conditions in a modified time-left procedure in which only a single response per choice trial was allowed. Results showed that bias was reduced for the no-bias group, observed indifference grew linearly with predicted indifference for both groups, and difference limen and Weber ratios decreased as expected indifference increased for the control group, which is consistent with linear timing, whereas for the no-bias group they remained constant, consistent with logarithmic timing. Therefore, the time-left procedure generates results consistent with logarithmic perceived time once bias is experimentally reduced.

  19. Simple In-Hospital Interventions to Reduce Door-to-CT Time in Acute Stroke

    PubMed Central

    Taheraghdam, Aliakbar; Rikhtegar, Reza; Mehrvar, Kaveh; Mehrara, Mehrdad; Hassasi, Rogayyeh; Aliyar, Hannane; Farzi, Mohammadamin; Hasaneh Tamar, Somayyeh

    2016-01-01

    Background. Intravenous tissue plasminogen activator, a time dependent therapy, can reduce the morbidity and mortality of acute ischemic stroke. This study was designed to assess the effect of simple in-hospital interventions on reducing door-to-CT (DTC) time and reaching door-to-needle (DTN) time of less than 60 minutes. Methods. Before any intervention, DTC time was recorded for 213 patients over a one-year period at our center. Five simple quality-improvement interventions were implemented, namely, call notification, prioritizing patients for CT scan, prioritizing patients for lab analysis, specifying a bed for acute stroke patients, and staff education. After intervention, over a course of 44 months, DTC time was recorded for 276 patients with the stroke code. Furthermore DTN time was recorded for 106 patients who were treated with IV thrombolytic therapy. Results. The median DTC time significantly decreased in the postintervention period comparing to the preintervention period [median (IQR); 20 (12–30) versus 75 (52.5–105), P < 0.001]. At the postintervention period, the median (IQR) DTN time was 55 (40–73) minutes and proportion of patients with DTN time less than 60 minutes was 62.4% (P < 0.001). Conclusion. Our interventions significantly reduced DTC time and resulted in an acceptable DTN time. These interventions are feasible in most hospitals and should be considered. PMID:27478641

  20. Optimization Methods for Spiking Neurons and Networks

    PubMed Central

    Russell, Alexander; Orchard, Garrick; Dong, Yi; Mihalaş, Ştefan; Niebur, Ernst; Tapson, Jonathan; Etienne-Cummings, Ralph

    2011-01-01

    Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual manipulation of parameters is both time consuming and non-intuitive due to the nonlinear relationship between parameters and the neuron’s output. The complexity rises even further as the neurons are networked and the systems often become mathematically intractable. In large circuits, the desired behavior and timing of action potential trains may be known but the timing of the individual action potentials is unknown and unimportant, whereas in single neuron systems the timing of individual action potentials is critical. In this paper, we automate the process of finding parameters. To configure a single neuron we derive a maximum likelihood method for configuring a neuron model, specifically the Mihalas–Niebur Neuron. Similarly, to configure neural circuits, we show how we use genetic algorithms (GAs) to configure parameters for a network of simple integrate and fire with adaptation neurons. The GA approach is demonstrated both in software simulation and hardware implementation on a reconfigurable custom very large scale integration chip. PMID:20959265

  1. Inter-Laboratory Uranium Double-Spike Experiment

    SciTech Connect

    Russ, G. P

    1999-11-11

    In environmental samples, the major analytical limitation on the use of uranium {sup 238}U/{sup 235}U determinations as an indicator of uranium enrichment is mass dependent bias occurring during the measurement. The double-spike technique can be used to correct the data for this effect. The purpose of this experiment was to evaluate the variation of mass bias among several laboratories and to determine the extent to which the double-spike could be used to reduce analytical uncertainty. Four laboratories performed replicate analyses on each of three samples. Generally mass bias was determined to be small compared to the random scatter of the measurements, but in at least one case, the bias was > 1%. In 8 of 12 cases, intra-laboratory variance was reduced when the double-spike correction was applied. For all three samples, the inter-laboratory variance was decreased, though the decrease was small. Based on a reasonable assumption about the true isotopic compositions of the samples, the accuracy of 11 of the twelve analyses was improved by applying the double spike correction. When the double spike is used to correct for mass bias, the {sup 238}U/{sup 235}U accuracy is better than 1% even for samples as small as 1 ng. For 50 ng of uranium, 0.1% accuracy was achieved.

  2. Neuronal spike train entropy estimation by history clustering.

    PubMed

    Watters, Nicholas; Reeke, George N

    2014-09-01

    Neurons send signals to each other by means of sequences of action potentials (spikes). Ignoring variations in spike amplitude and shape that are probably not meaningful to a receiving cell, the information content, or entropy of the signal depends on only the timing of action potentials, and because there is no external clock, only the interspike intervals, and not the absolute spike times, are significant. Estimating spike train entropy is a difficult task, particularly with small data sets, and many methods of entropy estimation have been proposed. Here we present two related model-based methods for estimating the entropy of neural signals and compare them to existing methods. One of the methods is fast and reasonably accurate, and it converges well with short spike time records; the other is impractically time-consuming but apparently very accurate, relying on generating artificial data that are a statistical match to the experimental data. Using the slow, accurate method to generate a best-estimate entropy value, we find that the faster estimator converges to this value more closely and with smaller data sets than many existing entropy estimators.

  3. Estimating the correlation between bursty spike trains and local field potentials.

    PubMed

    Li, Zhaohui; Ouyang, Gaoxiang; Yao, Li; Li, Xiaoli

    2014-09-01

    To further understand rhythmic neuronal synchronization, an increasingly useful method is to determine the relationship between the spiking activity of individual neurons and the local field potentials (LFPs) of neural ensembles. Spike field coherence (SFC) is a widely used method for measuring the synchronization between spike trains and LFPs. However, due to the strong dependency of SFC on the burst index, it is not suitable for analyzing the relationship between bursty spike trains and LFPs, particularly in high frequency bands. To address this issue, we developed a method called weighted spike field correlation (WSFC), which uses the first spike in each burst multiple times to estimate the relationship. In the calculation, the number of times that the first spike is used is equal to the spike count per burst. The performance of this method was demonstrated using simulated bursty spike trains and LFPs, which comprised sinusoids with different frequencies, amplitudes, and phases. This method was also used to estimate the correlation between pyramidal cells in the hippocampus and gamma oscillations in rats performing behaviors. Analyses using simulated and real data demonstrated that the WSFC method is a promising measure for estimating the correlation between bursty spike trains and high frequency LFPs.

  4. Bistability induces episodic spike communication by inhibitory neurons in neuronal networks

    NASA Astrophysics Data System (ADS)

    Kazantsev, V. B.; Asatryan, S. Yu.

    2011-09-01

    Bistability is one of the important features of nonlinear dynamical systems. In neurodynamics, bistability has been found in basic Hodgkin-Huxley equations describing the cell membrane dynamics. When the neuron is clamped near its threshold, the stable rest potential may coexist with the stable limit cycle describing periodic spiking. However, this effect is often neglected in network computations where the neurons are typically reduced to threshold firing units (e.g., integrate-and-fire models). We found that the bistability may induce spike communication by inhibitory coupled neurons in the spiking network. The communication is realized in the form of episodic discharges with synchronous (correlated) spikes during the episodes. A spiking phase map is constructed to describe the synchronization and to estimate basic spike phase locking modes.

  5. Calcium-activated chloride channels as a new target to control the spiking pattern of neurons.

    PubMed

    Ha, Go Eun; Cheong, Eunji

    2017-03-03

    Neuronal firing patterns and frequencies determine the nature of encoded information in the neural circuits. Here we discuss the molecular identity and cellular mechanisms of spike-frequency adaptation in central nervous system (CNS). Spike-frequency adaptation in thalamocortical (TC) and CA1 hippocampal neurons is mediated by the Ca2+-activated Cl- channel (CACC) anoctamin-2 (ANO2). Knockdown of ANO2 in these neurons results in significantly reduced spike-frequency adaptation along with increased number of spikes. No previous study has described the finding that CACCs mediate afterhyperpolarization currents, which result in the modulation of neuronal spike patterns in the central nervous system. Therefore, our study proposes a novel role for ANO2 in spike-frequency adaptation and transmission of information in the brain.

  6. Measuring time costs in interventions designed to reduce behavior problems among children and youth.

    PubMed

    Foster, E Michael; Johnson-Shelton, Deborah; Taylor, Ted K

    2007-09-01

    The economic evaluation of psychosocial interventions is a growing area of research. Though time costs are central to the economist's understanding of social costs, these costs generally have been ignored by prevention scientists. This article highlights the need to measure such costs and then reviews the principles economists use in valuing time. It then considers the specific time costs that often arise in interventions designed to reduce behavior problems among children and youth. These include classroom time devoted to program activities, the time of parents or other caregivers, the time of teachers (outside of the classroom), and the time of volunteers. We consider the economic principles that govern how economists value these inputs and then apply these principles to data from an evaluation of a prominent intervention in the field, the Incredible Years Program. We find that the time costs are potentially rather large and consider the implications for public policy of ignoring them.

  7. Self-control with spiking and non-spiking neural networks playing games.

    PubMed

    Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

    2010-01-01

    Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the

  8. Spike voltage topography in temporal lobe epilepsy.

    PubMed

    Asadi-Pooya, Ali A; Asadollahi, Marjan; Shimamoto, Shoichi; Lorenzo, Matthew; Sperling, Michael R

    2016-07-15

    We investigated the voltage topography of interictal spikes in patients with temporal lobe epilepsy (TLE) to see whether topography was related to etiology for TLE. Adults with TLE, who had epilepsy surgery for drug-resistant seizures from 2011 until 2014 at Jefferson Comprehensive Epilepsy Center were selected. Two groups of patients were studied: patients with mesial temporal sclerosis (MTS) on MRI and those with other MRI findings. The voltage topography maps of the interictal spikes at the peak were created using BESA software. We classified the interictal spikes as polar, basal, lateral, or others. Thirty-four patients were studied, from which the characteristics of 340 spikes were investigated. The most common type of spike orientation was others (186 spikes; 54.7%), followed by lateral (146; 42.9%), polar (5; 1.5%), and basal (3; 0.9%). Characteristics of the voltage topography maps of the spikes between the two groups of patients were somewhat different. Five spikes in patients with MTS had polar orientation, but none of the spikes in patients with other MRI findings had polar orientation (odds ratio=6.98, 95% confidence interval=0.38 to 127.38; p=0.07). Scalp topographic mapping of interictal spikes has the potential to offer different information than visual inspection alone. The present results do not allow an immediate clinical application of our findings; however, detecting a polar spike in a patient with TLE may increase the possibility of mesial temporal sclerosis as the underlying etiology.

  9. 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

  10. Local Variation of Hashtag Spike Trains and Popularity in Twitter.

    PubMed

    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.

  11. Microwave and digital imaging technology reduce turnaround times for diagnostic electron microscopy.

    PubMed

    Giberson, Richard T; Austin, Ronald L; Charlesworth, Jon; Adamson, Grete; Herrera, Guillermo A

    2003-01-01

    The contributions of microwave methods and digital imaging techniques, when taken together, can reduce routine specimen processing and evaluation for diagnostic electron microscopy to a time frame never thought possible. Significant improvements in both technologies over the last 5 years led the authors to evaluate their combined attributes as the most likely candidate to provide a realistic solution in the reduction of turnaround times for diagnostic electron microscopy. For diagnostic electron microscopy to compete favorably with immunohistochemistry and other ancillary diagnostic techniques, it must improve its turnaround time. To evaluate this hypothesis the microwave-assisted processing results of over 2,000 diagnostic cases were evaluated as was a digital image administration system used for the acquisition and dissemination of diagnostic results. The incorporation of both technologies resulted in turnaround times being reduced to 4 h or less.

  12. Applying Reduced Generator Models in the Coarse Solver of Parareal in Time Parallel Power System Simulation

    SciTech Connect

    Duan, Nan; Dimitrovski, Aleksandar D; Simunovic, Srdjan; Sun, Kai

    2016-01-01

    The development of high-performance computing techniques and platforms has provided many opportunities for real-time or even faster-than-real-time implementation of power system simulations. One approach uses the Parareal in time framework. The Parareal algorithm has shown promising theoretical simulation speedups by temporal decomposing a simulation run into a coarse simulation on the entire simulation interval and fine simulations on sequential sub-intervals linked through the coarse simulation. However, it has been found that the time cost of the coarse solver needs to be reduced to fully exploit the potentials of the Parareal algorithm. This paper studies a Parareal implementation using reduced generator models for the coarse solver and reports the testing results on the IEEE 39-bus system and a 327-generator 2383-bus Polish system model.

  13. Two effective approaches to reduce data storage in reverse time migration

    NASA Astrophysics Data System (ADS)

    Sun, Weijia; Fu, Li-Yun

    2013-07-01

    Prestack reverse time migration (RTM) requires extensive data storage since it computes wavefields in forward time and accesses wavefields in reverse order. We first review several successful schemes that have been proposed to reduce data storage, but require more computational redundancies. We propose two effective strategies to reduce data storage during RTM. The first strategy is based on the Nyquist sampling theorem, which involves no extra computational cost. The fact is that the time sampling intervals required by numerical algorithms or given by field records is generally several times smaller than that satisfied by the Nyquist sampling theorem. Therefore, we can correlate the source wavefields with the receiver wavefields at the Nyquist time step, which helps decrease storage of time history. The second strategy is based on a lossless compression algorithm, which is widely used in computer science and information theory. The compression approach reduces storage significantly at a little computational cost. Numerical examples show that the two proposed strategies are effective and efficient.

  14. Using real time process measurements to reduce catheter related bloodstream infections in the intensive care unit

    PubMed Central

    Wall, R; Ely, E; Elasy, T; Dittus, R; Foss, J; Wilkerson, K; Speroff, T

    2005-01-01

    

Problem: Measuring a process of care in real time is essential for continuous quality improvement (CQI). Our inability to measure the process of central venous catheter (CVC) care in real time prevented CQI efforts aimed at reducing catheter related bloodstream infections (CR-BSIs) from these devices. Design: A system was developed for measuring the process of CVC care in real time. We used these new process measurements to continuously monitor the system, guide CQI activities, and deliver performance feedback to providers. Setting: Adult medical intensive care unit (MICU). Key measures for improvement: Measured process of CVC care in real time; CR-BSI rate and time between CR-BSI events; and performance feedback to staff. Strategies for change: An interdisciplinary team developed a standardized, user friendly nursing checklist for CVC insertion. Infection control practitioners scanned the completed checklists into a computerized database, thereby generating real time measurements for the process of CVC insertion. Armed with these new process measurements, the team optimized the impact of a multifaceted intervention aimed at reducing CR-BSIs. Effects of change: The new checklist immediately provided real time measurements for the process of CVC insertion. These process measures allowed the team to directly monitor adherence to evidence-based guidelines. Through continuous process measurement, the team successfully overcame barriers to change, reduced the CR-BSI rate, and improved patient safety. Two years after the introduction of the checklist the CR-BSI rate remained at a historic low. Lessons learnt: Measuring the process of CVC care in real time is feasible in the ICU. When trying to improve care, real time process measurements are an excellent tool for overcoming barriers to change and enhancing the sustainability of efforts. To continually improve patient safety, healthcare organizations should continually measure their key clinical processes in real

  15. Reducibility of 1-d Schrödinger Equation with Time Quasiperiodic Unbounded Perturbations, II

    NASA Astrophysics Data System (ADS)

    Bambusi, D.

    2017-01-01

    We study the Schrödinger equation on R with a potential behaving as {x^{2l}} at infinity, {l in [1, + ∞)} and with a small time quasiperiodic perturbation. We prove that if the perturbation belongs to a class of unbounded symbols including smooth potentials and magnetic type terms with controlled growth at infinity, then the system is reducible.

  16. 22 CFR 401.19 - Reducing or extending time and dispensing with statements.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... statements. 401.19 Section 401.19 Foreign Relations INTERNATIONAL JOINT COMMISSION, UNITED STATES AND CANADA... any case where the Commission considers that such action would be in the public interest and not... Commission may reduce or extend the time for the presentation of any paper or the doing of any act...

  17. 22 CFR 401.19 - Reducing or extending time and dispensing with statements.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... statements. 401.19 Section 401.19 Foreign Relations INTERNATIONAL JOINT COMMISSION, UNITED STATES AND CANADA... any case where the Commission considers that such action would be in the public interest and not... Commission may reduce or extend the time for the presentation of any paper or the doing of any act...

  18. 22 CFR 401.19 - Reducing or extending time and dispensing with statements.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... statements. 401.19 Section 401.19 Foreign Relations INTERNATIONAL JOINT COMMISSION, UNITED STATES AND CANADA... any case where the Commission considers that such action would be in the public interest and not... Commission may reduce or extend the time for the presentation of any paper or the doing of any act...

  19. 22 CFR 401.19 - Reducing or extending time and dispensing with statements.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... statements. 401.19 Section 401.19 Foreign Relations INTERNATIONAL JOINT COMMISSION, UNITED STATES AND CANADA... any case where the Commission considers that such action would be in the public interest and not... Commission may reduce or extend the time for the presentation of any paper or the doing of any act...

  20. 22 CFR 401.19 - Reducing or extending time and dispensing with statements.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... statements. 401.19 Section 401.19 Foreign Relations INTERNATIONAL JOINT COMMISSION, UNITED STATES AND CANADA... any case where the Commission considers that such action would be in the public interest and not... Commission may reduce or extend the time for the presentation of any paper or the doing of any act...

  1. Time-restricted feeding of a high-fat diet reduces diet-induced obesity

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Reducing obesity may alleviate many medical complications including diabetes, cardiovascular disease and cancer. It has been suggested that obesity is contributed by the disruption of the circadian rhythms in addition to increased caloric intake. Restricting feeding to particular times of the day ma...

  2. Controllability discrepancy and irreducibility/reducibility of Floquet factorisations in linear continuous-time periodic systems

    NASA Astrophysics Data System (ADS)

    Zhou, Jun; Lu, Xinbiao; Qian, Huimin

    2016-09-01

    The paper reports interesting but unnoticed facts about irreducibility (resp., reducibility) of Flouqet factorisations and their harmonic implication in term of controllability in finite-dimensional linear continuous-time periodic (FDLCP) systems. Reducibility and irreducibility are attributed to matrix logarithm algorithms during computing Floquet factorisations in FDLCP systems, which are a pair of essential features but remain unnoticed in the Floquet theory so far. The study reveals that reducible Floquet factorisations may bring in harmonic waves variance into the Fourier analysis of FDLCP systems that in turn may alter our interpretation of controllability when the Floquet factors are used separately during controllability testing; namely, controllability interpretation discrepancy (or simply, controllability discrepancy) may occur and must be examined whenever reducible Floquet factorisations are involved. On the contrary, when irreducible Floquet factorisations are employed, controllability interpretation discrepancy can be avoided. Examples are included to illustrate such observations.

  3. Tunable Low Energy, Compact and High Performance Neuromorphic Circuit for Spike-Based Synaptic Plasticity

    PubMed Central

    Rahimi Azghadi, Mostafa; Iannella, Nicolangelo; Al-Sarawi, Said; Abbott, Derek

    2014-01-01

    Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities. PMID:24551089

  4. Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.

    PubMed

    Rahimi Azghadi, Mostafa; Iannella, Nicolangelo; Al-Sarawi, Said; Abbott, Derek

    2014-01-01

    Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities.

  5. Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation

    PubMed Central

    Liu, Qian; Pineda-García, Garibaldi; Stromatias, Evangelos; Serrano-Gotarredona, Teresa; Furber, Steve B.

    2016-01-01

    Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organization have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-based visual stimuli is needed to provide meaningful comparisons between different systems, and a corresponding evaluation methodology is also required to measure the performance of SNN models and their hardware implementations. In this paper we first propose an initial NE (Neuromorphic Engineering) dataset based on standard computer vision benchmarksand that uses digits from the MNIST database. This dataset is compatible with the state of current research on spike-based image recognition. The corresponding spike trains are produced using a range of techniques: rate-based Poisson spike generation, rank order encoding, and recorded output from a silicon retina with both flashing and oscillating input stimuli. In addition, a complementary evaluation methodology is presented to assess both model-level and hardware-level performance. Finally, we demonstrate the use of the dataset and the evaluation methodology using two SNN models to validate the performance of the models and their hardware

  6. Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation.

    PubMed

    Liu, Qian; Pineda-García, Garibaldi; Stromatias, Evangelos; Serrano-Gotarredona, Teresa; Furber, Steve B

    2016-01-01

    Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organization have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-based visual stimuli is needed to provide meaningful comparisons between different systems, and a corresponding evaluation methodology is also required to measure the performance of SNN models and their hardware implementations. In this paper we first propose an initial NE (Neuromorphic Engineering) dataset based on standard computer vision benchmarksand that uses digits from the MNIST database. This dataset is compatible with the state of current research on spike-based image recognition. The corresponding spike trains are produced using a range of techniques: rate-based Poisson spike generation, rank order encoding, and recorded output from a silicon retina with both flashing and oscillating input stimuli. In addition, a complementary evaluation methodology is presented to assess both model-level and hardware-level performance. Finally, we demonstrate the use of the dataset and the evaluation methodology using two SNN models to validate the performance of the models and their hardware

  7. 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

  8. Practical solutions for reducing container ships' waiting times at ports using simulation model

    NASA Astrophysics Data System (ADS)

    Sheikholeslami, Abdorreza; Ilati, Gholamreza; Yeganeh, Yones Eftekhari

    2013-12-01

    The main challenge for container ports is the planning required for berthing container ships while docked in port. Growth of containerization is creating problems for ports and container terminals as they reach their capacity limits of various resources which increasingly leads to traffic and port congestion. Good planning and management of container terminal operations reduces waiting time for liner ships. Reducing the waiting time improves the terminal's productivity and decreases the port difficulties. Two important keys to reducing waiting time with berth allocation are determining suitable access channel depths and increasing the number of berths which in this paper are studied and analyzed as practical solutions. Simulation based analysis is the only way to understand how various resources interact with each other and how they are affected in the berthing time of ships. We used the Enterprise Dynamics software to produce simulation models due to the complexity and nature of the problems. We further present case study for berth allocation simulation of the biggest container terminal in Iran and the optimum access channel depth and the number of berths are obtained from simulation results. The results show a significant reduction in the waiting time for container ships and can be useful for major functions in operations and development of container ship terminals.

  9. Caffeine reduces reaction time and improves performance in simulated-contest of taekwondo.

    PubMed

    Santos, Victor G F; Santos, Vander R F; Felippe, Leandro J C; Almeida, Jose W; Bertuzzi, Rômulo; Kiss, Maria A P D M; Lima-Silva, Adriano E

    2014-02-10

    The aim of this study was to investigate the effects of caffeine on reaction time during a specific taekwondo task and athletic performance during a simulated taekwondo contest. Ten taekwondo athletes ingested either 5 mg·kg⁻¹ body mass caffeine or placebo and performed two combats (spaced apart by 20 min). The reaction-time test (five kicks "Bandal Tchagui") was performed immediately prior to the first combat and immediately after the first and second combats. Caffeine improved reaction time (from 0.42 ± 0.05 to 0.37 ± 0.07 s) only prior to the first combat (P = 0.004). During the first combat, break times during the first two rounds were shorter in caffeine ingestion, followed by higher plasma lactate concentrations compared with placebo (P = 0.029 and 0.014, respectively). During the second combat, skipping-time was reduced, and relative attack times and attack/skipping ratio was increased following ingestion of caffeine during the first two rounds (all P < 0.05). Caffeine resulted in no change in combat intensity parameters between the first and second combat (all P > 0.05), but combat intensity was decreased following placebo (all P < 0.05). In conclusion, caffeine reduced reaction time in non-fatigued conditions and delayed fatigue during successive taekwondo combats.

  10. Using Force Sensors and Neural Models to Encode Tactile Stimuli as Spike-based Responses

    PubMed Central

    Kim, Elmer K.; Gerling, Gregory J.; Wellnitz, Scott A.; Lumpkin, Ellen A.

    2011-01-01

    Tactile sensors will augment the next generation of prosthetic limbs. However, currently available sensors do not produce biologically-compatible output. This work seeks to illustrate that a force sensor combined with a bi-phasic, neural spiking algorithm, or spiking-sensor, can produce spiking patterns similar to that of the slowly adapting type I (SAI) mechanoreceptor. Experiments were conducted where first spike latency and inter-spike interval, in response to a rapidly delivered (100 ms) sustained displacement (1.1, 1.3, 1.5 mm for 5 s), were compared between the spiking-sensor and SAI recording. The results indicated that the predicted spike times were similar, in magnitude and increasing linear trend, to those observed with the SAI. Over the three displacements, average dynamic ISIs were 7.3, 4.2, 3.8 ms for the spiking-sensor and 6.2, 6.9, 4.1 ms for the SAI, while average static ISIs were 69.0, 45.2, 35.1 ms and 159.9, 69.6, 38.8 ms. The predicted first spike latencies (74.3, 73.9, 96.3 ms) lagged in comparison to those observed for the SAI (26.8, 31.7, 28.8 ms), which may be due to both the different applied force ramp-ups and the SAI’s exquisite dynamic sensitivity range and rapid response time. PMID:21826287

  11. Real-time, high frequency QRS electrocardiograph with reduced amplitude zone detection

    NASA Technical Reports Server (NTRS)

    Schlegel, Todd T. (Inventor); DePalma, Jude L. (Inventor); Moradi, Saeed (Inventor)

    2009-01-01

    Real time cardiac electrical data are received from a patient, manipulated to determine various useful aspects of the ECG signal, and displayed in real time in a useful form on a computer screen or monitor. The monitor displays the high frequency data from the QRS complex in units of microvolts, juxtaposed with a display of conventional ECG data in units of millivolts or microvolts. The high frequency data are analyzed for their root mean square (RMS) voltage values and the discrete RMS values and related parameters are displayed in real time. The high frequency data from the QRS complex are analyzed with imbedded algorithms to determine the presence or absence of reduced amplitude zones, referred to herein as ''RAZs''. RAZs are displayed as ''go, no-go'' signals on the computer monitor. The RMS and related values of the high frequency components are displayed as time varying signals, and the presence or absence of RAZs may be similarly displayed over time.

  12. Participatory Workplace Interventions Can Reduce Sedentary Time for Office Workers—A Randomised Controlled Trial

    PubMed Central

    Parry, Sharon; Straker, Leon; Gilson, Nicholas D.; Smith, Anne J.

    2013-01-01

    Background Occupational sedentary behaviour is an important contributor to overall sedentary risk. There is limited evidence for effective workplace interventions to reduce occupational sedentary time and increase light activity during work hours. The purpose of the study was to determine if participatory workplace interventions could reduce total sedentary time, sustained sedentary time (bouts >30 minutes), increase the frequency of breaks in sedentary time and promote light intensity activity and moderate/vigorous activity (MVPA) during work hours. Methods A randomised controlled trial (ANZCTR number: ACTN12612000743864) was conducted using clerical, call centre and data processing workers (n = 62, aged 25–59 years) in 3 large government organisations in Perth, Australia. Three groups developed interventions with a participatory approach: ‘Active office’ (n = 19), ‘Active Workstation’ and promotion of incidental office activity; ‘Traditional physical activity’ (n = 14), pedometer challenge to increase activity between productive work time and ‘Office ergonomics’ (n = 29), computer workstation design and breaking up computer tasks. Accelerometer (ActiGraph GT3X, 7 days) determined sedentary time, sustained sedentary time, breaks in sedentary time, light intensity activity and MVPA on work days and during work hours were measured before and following a 12 week intervention period. Results For all participants there was a significant reduction in sedentary time on work days (−1.6%, p = 0.006) and during work hours (−1.7%, p = 0.014) and a significant increase in number of breaks/sedentary hour on work days (0.64, p = 0.005) and during work hours (0.72, p = 0.015); there was a concurrent significant increase in light activity during work hours (1.5%, p = 0.012) and MVPA on work days (0.6%, p = 0.012). Conclusions This study explored novel ways to modify work practices to reduce occupational sedentary

  13. Stable propagation of synchronous spiking in cortical neural networks

    NASA Astrophysics Data System (ADS)

    Diesmann, Markus; Gewaltig, Marc-Oliver; Aertsen, Ad

    1999-12-01

    The classical view of neural coding has emphasized the importance of information carried by the rate at which neurons discharge action potentials. More recent proposals that information may be carried by precise spike timing have been challenged by the assumption that these neurons operate in a noisy fashion-presumably reflecting fluctuations in synaptic input-and, thus, incapable of transmitting signals with millisecond fidelity. Here we show that precisely synchronized action potentials can propagate within a model of cortical network activity that recapitulates many of the features of biological systems. An attractor, yielding a stable spiking precision in the (sub)millisecond range, governs the dynamics of synchronization. Our results indicate that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is possible within a cortical-like network.

  14. Audiovisual biofeedback improves image quality and reduces scan time for respiratory-gated 3D MRI

    NASA Astrophysics Data System (ADS)

    Lee, D.; Greer, P. B.; Arm, J.; Keall, P.; Kim, T.

    2014-03-01

    The purpose of this study was to test the hypothesis that audiovisual (AV) biofeedback can improve image quality and reduce scan time for respiratory-gated 3D thoracic MRI. For five healthy human subjects respiratory motion guidance in MR scans was provided using an AV biofeedback system, utilizing real-time respiratory motion signals. To investigate the improvement of respiratory-gated 3D MR images between free breathing (FB) and AV biofeedback (AV), each subject underwent two imaging sessions. Respiratory-related motion artifacts and imaging time were qualitatively evaluated in addition to the reproducibility of external (abdominal) motion. In the results, 3D MR images in AV biofeedback showed more anatomic information such as a clear distinction of diaphragm, lung lobes and sharper organ boundaries. The scan time was reduced from 401±215 s in FB to 334±94 s in AV (p-value 0.36). The root mean square variation of the displacement and period of the abdominal motion was reduced from 0.4±0.22 cm and 2.8±2.5 s in FB to 0.1±0.15 cm and 0.9±1.3 s in AV (p-value of displacement <0.01 and p-value of period 0.12). This study demonstrated that audiovisual biofeedback improves image quality and reduces scan time for respiratory-gated 3D MRI. These results suggest that AV biofeedback has the potential to be a useful motion management tool in medical imaging and radiation therapy procedures.

  15. Reducing Youth Screen Time: Qualitative Metasynthesis of Findings on Barriers and Facilitators

    PubMed Central

    Minges, Karl E.; Salmon, Jo; Dunstan, David W.; Owen, Neville; Chao, Ariana; Whittemore, Robin

    2015-01-01

    Objective An integrated perspective on the relevant qualitative findings on the experience of screen time in youth can inform the development of hypotheses to be tested in future research and can guide the development of interventions to decrease sedentary behavior. The purpose of this qualitative metasynthesis was to explore parent, youth, and educational professionals’ perceptions of barriers to, and facilitators of, reducing youth screen time. Method Qualitative metasynthesis techniques were used to analyze and synthesize 15 qualitative studies of screen time among youth (11–18 years) meeting inclusion criteria. The phrases, quotes, and/or author interpretations (i.e., theme or subtheme) were recorded in a data display matrix to facilitate article comparisons. Codes were collapsed into 23 categories of similar conceptual meaning and 3 overarching themes were derived using thematic analysis procedures. Results Study sample sizes ranged from 6 to 270 participants from 6 countries. Data collection methods included focus groups (n = 6), interviews (n = 4), focus group and interviews (n = 4), and naturalistic observation (n = 1) with youth and/or parents. Data analysis techniques included thematic analysis (n = 9), content analysis (n = 3), grounded theory (n = 1), observation (n = 1), and interpretive phenomenological analysis (n = 1). Three thematic categories were identified: (a) youth’s norms—screen time is an integral part of daily life, and facilitates opportunities for entertainment, social interaction, and escapism; (b) family dynamics and parental roles—parents are conflicted and send mixed messages about the appropriate uses and amounts of screen time; and, (c) resources and environment—engagement in screen time is dependent on school, community, neighborhood, and home environmental contexts. Conclusions Screen time is an established norm in many youth cultures, presenting barriers to behavior change. Parents recognize the importance of reducing

  16. Analysis of high-level radioactive slurries as a method to reduce DWPF turnaround times

    SciTech Connect

    Coleman, C.J.; Bibler, N.E.; Ferrara, D.M.; Hay, M.S.

    1996-06-01

    Analysis of Defense Waste Processing Facility (DWPF) samples as slurries rather than as dried or vitrified samples is an effective way to reduce sample turnaround times. Slurries can be dissolved with a mixture of concentrated acids to yield solutions for elemental analysis by inductively coupled plasma-atomic emission spectroscopy (ICP-AES). Slurry analyses can be performed in eight hours, whereas analyses of vitrified samples require up to 40 hours to complete. Analyses of melter feed samples consisting of the DWPF borosilicate frit and either simulated or actual DWPF radioactive sludge were typically within a range of 3--5% of the predicted value based on the relative amounts of sludge and frit added to the slurry. The results indicate that the slurry analysis approach yields analytical accuracy and precision competitive with those obtained from analyses of vitrified samples. Slurry analyses offer a viable alternative to analyses of solid samples as a simple way to reduce analytical turnaround times.

  17. Use of videoconferencing in Wales to reduce carbon dioxide emissions, travel costs and time.

    PubMed

    Lewis, Delyth; Tranter, Glynis; Axford, Alan T

    2009-01-01

    In September 2005 a telemedicine service was started to assist multidisciplinary teams in Wales to improve cancer services. In October 2006 and October 2007 users of videoconferencing equipment at one site completed questionnaires. During October 2006 a total of 18,000 km of car travel were avoided, equivalent to 1696 kg of CO(2) emission. During October 2007 a total of 20,800 km of car travel were avoided, equivalent to 2590 kg of CO(2) emission. We estimate that 48 trees would take a year to absorb that quantity of CO(2). The results of the surveys show that exploiting telemedicine makes better use of staff time, reduces the time spent travelling and assists in reducing climate change by limiting the emissions of CO(2).

  18. Improved cosmological model fitting of Planck data with a dark energy spike

    NASA Astrophysics Data System (ADS)

    Park, Chan-Gyung

    2015-06-01

    The Λ cold dark matter (Λ CDM ) model is currently known as the simplest cosmology model that best describes observations with a minimal number of parameters. Here we introduce a cosmology model that is preferred over the conventional Λ CDM one by constructing dark energy as the sum of the cosmological constant Λ and an additional fluid that is designed to have an extremely short transient spike in energy density during the radiation-matter equality era and an early scaling behavior with radiation and matter densities. The density parameter of the additional fluid is defined as a Gaussian function plus a constant in logarithmic scale-factor space. Searching for the best-fit cosmological parameters in the presence of such a dark energy spike gives a far smaller chi-square value by about 5 times the number of additional parameters introduced and narrower constraints on the matter density and Hubble constant compared with the best-fit Λ CDM model. The significant improvement in reducing the chi square mainly comes from the better fitting of the Planck temperature power spectrum around the third (ℓ≈800 ) and sixth (ℓ≈1800 ) acoustic peaks. The likelihood ratio test and the Akaike information criterion suggest that the model of a dark energy spike is strongly favored by the current cosmological observations over the conventional Λ CDM model. However, based on the Bayesian information criterion which penalizes models with more parameters, the strong evidence supporting the presence of a dark energy spike disappears. Our result emphasizes that the alternative cosmological parameter estimation with even better fitting of the same observational data is allowed in Einstein's gravity.

  19. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES).

    PubMed

    Hansen, Nikolaus; Müller, Sibylle D; Koumoutsakos, Petros

    2003-01-01

    This paper presents a novel evolutionary optimization strategy based on the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). This new approach is intended to reduce the number of generations required for convergence to the optimum. Reducing the number of generations, i.e., the time complexity of the algorithm, is important if a large population size is desired: (1) to reduce the effect of noise; (2) to improve global search properties; and (3) to implement the algorithm on (highly) parallel machines. Our method results in a highly parallel algorithm which scales favorably with large numbers of processors. This is accomplished by efficiently incorporating the available information from a large population, thus significantly reducing the number of generations needed to adapt the covariance matrix. The original version of the CMA-ES was designed to reliably adapt the covariance matrix in small populations but it cannot exploit large populations efficiently. Our modifications scale up the efficiency to population sizes of up to 10n, where n is the problem dimension. This method has been applied to a large number of test problems, demonstrating that in many cases the CMA-ES can be advanced from quadratic to linear time complexity.

  20. Nursing students' time management, reducing stress and gaining satisfaction: a grounded theory study.

    PubMed

    Mirzaei, Tayebeh; Oskouie, Fatemeh; Rafii, Forough

    2012-03-01

    In the course of their studies, nursing students must learn many skills and acquire the knowledge required for their future profession. This study investigates how Iranian nursing students manage their time according to the circumstances and obstacles of their academic field. Research was conducted using the grounded theory method. Twenty-one nursing students were purposefully chosen as participants. Data was collected through semi-structured interviews and analyzed using the method suggested by Corbin and Strauss. One of the three processes that the nursing students used was "unidirectional time management." This pattern consists of accepting the nursing field, overcoming uncertainty, assessing conditions, feeling stress, and trying to reduce stress and create satisfaction. It was found that students allotted most of their time to academic tasks in an attempt to overcome their stress. The findings of this study indicate the need for these students to have time for the extra-curricular activities and responsibilities that are appropriate to their age.

  1. Analytic treatment of source photon emission times to reduce noise in implicit Monte Carlo calculations

    SciTech Connect

    Trahan, Travis J.; Gentile, Nicholas A.

    2012-09-10

    Statistical uncertainty is inherent to any Monte Carlo simulation of radiation transport problems. In space-angle-frequency independent radiative transfer calculations, the uncertainty in the solution is entirely due to random sampling of source photon emission times. We have developed a modification to the Implicit Monte Carlo algorithm that eliminates noise due to sampling of the emission time of source photons. In problems that are independent of space, angle, and energy, the new algorithm generates a smooth solution, while a standard implicit Monte Carlo solution is noisy. For space- and angle-dependent problems, the new algorithm exhibits reduced noise relative to standard implicit Monte Carlo in some cases, and comparable noise in all other cases. In conclusion, the improvements are limited to short time scales; over long time scales, noise due to random sampling of spatial and angular variables tends to dominate the noise reduction from the new algorithm.

  2. How to reduce the time windows for primary percutaneous coronary intervention.

    PubMed

    Ortolani, Paolo; Reimers, Bernhard; Tubaro, Marco; Sesana, Giovanni

    2009-10-01

    Percutaneous coronary intervention (PCI) is an effective procedure for re-establishing coronary artery perfusion in ST-segment elevation myocardial infarction. PCI is the preferred therapeutic option when it can be performed by an experienced team within 90-120 min of the first medical contact. Time from the onset of symptoms to balloon inflation seems to correlate directly with mortality rates. We discuss both hospital strategies and territorial system networks aimed at reducing the time windows for primary PCI, thereby improving clinical outcome and survival rates.

  3. 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.

  4. USING CENTER HOLE HEAT TRANSFER TO REDUCE FORMATION TIMES FOR CERAMIC WASTE FORMS FROM PYROPROCESSING

    SciTech Connect

    Kenneth J. Bateman; Charles W. Solbrig

    2006-07-01

    The waste produced from processing spent fuel from the EBR II reactor must be processed into a waste form suitable for long term storage in Yucca Mountain. The method chosen produces zeolite granules mixed with glass frit, which must then be converted into a solid. This is accomplished by loading it into a can and heating to 900 C in a furnace regulated at 915 C. During heatup to 900 C, the zeolite and glass frit react and consolidate to produce a sodalite monolith. The resultant ceramic waste form (CWF) is then cooled. The waste is 52 cm in diameter and initially 300 cm long but consolidates to 150 cm long during the heating process. After cooling it is then inserted in a 5-DHLW/DOE SNF Long Canister. Without intervention, the waste takes 82 hours to heat up to 900 C in a furnace designed to geometrically fit the cylindrical waste form. This paper investigates the reduction in heating times possible with four different methods of additional heating through a center hole. The hole size is kept small to maximize the amount of CWF that is processed in a single run. A hole radius of 1.82 cm was selected which removes only 1% of the CWF. A reference computation was done with a specified inner hole surface temperature of 915 C to provide a benchmark for the amount of improvement which can be made. It showed that the heatup time can potentially be reduced to 43 hours with center hole heating. The first method, simply pouring high temperature liquid aluminum into the hole, did not produce any noticeable effect on reducing heat up times. The second method, flowing liquid aluminum through the hole, works well as long as the velocity is high enough (2.5 cm/sec) to prevent solidification of the aluminum during the initial front movement of the aluminum into the center hole. The velocity can be reduced to 1 cm/sec after the initial front has traversed the ceramic. This procedure reduces the formation time to near that of the reference case. The third method, flowing a gas

  5. Increasing temperature forcing reduces the Greenland Ice Sheet's response time scale

    NASA Astrophysics Data System (ADS)

    Applegate, Patrick J.; Parizek, Byron R.; Nicholas, Robert E.; Alley, Richard B.; Keller, Klaus

    2015-10-01

    Damages from sea level rise, as well as strategies to manage the associated risk, hinge critically on the time scale and eventual magnitude of sea level rise. Satellite observations and paleo-data suggest that the Greenland Ice Sheet (GIS) loses mass in response to increased temperatures, and may thus contribute substantially to sea level rise as anthropogenic climate change progresses. The time scale of GIS mass loss and sea level rise are deeply uncertain, and are often assumed to be constant. However, previous ice sheet modeling studies have shown that the time scale of GIS response likely decreases strongly with increasing temperature anomaly. Here, we map the relationship between temperature anomaly and the time scale of GIS response, by perturbing a calibrated, three-dimensional model of GIS behavior. Additional simulations with a profile, higher-order, ice sheet model yield time scales that are broadly consistent with those obtained using the three-dimensional model, and shed light on the feedbacks in the ice sheet system that cause the time scale shortening. Semi-empirical modeling studies that assume a constant time scale of sea level adjustment, and are calibrated to small preanthropogenic temperature and sea level changes, may underestimate future sea level rise. Our analysis suggests that the benefits of reducing greenhouse gas emissions, in terms of avoided sea level rise from the GIS, may be greatest if emissions reductions begin before large temperature increases have been realized. Reducing anthropogenic climate change may also allow more time for design and deployment of risk management strategies by slowing sea level contributions from the GIS.

  6. Reducing the ecological consequences of night-time light pollution: options and developments

    PubMed Central

    Gaston, Kevin J; Davies, Thomas W; Bennie, Jonathan; Hopkins, John

    2012-01-01

    1. Much concern has been expressed about the ecological consequences of night-time light pollution. This concern is most often focused on the encroachment of artificial light into previously unlit areas of the night-time environment, but changes in the spectral composition, duration and spatial pattern of light are also recognized as having ecological effects. 2. Here, we examine the potential consequences for organisms of five management options to reduce night-time light pollution. These are to (i) prevent areas from being artificially lit; (ii) limit the duration of lighting; (iii) reduce the ‘trespass’ of lighting into areas that are not intended to be lit (including the night sky); (iv) change the intensity of lighting; and (v) change the spectral composition of lighting. 3. Maintaining and increasing natural unlit areas is likely to be the most effective option for reducing the ecological effects of lighting. However, this will often conflict with other social and economic objectives. Decreasing the duration of lighting will reduce energy costs and carbon emissions, but is unlikely to alleviate many impacts on nocturnal and crepuscular animals, as peak times of demand for lighting frequently coincide with those in the activities of these species. Reducing the trespass of lighting will maintain heterogeneity even in otherwise well-lit areas, providing dark refuges that mobile animals can exploit. Decreasing the intensity of lighting will reduce energy consumption and limit both skyglow and the area impacted by high-intensity direct light. Shifts towards ‘whiter’ light are likely to increase the potential range of environmental impacts as light is emitted across a broader range of wavelengths. 4. Synthesis and applications. The artificial lightscape will change considerably over coming decades with the drive for more cost-effective low-carbon street lighting solutions and growth in the artificially lit area. Developing lighting strategies that minimize

  7. Reducing the ecological consequences of night-time light pollution: options and developments.

    PubMed

    Gaston, Kevin J; Davies, Thomas W; Bennie, Jonathan; Hopkins, John

    2012-12-01

    1. Much concern has been expressed about the ecological consequences of night-time light pollution. This concern is most often focused on the encroachment of artificial light into previously unlit areas of the night-time environment, but changes in the spectral composition, duration and spatial pattern of light are also recognized as having ecological effects.2. Here, we examine the potential consequences for organisms of five management options to reduce night-time light pollution. These are to (i) prevent areas from being artificially lit; (ii) limit the duration of lighting; (iii) reduce the 'trespass' of lighting into areas that are not intended to be lit (including the night sky); (iv) change the intensity of lighting; and (v) change the spectral composition of lighting.3. Maintaining and increasing natural unlit areas is likely to be the most effective option for reducing the ecological effects of lighting. However, this will often conflict with other social and economic objectives. Decreasing the duration of lighting will reduce energy costs and carbon emissions, but is unlikely to alleviate many impacts on nocturnal and crepuscular animals, as peak times of demand for lighting frequently coincide with those in the activities of these species. Reducing the trespass of lighting will maintain heterogeneity even in otherwise well-lit areas, providing dark refuges that mobile animals can exploit. Decreasing the intensity of lighting will reduce energy consumption and limit both skyglow and the area impacted by high-intensity direct light. Shifts towards 'whiter' light are likely to increase the potential range of environmental impacts as light is emitted across a broader range of wavelengths.4.Synthesis and applications. The artificial lightscape will change considerably over coming decades with the drive for more cost-effective low-carbon street lighting solutions and growth in the artificially lit area. Developing lighting strategies that minimize adverse

  8. Advantage of support vector machine for neural spike train decoding under spike sorting errors.

    PubMed

    Hwan Kim, Kyung; Shin Kim, Sung; June Kim, Sung

    2005-01-01

    Decoding of kinematic variables from neuronal spike trains is important for neuroprosthetic devices. The spike trains from single units must be extracted from extracellular neural signals and thus spike detection and sorting procedure is essential. Since the spike detection and sorting procedure may yield considerable errors, decoding algorithm should be robust against spike train errors. Here we showed that the spike train decoding algorithms employing a nonlinear mapping, especially support vector machine (SVM), may be more advantageous contrary to conventional belief that linear filter is sufficient. The advantage became more conspicuous with erroneous spike trains. Using the SVM, satisfactory performance could be obtained much more easily, compared to the case of using multilayer perceptron, which was employed for previous studies. The results suggests the possibility of neuroprosthetic device with a low-quality spike sorting preprocessor.

  9. Channel noise effects on first spike latency of a stochastic Hodgkin-Huxley neuron

    NASA Astrophysics Data System (ADS)

    Maisel, Brenton; Lindenberg, Katja

    2017-02-01

    While it is widely accepted that information is encoded in neurons via action potentials or spikes, it is far less understood what specific features of spiking contain encoded information. Experimental evidence has suggested that the timing of the first spike may be an energy-efficient coding mechanism that contains more neural information than subsequent spikes. Therefore, the biophysical features of neurons that underlie response latency are of considerable interest. Here we examine the effects of channel noise on the first spike latency of a Hodgkin-Huxley neuron receiving random input from many other neurons. Because the principal feature of a Hodgkin-Huxley neuron is the stochastic opening and closing of channels, the fluctuations in the number of open channels lead to fluctuations in the membrane voltage and modify the timing of the first spike. Our results show that when a neuron has a larger number of channels, (i) the occurrence of the first spike is delayed and (ii) the variation in the first spike timing is greater. We also show that the mean, median, and interquartile range of first spike latency can be accurately predicted from a simple linear regression by knowing only the number of channels in the neuron and the rate at which presynaptic neurons fire, but the standard deviation (i.e., neuronal jitter) cannot be predicted using only this information. We then compare our results to another commonly used stochastic Hodgkin-Huxley model and show that the more commonly used model overstates the first spike latency but can predict the standard deviation of first spike latencies accurately. We end by suggesting a more suitable definition for the neuronal jitter based upon our simulations and comparison of the two models.

  10. Optimal testing input sets for reduced diagnosis time of nuclear power plant digital electronic circuits

    SciTech Connect

    Kim, D.S.; Seong, P.H. . Dept. of Nuclear Engineering)

    1994-02-01

    This paper describes the optimal testing input sets required for the fault diagnosis of the nuclear power plant digital electronic circuits. With the complicated systems such as very large scale integration (VLSI), nuclear power plant (NPP), and aircraft, testing is the major factor of the maintenance of the system. Particularly, diagnosis time grows quickly with the complexity of the component. In this research, for reduce diagnosis time the authors derived the optimal testing sets that are the minimal testing sets required for detecting the failure and for locating of the failed component. For reduced diagnosis time, the technique presented by Hayes fits best for the approach to testing sets generation among many conventional methods. However, this method has the following disadvantages: (a) it considers only the simple network (b) it concerns only whether the system is in failed state or not and does not provide the way to locate the failed component. Therefore the authors have derived the optimal testing input sets that resolve these problems by Hayes while preserving its advantages. When they applied the optimal testing sets to the automatic fault diagnosis system (AFDS) which incorporates the advanced fault diagnosis method of artificial intelligence technique, they found that the fault diagnosis using the optimal testing sets makes testing the digital electronic circuits much faster than that using exhaustive testing input sets; when they applied them to test the Universal (UV) Card which is a nuclear power plant digital input/output solid state protection system card, they reduced the testing time up to about 100 times.

  11. Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain

    PubMed Central

    Brette, Romain

    2015-01-01

    Does the brain use a firing rate code or a spike timing code? Considering this controversial question from an epistemological perspective, I argue that progress has been hampered by its problematic phrasing. It takes the perspective of an external observer looking at whether those two observables vary with stimuli, and thereby misses the relevant question: which one has a causal role in neural activity? When rephrased in a more meaningful way, the rate-based view appears as an ad hoc methodological postulate, one that is practical but with virtually no empirical or theoretical support. PMID:26617496

  12. Spike sorting for large, dense electrode arrays

    PubMed Central

    Goodman, Dan F. M.; Schulman, John; Hunter, Maximilian L.D.; Saleem, Aman B.; Grosmark, Andres; Belluscio, Mariano; Denfield, George H.; Ecker, Alexander S.; Tolias, Andreas S.; Solomon, Samuel; Buzsaki, Gyorgy; Carandini, Matteo; Harris, Kenneth D.

    2016-01-01

    Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely-spaced recording sites, and electrodes with thousands of sites are currently under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons, from the raw data captured from the probes. Here, we present a set of novel tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus, and thalamus of rat, mouse, macaque, and marmoset, demonstrating error rates as low as 5%. PMID:26974951

  13. Spiking neuron network Helmholtz machine

    PubMed Central

    Sountsov, Pavel; Miller, Paul

    2015-01-01

    An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule. PMID:25954191

  14. Spiking neuron network Helmholtz machine.

    PubMed

    Sountsov, Pavel; Miller, Paul

    2015-01-01

    An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

  15. BDNF Boosts Spike Fidelity in Chaotic Neural Oscillations

    PubMed Central

    Fujisawa, Shigeyoshi; Yamada, Maki K.; Nishiyama, Nobuyoshi; Matsuki, Norio; Ikegaya, Yuji

    2004-01-01

    Oscillatory activity and its nonlinear dynamics are of fundamental importance for information processing in the central nervous system. Here we show that in aperiodic oscillations, brain-derived neurotrophic factor (BDNF), a member of the neurotrophin family, enhances the accuracy of action potentials in terms of spike reliability and temporal precision. Cultured hippocampal neurons displayed irregular oscillations of membrane potential in response to sinusoidal 20-Hz somatic current injection, yielding wobbly orbits in the phase space, i.e., a strange attractor. Brief application of BDNF suppressed this unpredictable dynamics and stabilized membrane potential fluctuations, leading to rhythmical firing. Even in complex oscillations induced by external stimuli of 40 Hz (γ) on a 5-Hz (θ) carrier, BDNF-treated neurons generated more precisely timed spikes, i.e., phase-locked firing, coupled with θ-phase precession. These phenomena were sensitive to K252a, an inhibitor of tyrosine receptor kinases and appeared attributable to BDNF-evoked Na+ current. The data are the first indication of pharmacological control of endogenous chaos. BDNF diminishes the ambiguity of spike time jitter and thereby might assure neural encoding, such as spike timing-dependent synaptic plasticity. PMID:14990508

  16. Stochasticity, spikes and decoding: sufficiency and utility of order statistics.

    PubMed

    Richmond, Barry J

    2009-06-01

    For over 75 years it has been clear that the number of spikes in a neural response is an important part of the neuronal code. Starting as early as the 1950's with MacKay and McCullough, there has been speculation over whether each spike and its exact time of occurrence carry information. Although it is obvious that the firing rate carries information it has been less clear as to whether there is information in exactly timed patterns, when they arise from the dynamics of the neurons and networks, as opposed to when they represent some strong external drive that entrains them. One strong null hypothesis that can be applied is that spike trains arise from stochastic sampling of an underlying deterministic temporally modulated rate function, that is, there is a time-varying rate function. In this view, order statistics seem to provide a sufficient theoretical construct to both generate simulated spike trains that are indistinguishable from those observed experimentally, and to evaluate (decode) the data recovered from experiments. It remains to learn whether there are physiologically important signals that are not described by such a null hypothesis.

  17. Solar microwave millisecond spike at 2.84 GHz

    NASA Technical Reports Server (NTRS)

    Fu, Qi-Jun; Jin, Sheng-Zhen; Zhao, Ren-Yang; Zheng, Le-Ping; Liu, Yu-Ying; Li, Xiao-Cong; Wang, Shu-Lan; Chen, Zhi-Jun; Hu, Chu-Min

    1986-01-01

    Using the high time resolution of 1 ms, the data of solar microwave millisecond spike (MMS) event was recorded more than two hundred times at the frequency of 2.84 GHz at Beijing (Peking) Observatory since May 1981. A preliminary analysis was made. It can be seen from the data that the MMS-events have a variety of the fast activities such as the dispersed and isolated spikes, the clusters of the crowded spikes, the weak spikes superimposed on the noise background, and the phenomena of absorption. The marked differences from that observed with lower time resolution are presented. Using the data, a valuable statistical analysis was made. There are close correlations between MMS-events and hard X-ray bursts, and fast drifting bursts. The MMS events are highly dependent on the type of active regions and the magnetic field configuration. It seems to be crucial to find out the accurate positions on the active region where the MMS-events happen and to make co-operative observations at different bands during the special period when specific active regions appear on the solar disk.

  18. Doping-Spike PtSi Schottky Infrared Detectors with Extended Cutoff Wavelengths

    NASA Technical Reports Server (NTRS)

    Lin, T. L.; Park, J. S.; Gunapala, S. D.; Jones, E. W.; Castillo, H. M. Del

    1994-01-01

    A technique incorporating a p+ doping spike at the silicide/Si interface to reduce the effective Schottky barrier of the silicide infrared detectors and thus extend the cutoff wavelength has been developed.

  19. Does Enhancing Work-Time Control and Flexibility Reduce Turnover? A Naturally Occurring Experiment

    PubMed Central

    Moen, Phyllis; Kelly, Erin L.; Hill, Rachelle

    2011-01-01

    We investigate the turnover effects of an organizational innovation (ROWE—Results Only Work Environment) aimed at moving away from standard time practices to focus on results rather than time spent at work. To model rates of turnover, we draw on survey data from a sample of employees at a corporate headquarters (N = 775) and institutional records of turnover over eight months following the ROWE implementation. We find the odds of turnover are indeed lower for employees participating in the ROWE initiative, which offers employees greater work-time control and flexibility, and that this is the case regardless of employees’ gender, age, or family life stage. ROWE also moderates the turnover effects of organizational tenure and negative home-to-work spillover, physical symptoms, and job insecurity, with those in ROWE who report these situations generally less likely to leave the organization. Additionally, ROWE reduces turnover intentions among those remaining with the corporation. This research moves the “opting-out” argument from one of private troubles to an issue of greater employee work-time control and flexibility by showing that an organizational policy initiative can reduce turnover. PMID:21532909

  20. Systematic review of effective strategies for reducing screen time among young children.

    PubMed

    Schmidt, Marie Evans; Haines, Jess; O'Brien, Ashley; McDonald, Julia; Price, Sarah; Sherry, Bettylou; Taveras, Elsie M

    2012-07-01

    Screen-media use among young children is highly prevalent, disproportionately high among children from lower-income families and racial/ethnic minorities, and may have adverse effects on obesity risk. Few systematic reviews have examined early intervention strategies to limit TV or total screen time; none have examined strategies to discourage parents from putting TVs in their children's bedrooms or remove TVs if they are already there. In order to identify strategies to reduce TV viewing or total screen time among children <12 years of age, we conducted a systematic review of seven electronic databases to June 2011, using the terms "intervention" and "television," "media," or "screen time." Peer-reviewed intervention studies that reported frequencies of TV viewing or screen-media use in children under age 12 were eligible for inclusion. We identified 144 studies; 47 met our inclusion criteria. Twenty-nine achieved significant reductions in TV viewing or screen-media use. Studies utilizing electronic TV monitoring devices, contingent feedback systems, and clinic-based counseling were most effective. While studies have reduced screen-media use in children, there are several research gaps, including a relative paucity of studies targeting young children (n = 13) or minorities (n = 14), limited long-term (>6 month) follow-up data (n = 5), and few (n = 4) targeting removing TVs from children's bedrooms. Attention to these issues may help increase the effectiveness of existing strategies for screen time reduction and extend them to different populations.

  1. Use of Six Sigma Methodology to Reduce Appointment Lead-Time in Obstetrics Outpatient Department.

    PubMed

    Ortiz Barrios, Miguel A; Felizzola Jiménez, Heriberto

    2016-10-01

    This paper focuses on the issue of longer appointment lead-time in the obstetrics outpatient department of a maternal-child hospital in Colombia. Because of extended appointment lead-time, women with high-risk pregnancy could develop severe complications in their health status and put their babies at risk. This problem was detected through a project selection process explained in this article and to solve it, Six Sigma methodology has been used. First, the process was defined through a SIPOC diagram to identify its input and output variables. Second, six sigma performance indicators were calculated to establish the process baseline. Then, a fishbone diagram was used to determine the possible causes of the problem. These causes were validated with the aid of correlation analysis and other statistical tools. Later, improvement strategies were designed to reduce appointment lead-time in this department. Project results evidenced that average appointment lead-time reduced from 6,89 days to 4,08 days and the deviation standard dropped from 1,57 days to 1,24 days. In this way, the hospital will serve pregnant women faster, which represents a risk reduction of perinatal and maternal mortality.

  2. 25 CFR 900.135 - May the time frames for action set out in this subpart be reduced?

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 25 Indians 2 2010-04-01 2010-04-01 false May the time frames for action set out in this subpart be... EDUCATION ASSISTANCE ACT Construction § 900.135 May the time frames for action set out in this subpart be reduced? Yes. The time frames in this subpart are intended to be maximum times and may be reduced based...

  3. 25 CFR 900.135 - May the time frames for action set out in this subpart be reduced?

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 25 Indians 2 2011-04-01 2011-04-01 false May the time frames for action set out in this subpart be... EDUCATION ASSISTANCE ACT Construction § 900.135 May the time frames for action set out in this subpart be reduced? Yes. The time frames in this subpart are intended to be maximum times and may be reduced based...

  4. Macrotextured spoked surfaces reduce the residence time of a bouncing Leidenfrost drop

    NASA Astrophysics Data System (ADS)

    Patterson, Colin J.; Shiri, Samira; Bird, James C.

    2017-02-01

    Liquid drops can bounce when they impact non-wetting surfaces. Recently, studies have demonstrated that the time that the bouncing drop contacts a superhydrophobic surface can be reduced by incorporating ridged macrotextures on the surface. Yet the existing models aimed at explaining this phenomenon offer incompatible predictions of the contact time when a drop impacts multiple intersecting macrotextures, or spokes. Furthermore, it is unclear whether the effects of the macrotexture on the drop hydrodynamics extend to non-wetting surfaces in which direct contact is avoided by a thin vapor layer. Here we demonstrate that the phenomenon observed for macrotextured, superhydrophobic surfaces extends to macrotextured, wettable surfaces above the Leidenfrost temperature. We show that the number of droplets and overall residence time both depend on the number of intersecting spokes. Finally, we compare and contrast our results with mechanistic models to rationalize various elements of the phenomenon.

  5. Aligning work and circadian time in shift workers improves sleep and reduces circadian disruption.

    PubMed

    Vetter, Céline; Fischer, Dorothee; Matera, Joana L; Roenneberg, Till

    2015-03-30

    Sleep loss and circadian disruption-a state of misalignment between physiological functions and imposed sleep/wake behavior-supposedly play central roles in the etiology of shift work-related pathologies [1-4]. Circadian entrainment is, however, highly individual [5], resulting in different chronotypes [6, 7]. Chronotype in turn modulates the effects of working times: compared to late chronotypes, earlier ones sleep worse and shorter and show higher levels of circadian misalignment during night shifts, while late types experience more sleep and circadian disruption than early types when working morning shifts [8]. To promote sleep and reduce the mismatch between circadian and working time, we implemented a chronotype-adjusted (CTA) shift schedule in a factory. We abolished the most strenuous shifts for extreme chronotypes (i.e., mornings for late chronotypes, nights for early ones) and examined whether sleep duration and quality, social jetlag [9, 10], wellbeing, subjective stress perception, and satisfaction with leisure time improved in this schedule. Intermediate chronotypes (quartiles 2 and 3) served as a control group, still working morning (6:00-14:00), evening (14:00-22:00), and night (22:00-6:00) shifts, with two strenuous shifts (out of twelve per month) replaced by evening ones. We observed a significant increase of self-reported sleep duration and quality, along with increased wellbeing ratings on workdays among extreme chronotypes. The CTA schedule reduced overall social jetlag by 1 hr, did not alter stress levels, and increased satisfaction with leisure time (early types only). Chronotype-based schedules thus can reduce circadian disruption and improve sleep; potential long-term effects on health and economic indicators need to be elucidated in future studies.

  6. Reducing time spent by junior doctors on call performing routine tasks at weekends

    PubMed Central

    Ibrahim, Wadah; Lin, Simeng

    2013-01-01

    At the Northern General Hospital, there are sixteen medical wards, spread over approximately half a mile. Weekend care for inpatients on these wards is provided by a team of four junior doctors, of different levels of training. We undertook a quality improvement project to reduce the amount of time junior doctors spent performing routine tasks at weekends. This may increase their available time for direct patient care. The study was performed over a period of nine weeks on two medical wards - Diabetes & Endocrine (W1) and Care of the Elderly Rehabilitation (W2). We monitored the bleeps received by the covering junior doctors during the weekend daytime shifts from the two study wards. We noted that a proportion of bleeps were routine tasks that could have been performed during weekday working hours. We also noted that W2 recorded fewer bleeps than W1 ward. This seemed to be because W2 batched junior doctors' jobs together. Firstly, we attempted to reduce the amount of routine work left undone each weekend. We provided a poster to remind Junior Doctors to complete such work during the week. Secondly, on W1 we replicated the job-batching system already in place on W2. A Doctors' Book was introduced in which nursing staff recorded the tasks that needed doing. This saved them from having to bleep the doctor repeatedly. The two changes resulted in a reduction in the number of bleeps generated by each ward and the number of visits required by the Junior Doctors to W1. Simple changes can reduce the amount of time junior doctors spend performing routine work at weekends. We implemented two such changes and achieved a reduction in the number of bleeps experienced by junior doctors and the number of times they had to return to one ward. PMID:26734161

  7. Conditions for making direct reduced iron, transition direct reduced iron and pig iron nuggets in a laboratory furnace - Temperature-time transformations

    SciTech Connect

    Anameric, B.; Kawatra, S.K.

    2007-02-15

    The pig iron nugget process is gaining in importance as an alternative to the traditional blast furnace. Throughout the process, self-reducing-fluxing dried greenballs composed of iron ore concentrate, reducing-carburizing agent (coal), flux (limestone) and binder (bentonite) are heat-treated. During the heat treatment, dried greenballs are first transformed into direct reduced iron (DRI), then to transition direct reduced iron (TDRI) and finally to pig iron nuggets. The furnace temperature and/or residence time and the corresponding levels of carburization, reduction and metallization dictate these transformations. This study involved the determination of threshold furnace temperatures and residence times for completion of all of the transformation reactions and pig iron nugget production. The experiments involved the heat treatment of self-reducing-fluxing dried greenballs at various furnace temperatures and residence times. The products of these heat treatments were identified by utilizing optical microscopy, apparent density and microhardness measurements.

  8. Impacts of clustering on noise-induced spiking regularity in the excitatory neuronal networks of subnetworks

    PubMed Central

    Li, Huiyan; Sun, Xiaojuan; Xiao, Jinghua

    2015-01-01

    In this paper, we investigate how clustering factors influent spiking regularity of the neuronal network of subnetworks. In order to do so, we fix the averaged coupling probability and the averaged coupling strength, and take the cluster number M, the ratio of intra-connection probability and inter-connection probability R, the ratio of intra-coupling strength and inter-coupling strength S as controlled parameters. With the obtained simulation results, we find that spiking regularity of the neuronal networks has little variations with changing of R and S when M is fixed. However, cluster number M could reduce the spiking regularity to low level when the uniform neuronal network's spiking regularity is at high level. Combined the obtained results, we can see that clustering factors have little influences on the spiking regularity when the entire energy is fixed, which could be controlled by the averaged coupling strength and the averaged connection probability. PMID:26217216

  9. Impacts of clustering on noise-induced spiking regularity in the excitatory neuronal networks of subnetworks.

    PubMed

    Li, Huiyan; Sun, Xiaojuan; Xiao, Jinghua

    2015-01-01

    In this paper, we investigate how clustering factors influent spiking regularity of the neuronal network of subnetworks. In order to do so, we fix the averaged coupling probability and the averaged coupling strength, and take the cluster number M, the ratio of intra-connection probability and inter-connection probability R, the ratio of intra-coupling strength and inter-coupling strength S as controlled parameters. With the obtained simulation results, we find that spiking regularity of the neuronal networks has little variations with changing of R and S when M is fixed. However, cluster number M could reduce the spiking regularity to low level when the uniform neuronal network's spiking regularity is at high level. Combined the obtained results, we can see that clustering factors have little influences on the spiking regularity when the entire energy is fixed, which could be controlled by the averaged coupling strength and the averaged connection probability.

  10. A computerized standard protocol order entry for pediatric inpatient acute seizure emergencies reduces time to treatment.

    PubMed

    Xie, Yi; Morgan, Robin; Schiff, Linda; Hannah, Debbie; Wheless, James

    2014-02-01

    Time to treatment of seizures is critical to efficacy. We performed a quality initiative and evaluated time to treatment of inpatient seizure emergencies with first- and second-line medicines before and after implementation of a computerized, standard treatment protocol. Data from 125 patients revealed that 179 seizure episodes required first-line antiepileptic drugs, and the mean time to treatment was 7.72 minutes. In 87 episodes, patients (49%) received the drugs within 5 minutes. Forty-six episodes required second-line drugs. In 17 (37%), patients received them within 30 minutes (mean 49.48 minutes). After implementation of the protocol, the mean time to treatment with first-line drugs was 3.74 minutes, a reduction of >50% (P < .0001). The mean time to treatment with second-line drugs was 25.05 minutes, a reduction of ∼50% (P < .0001). This effective model for reducing the time to treatment of seizure emergencies may be useful to similar institutions.

  11. Effectiveness of a Myocardial Infarction Protocol in Reducing Door-to-Ballon Time

    PubMed Central

    Correia, Luis Cláudio Lemos; Brito, Mariana; Kalil, Felipe; Sabino, Michael; Garcia, Guilherme; Ferreira, Felipe; Matos, Iracy; Jacobs, Peter; Ronzoni, Liliana; Noya-Rabelo, Márcia

    2013-01-01

    Background An adequate door-to-balloon time (<120 minutes) is the necessary condition for the efficacy of primary angioplasty in infarction to translate into effectiveness. Objective To describe the effectiveness of a quality of care protocol in reducing the door-to-balloon time. Methods Between May 2010 and August 2012, all individuals undergoing primary angioplasty in our hospital were analyzed. The door time was electronically recorded at the moment the patient took a number to be evaluated in the emergency room, which occurred prior to filling the check-in forms and to the triage. The balloon time was defined as the beginning of artery opening (introduction of the first device). The first 5 months of monitoring corresponded to the period of pre-implementation of the protocol. The protocol comprised the definition of a flowchart of actions from patient arrival at the hospital, the team's awareness raising in relation to the prioritization of time, and provision of a periodic feedback on the results and possible inadequacies. Results A total of 50 individuals were assessed. They were divided into five groups of 10 sequential patients (one group pre-and four groups post-protocol). The door-to-balloon time regarding the 10 cases recorded before protocol implementation was 200 ± 77 minutes. After protocol implementation, there was a progressive reduction of the door-to-balloon time to 142 ± 78 minutes in the first 10 patients, then to 150 ± 50 minutes, 131 ± 37 minutes and, finally, 116 ± 29 minutes in the three sequential groups of 10 patients, respectively. Linear regression between sequential patients and the door-to-balloon time (r = - 0.41) showed a regression coefficient of - 1.74 minutes. Conclusion The protocol implementation proved effective in the reduction of the door-to-balloon time. PMID:23702814

  12. A "Last Word" on Ice Spikes.

    ERIC Educational Resources Information Center

    Perry, Helene F.

    1995-01-01

    Attempts an explanation of how "ice spikes" are formed. The spikes are upward protrusions of ice that occur when water expands as it cools in a rigid container of low thermal conductivity. Describes the results of an investigation and includes color photos. (LZ)

  13. 5 CFR 831.612 - Election at time of retirement of a fully reduced annuity or a partially reduced annuity to...

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 5 Administrative Personnel 2 2010-01-01 2010-01-01 false Election at time of retirement of a fully reduced annuity or a partially reduced annuity to provide a former spouse annuity. 831.612 Section 831.612 Administrative Personnel OFFICE OF PERSONNEL MANAGEMENT (CONTINUED) CIVIL SERVICE REGULATIONS (CONTINUED) RETIREMENT Survivor...

  14. 5 CFR 842.604 - Election at time of retirement of a fully reduced annuity or a one-half reduced annuity to...

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 5 Administrative Personnel 2 2010-01-01 2010-01-01 false Election at time of retirement of a fully reduced annuity or a one-half reduced annuity to provide a former spouse annuity. 842.604 Section 842.604 Administrative Personnel OFFICE OF PERSONNEL MANAGEMENT (CONTINUED) CIVIL SERVICE REGULATIONS (CONTINUED) FEDERAL EMPLOYEES...

  15. Methodological issues about techniques for the spiking of standard OECD soil with nanoparticles: evidence of different behaviours

    NASA Astrophysics Data System (ADS)