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Sample records for reduces spike timing

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

    PubMed

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

    2007-09-20

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

  2. Spike-timing-dependent construction.

    PubMed

    Lightheart, Toby; Grainger, Steven; Lu, Tien-Fu

    2013-10-01

    Spike-timing-dependent construction (STDC) is the production of new spiking neurons and connections in a simulated neural network in response to neuron activity. Following the discovery of spike-timing-dependent plasticity (STDP), significant effort has gone into the modeling and simulation of adaptation in spiking neural networks (SNNs). Limitations in computational power imposed by network topology, however, constrain learning capabilities through connection weight modification alone. Constructive algorithms produce new neurons and connections, allowing automatic structural responses for applications of unknown complexity and nonstationary solutions. A conceptual analogy is developed and extended to theoretical conditions for modeling synaptic plasticity as network construction. Generalizing past constructive algorithms, we propose a framework for the design of novel constructive SNNs and demonstrate its application in the development of simulations for the validation of developed theory. Potential directions of future research and applications of STDC for biological modeling and machine learning are also discussed.

  3. 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.

  4. The spike timing dependence of plasticity

    PubMed Central

    Feldman, Daniel E.

    2012-01-01

    In spike timing-dependent plasticity (STDP), the order and precise temporal interval between presynaptic and postsynaptic spikes determine the sign and magnitude of long-term potentiation (LTP) or depression (LTD). STDP is widely utilized in models of circuit-level plasticity, development, and learning. However, spike timing is just one of several factors (including firing rate, synaptic cooperativity, and depolarization) that govern plasticity induction, and its relative importance varies across synapses and activity regimes. This review summarizes the forms, cellular mechanisms, and prevalence of STDP, and evaluates the evidence that spike timing is an important determinant of plasticity in vivo. PMID:22920249

  5. 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.

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

    PubMed Central

    Higgs, Matthew H; Spain, William J

    2011-01-01

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

  7. Motor control by precisely timed spike patterns

    PubMed Central

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

    2017-01-01

    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. PMID:28100491

  8. 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.

  9. 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

  10. 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

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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

  17. 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

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. Evoking prescribed spike times in stochastic neurons

    NASA Astrophysics Data System (ADS)

    Doose, Jens; Lindner, Benjamin

    2017-09-01

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

  3. Spectral Analysis of Input Spike Trains by Spike-Timing-Dependent Plasticity

    PubMed Central

    Gilson, Matthieu; Fukai, Tomoki; Burkitt, Anthony N.

    2012-01-01

    Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, the corresponding computation in the case of arbitrary input signals is still unclear. This paper provides an overarching picture of the algorithm inherent to STDP, tying together many previous results for commonly used models of pairwise STDP. For a single neuron with plastic excitatory synapses, we show how STDP performs a spectral analysis on the temporal cross-correlograms between its afferent spike trains. The postsynaptic responses and STDP learning window determine kernel functions that specify how the neuron “sees” the input correlations. We thus denote this unsupervised learning scheme as ‘kernel spectral component analysis’ (kSCA). In particular, the whole input correlation structure must be considered since all plastic synapses compete with each other. We find that kSCA is enhanced when weight-dependent STDP induces gradual synaptic competition. For a spiking neuron with a “linear” response and pairwise STDP alone, we find that kSCA resembles principal component analysis (PCA). However, plain STDP does not isolate correlation sources in general, e.g., when they are mixed among the input spike trains. In other words, it does not perform independent component analysis (ICA). Tuning the neuron to a single correlation source can be achieved when STDP is paired with a homeostatic mechanism that reinforces the competition between synaptic inputs. Our results suggest that neuronal networks equipped with STDP can process signals encoded in the transient spiking activity at the timescales of tens of milliseconds for usual STDP. PMID:22792056

  4. 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

  5. Google Searches for "Cheap Cigarettes" Spike at Tax Increases: Evidence from an Algorithm to Detect Spikes in Time Series Data.

    PubMed

    Caputi, Theodore L

    2017-06-22

    Online cigarette dealers have lower prices than brick-and-mortar retailers and advertise tax-free status 1-8. Previous studies show smokers search out these online alternatives at the time of a cigarette tax increase 9-10. However, these studies rely upon researchers' decision to consider a specific date and preclude the possibility that researchers focus on the wrong date. The purpose of this study is to introduce an unbiased methodology to the field of observing search patterns and to use this methodology to determine whether smokers search Google for "cheap cigarettes" at cigarette tax increases and, if so, whether the increased level of searches persists. Publicly available data from Google Trends is used to observe standardized search volumes for the term, "cheap cigarettes." Seasonal Hybrid Extreme Studentized Deviate and E-Divisive with Means tests were performed to observe spikes and mean level shifts in search volume. Of the twelve cigarette tax increases studied, ten showed spikes in searches for "cheap cigarettes" within two weeks of the tax increase. However, the mean level shifts did not occur for any cigarette tax increase. Searches for "cheap cigarettes" spike around the time of a cigarette tax increase, but the mean level of searches does not shift in response to a tax increase. The SHESD and EDM tests are unbiased methodologies that can be used to identify spikes and mean level shifts in time series data without an a priori date to be studied. SHESD and EDM affirm spikes in interest are related to tax increases. Applies improved statistical techniques (SHESD and EDM) to Google search data related to cigarettes, reducing bias and increasing powerContributes to the body of evidence that state and federal tax increases are associated with spikes in searches for cheap cigarettes and may be good dates for increased online health messaging related to tobacco.

  6. 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.

  7. Computing Complex Visual Features with Retinal Spike Times

    PubMed Central

    Sompolinsky, Haim; Meister, Markus

    2013-01-01

    Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times. PMID:23301021

  8. A comparison of learning abilities of spiking networks with different spike timing-dependent plasticity forms

    NASA Astrophysics Data System (ADS)

    Sboev, Alexander; Vlasov, Danila; Serenko, Alexey; Rybka, Roman; Moloshnikov, Ivan

    2016-02-01

    A study of possibility to model the learning process on base of different forms of timing-dependent plasticity (STDP) was performed. It is shown that the learning ability depends on the choice of spike pairing scheme and the type of input signal used for learning. The comparison of performance of several STDP rules along with several neuron models (leaky integrate-and-fire, static, Izhikevich and Hodgkin-Huxley) was carried out using the NEST simulator. The combinations of input signal and STDP spike pairing scheme, which demonstrate the best learning abilities, were extracted.

  9. 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

  10. Requirement of dendritic calcium spikes for induction of spike-timing-dependent synaptic plasticity

    PubMed Central

    Kampa, Björn M; Letzkus, Johannes J; Stuart, Greg J

    2006-01-01

    Spike-timing-dependent synaptic plasticity (STDP) by definition requires the temporal association of pre- and postsynaptic action potentials (APs). Yet, in cortical pyramidal neurons pairing unitary EPSPs with single APs at low frequencies is ineffective at generating plasticity. Using recordings from synaptically coupled layer 5 pyramidal neurons, we show here that high-frequency (200 Hz) postsynaptic AP bursts, rather than single APs, are required for both long-term potentiation (LTP) induction and NMDA channel activation during EPSP–AP pairing at low frequencies. Furthermore, we find that AP bursts can lead to LTP induction and NMDA channel activation during EPSP–AP pairing at both positive and negative times. High-frequency AP bursts generated supralinear calcium signals in basal dendrites suggesting the generation of dendritic calcium spikes, as has been observed previously in apical dendrites during AP burst firing at frequencies greater than 100 Hz. Consistent with a role of these dendritic calcium spikes in LTP induction, pairing EPSPs with low frequency (50 Hz) AP bursts was ineffective in generating LTP. Furthermore, supralinear calcium signals in basal dendrites during AP bursts were blocked by low concentrations of the T- and R-type calcium channel antagonist nickel, which also blocked LTP and NMDA channel activation. These data suggest an important role of dendritic calcium spikes during AP bursts in determining both the efficacy and time window for STDP induction. PMID:16675489

  11. 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.

  12. Multiple spike time patterns occur at bifurcation points of membrane potential dynamics.

    PubMed

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

    2012-01-01

    The response of a neuron to repeated somatic fluctuating current injections in vitro can elicit a reliable and precisely timed sequence of action potentials. The set of responses obtained across trials can also be interpreted as the response of an ensemble of similar neurons receiving the same input, with the precise spike times representing synchronous volleys that would be effective in driving postsynaptic neurons. To study the reproducibility of the output spike times for different conditions that might occur in vivo, we somatically injected aperiodic current waveforms into cortical neurons in vitro and systematically varied the amplitude and DC offset of the fluctuations. As the amplitude of the fluctuations was increased, reliability increased and the spike times remained stable over a wide range of values. However, at specific values called bifurcation points, large shifts in the spike times were obtained in response to small changes in the stimulus, resulting in multiple spike patterns that were revealed using an unsupervised classification method. Increasing the DC offset, which mimicked an overall increase in network background activity, also revealed bifurcation points and increased the reliability. Furthermore, the spike times shifted earlier with increasing offset. Although the reliability was reduced at bifurcation points, a theoretical analysis showed that the information about the stimulus time course was increased because each of the spike time patterns contained different information about the input.

  13. Multiple Spike Time Patterns Occur at Bifurcation Points of Membrane Potential Dynamics

    PubMed Central

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

    2012-01-01

    The response of a neuron to repeated somatic fluctuating current injections in vitro can elicit a reliable and precisely timed sequence of action potentials. The set of responses obtained across trials can also be interpreted as the response of an ensemble of similar neurons receiving the same input, with the precise spike times representing synchronous volleys that would be effective in driving postsynaptic neurons. To study the reproducibility of the output spike times for different conditions that might occur in vivo, we somatically injected aperiodic current waveforms into cortical neurons in vitro and systematically varied the amplitude and DC offset of the fluctuations. As the amplitude of the fluctuations was increased, reliability increased and the spike times remained stable over a wide range of values. However, at specific values called bifurcation points, large shifts in the spike times were obtained in response to small changes in the stimulus, resulting in multiple spike patterns that were revealed using an unsupervised classification method. Increasing the DC offset, which mimicked an overall increase in network background activity, also revealed bifurcation points and increased the reliability. Furthermore, the spike times shifted earlier with increasing offset. Although the reliability was reduced at bifurcation points, a theoretical analysis showed that the information about the stimulus time course was increased because each of the spike time patterns contained different information about the input. PMID:23093916

  14. 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

  15. 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.

  16. Reading spike timing without a clock: intrinsic decoding of spike trains.

    PubMed

    Panzeri, Stefano; Ince, Robin A A; Diamond, Mathew E; Kayser, Christoph

    2014-03-05

    The precise timing of action potentials of sensory neurons relative to the time of stimulus presentation carries substantial sensory information that is lost or degraded when these responses are summed over longer time windows. However, it is unclear whether and how downstream networks can access information in precise time-varying neural responses. Here, we review approaches to test the hypothesis that the activity of neural populations provides the temporal reference frames needed to decode temporal spike patterns. These approaches are based on comparing the single-trial stimulus discriminability obtained from neural codes defined with respect to network-intrinsic reference frames to the discriminability obtained from codes defined relative to the experimenter's computer clock. Application of this formalism to auditory, visual and somatosensory data shows that information carried by millisecond-scale spike times can be decoded robustly even with little or no independent external knowledge of stimulus time. In cortex, key components of such intrinsic temporal reference frames include dedicated neural populations that signal stimulus onset with reliable and precise latencies, and low-frequency oscillations that can serve as reference for partitioning extended neuronal responses into informative spike patterns.

  17. Hippocampal Spike-Timing Correlations Lead to Hexagonal Grid Fields

    NASA Astrophysics Data System (ADS)

    Monsalve-Mercado, Mauro M.; Leibold, Christian

    2017-07-01

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

  18. 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.

  19. 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

  20. 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

  1. 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.

  2. Time to first spike in stochastic Hodgkin Huxley systems

    NASA Astrophysics Data System (ADS)

    Tuckwell, Henry C.; Wan, Frederic Y. M.

    2005-06-01

    The time to first spike is an experimentally observed quantity in laboratory experiments. In the auditory, somatic and visual sensory modalities, the times of first spikes in the corresponding cortical neurons have been implicated as coding much of the information about stimulus properties. We describe an analytical approach for determining the time to first spike from a given initial state which may be applied to a general nonlinear stochastic model neuron. We illustrate with a standard Hodgkin-Huxley model with a Gaussian white-noise input current whose drift parameter is μ and whose variance parameter is σ. Partial differential equations (PDEs) of second order are obtained for the first two moments of the time taken for the depolarization to reach a threshold value from rest state, as functions of the initial values. Simulation confirms that for small noise amplitudes a 2-component model is reasonably accurate. For small values of the noise parameter σ, including the deterministic case σ=0, perturbation methods are used to find the moments of the firing time and the results compare favorably with those from simulation. The approach is accurate for almost all σ when μ is above threshold for action potentials in the absence of noise and over a considerable range of values of σ when μ is as small as 2. The same methods may be applied to models similar to Hodgkin-Huxley which involve channels for additional or different ionic currents.

  3. Precise spike timing dynamics of hippocampal place cell activity sensitive to cholinergic disruption.

    PubMed

    Newman, Ehren L; Venditto, Sarah Jo C; Climer, Jason R; Petter, Elijah A; Gillet, Shea N; Levy, Sam

    2017-10-01

    New memory formation depends on both the hippocampus and modulatory effects of acetylcholine. The mechanism by which acetylcholine levels in the hippocampus enable new encoding remains poorly understood. Here, we tested the hypothesis that cholinergic modulation supports memory formation by leading to structured spike timing in the hippocampus. Specifically, we tested if phase precession in dorsal CA1 was reduced under the influence of a systemic cholinergic antagonist. Unit and field potential were recorded from the dorsal CA1 of rats as they completed laps on a circular track for food rewards before and during the influence of the systemically administered acetylcholine muscarinic receptor antagonist scopolamine. We found that scopolamine significantly reduced phase precession of spiking relative to the field theta, and that this was due to a decrease in the frequency of the spiking rhythmicity. We also found that the correlation between position and theta phase was significantly reduced. This effect was not due to changes in spatial tuning as tuning remained stable for those cells analyzed. Similarly, it was not due to changes in lap-to-lap reliability of spiking onset or offset relative to either position or phase as the reliability did not decrease following scopolamine administration. These findings support the hypothesis that memory impairments that follow muscarinic blockade are the result of degraded spike timing in the hippocampus. © 2017 The Authors. Hippocampus Published by Wiley Periodicals, Inc.

  4. Time-resolved and time-scale adaptive measures of spike train synchrony.

    PubMed

    Kreuz, Thomas; Chicharro, Daniel; Greschner, Martin; Andrzejak, Ralph G

    2011-01-30

    A wide variety of approaches to estimate the degree of synchrony between two or more spike trains have been proposed. One of the most recent methods is the ISI-distance which extracts information from the interspike intervals (ISIs) by evaluating the ratio of the instantaneous firing rates. In contrast to most previously proposed measures it is parameter free and time-scale independent. However, it is not well suited to track changes in synchrony that are based on spike coincidences. Here we propose the SPIKE-distance, a complementary measure which is sensitive to spike coincidences but still shares the fundamental advantages of the ISI-distance. In particular, it is easy to visualize in a time-resolved manner and can be extended to a method that is also applicable to larger sets of spike trains. We show the merit of the SPIKE-distance using both simulated and real data. Copyright © 2010 Elsevier B.V. All rights reserved.

  5. 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.

  6. Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

    PubMed Central

    Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus

    2017-01-01

    Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation. PMID:28596730

  7. Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.

    PubMed

    Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus

    2017-01-01

    Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

  8. 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.

  9. 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.

  10. 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.

  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. Adaptation of spike timing precision controls the sensitivity to interaural time difference in the avian auditory brainstem

    PubMed Central

    Higgs, Matthew H.; Kuznetsova, Marina S.; Spain, William J.

    2012-01-01

    While adaptation is widely thought to facilitate neural coding, the form of adaptation should depend on how the signals are encoded. Monaural neurons early in the interaural time difference (ITD) pathway encode the phase of sound input using spike timing rather than firing rate. Such neurons in chicken nucleus magnocellularis (NM) adapt to ongoing stimuli by increasing firing rate and decreasing spike timing precision. We measured NM neuron responses while adapting them to simulated physiological input, and used these responses to construct inputs to binaural coincidence detector neurons in nucleus laminaris (NL). Adaptation of spike timing in NM reduced ITD sensitivity in NL, demonstrating the dominant role of timing in the short-term plasticity as well as the immediate response of this sound localization circuit. PMID:23115186

  13. Formation and regulation of dynamic patterns in two-dimensional spiking neural circuits with spike-timing-dependent plasticity.

    PubMed

    Palmer, John H C; Gong, Pulin

    2013-11-01

    Spike-timing-dependent plasticity (STDP) is an important synaptic dynamics that is capable of shaping the complex spatiotemporal activity of neural circuits. In this study, we examine the effects of STDP on the spatiotemporal patterns of a spatially extended, two-dimensional spiking neural circuit. We show that STDP can promote the formation of multiple, localized spiking wave patterns or multiple spike timing sequences in a broad parameter space of the neural circuit. Furthermore, we illustrate that the formation of these dynamic patterns is due to the interaction between the dynamics of ongoing patterns in the neural circuit and STDP. This interaction is analyzed by developing a simple model able to capture its essential dynamics, which give rise to symmetry breaking. This occurs in a fundamentally self-organizing manner, without fine-tuning of the system parameters. Moreover, we find that STDP provides a synaptic mechanism to learn the paths taken by spiking waves and modulate the dynamics of their interactions, enabling them to be regulated. This regulation mechanism has error-correcting properties. Our results therefore highlight the important roles played by STDP in facilitating the formation and regulation of spiking wave patterns that may have crucial functional roles in brain information processing.

  14. 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

  15. Spike timing analysis in neural networks with unsupervised synaptic plasticity

    NASA Astrophysics Data System (ADS)

    Mizusaki, B. E. P.; Agnes, E. J.; Brunnet, L. G.; Erichsen, R., Jr.

    2013-01-01

    The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.

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

    PubMed

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

    2015-07-01

    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. 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 25-50 pairings and re-emerges for 5

  17. 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

  18. 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

  19. 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.

  20. 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

  1. A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

    NASA Astrophysics Data System (ADS)

    Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

    2017-06-01

    Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

  2. 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

  3. 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.

  4. 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

  5. The Applicability of Spike Time Dependent Plasticity to Development

    PubMed Central

    Butts, Daniel A.; Kanold, Patrick O.

    2010-01-01

    Spike time dependent plasticity (STDP) has been observed in both developing and adult animals. Theoretical studies suggest that it implicitly leads to both competition and homeostasis in addition to correlation-based plasticity, making it a good candidate to explain developmental refinement and plasticity in a number of systems. However, it has only been observed to play a clear role in development in a small number of cases. Because the fast time scales necessary to elicit STDP, it would likely be inefficient in governing synaptic modifications in the absence of fast correlations in neural activity. In contrast, later stages of development often depend on sensory inputs that can drive activity on much faster time scales, suggesting a role in STDP in many sensory systems after opening of the eyes and ear canals. Correlations on fast time scales can be also be present earlier in developing microcircuits, such as those produced by specific transient “teacher” circuits in the cerebral cortex. By reviewing examples of each case, we suggest that STDP is not a universal rule, but rather might be masked or phased in, depending on the information available to instruct refinement in different developing circuits. Thus, this review describes selected cases where STDP has been studied in developmental contexts, and uses these examples to suggest a more general framework for understanding where it could be playing a role in development. PMID:21423516

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. A Computational Study of Spike Time Reliability in Two Types of Threshold Dynamics

    PubMed Central

    2013-01-01

    Spike time reliability (STR) refers to the phenomenon in which repetitive applications of a frozen copy of one stochastic signal to a neuron trigger spikes with reliable timing while a constant signal fails to do so. Observed and explored in numerous experimental and theoretical studies, STR is a complex dynamic phenomenon depending on the nature of external inputs as well as intrinsic properties of a neuron. The neuron under consideration could be either quiescent or spontaneously spiking in the absence of the external stimulus. Focusing on the situation in which the unstimulated neuron is quiescent but close to a switching point to oscillations, we numerically analyze STR treating each spike occurrence as a time localized event in a model neuron. We study both the averaged properties as well as individual features of spike-evoking epochs (SEEs). The effects of interactions between spikes is minimized by selecting signals that generate spikes with relatively long interspike intervals (ISIs). Under these conditions, the frequency content of the input signal has little impact on STR. We study two distinct cases, Type I in which the f–I relation (f for frequency, I for applied current) is continuous and Type II where the f–I relation exhibits a jump. STR in the two types shows a number of similar features and differ in some others. SEEs that are capable of triggering spikes show great variety in amplitude and time profile. On average, reliable spike timing is associated with an accelerated increase in the “action” of the signal as a threshold for spike generation is approached. Here, “action” is defined as the average amount of current delivered during a fixed time interval. When individual SEEs are studied, however, their time profiles are found important for triggering more precisely timed spikes. The SEEs that have a more favorable time profile are capable of triggering spikes with higher precision even at lower action levels. PMID:23945258

  12. 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

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

    PubMed

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

    2016-02-27

    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.

  14. 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.

  15. 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

  16. 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

  17. 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

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

    PubMed

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

    2015-02-01

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

  19. 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

  20. A history of spike-timing-dependent plasticity.

    PubMed

    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.

  1. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

    PubMed Central

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns. PMID:26900845

  2. 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

  3. 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

  4. 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

  5. Associative memory based on synchronized firing of spiking neurons with time-delayed interactions

    NASA Astrophysics Data System (ADS)

    Yoshioka, Masahiko; Shiino, Masatoshi

    1998-09-01

    We study associative memory of a neural network of spiking neurons with time-delayed synaptic interactions incorporating the time taken by an action potential to propagate along the axon. Individual spiking neurons are described by a set of nonlinear differential equations capable of exhibiting excitability such as that of Hodgkin-Huxley and FitzHugh neurons. When a simple learning rule of the autocorrelation type based on random patterns is assumed, memory retrieval is shown to be accompanied by synchronized firing of neurons. The reduced dynamics with a few degrees of freedom of the network with a finite number of stored patterns is analytically derived in the limit of infinitely many neurons. The dependence of the appearance of retrieval states on the distribution of time delay and on the size of refractory period given implicitly in the model is obtained, showing good agreement between the result of numerical simulations and that obtained from the reduced dynamics. The behavior of the network with an extensive number of patterns is also investigated and an approximate analysis is presented to discuss the storage capacity.

  6. What can neuromorphic event-driven precise timing add to spike-based pattern recognition?

    PubMed

    Akolkar, Himanshu; Meyer, Cedric; Clady, Zavier; Marre, Olivier; Bartolozzi, Chiara; Panzeri, Stefano; Benosman, Ryad

    2015-03-01

    This letter introduces a study to precisely measure what an increase in spike timing precision can add to spike-driven pattern recognition algorithms. The concept of generating spikes from images by converting gray levels into spike timings is currently at the basis of almost every spike-based modeling of biological visual systems. The use of images naturally leads to generating incorrect artificial and redundant spike timings and, more important, also contradicts biological findings indicating that visual processing is massively parallel, asynchronous with high temporal resolution. A new concept for acquiring visual information through pixel-individual asynchronous level-crossing sampling has been proposed in a recent generation of asynchronous neuromorphic visual sensors. Unlike conventional cameras, these sensors acquire data not at fixed points in time for the entire array but at fixed amplitude changes of their input, resulting optimally sparse in space and time-pixel individually and precisely timed only if new, (previously unknown) information is available (event based). This letter uses the high temporal resolution spiking output of neuromorphic event-based visual sensors to show that lowering time precision degrades performance on several recognition tasks specifically when reaching the conventional range of machine vision acquisition frequencies (30-60 Hz). The use of information theory to characterize separability between classes for each temporal resolution shows that high temporal acquisition provides up to 70% more information that conventional spikes generated from frame-based acquisition as used in standard artificial vision, thus drastically increasing the separability between classes of objects. Experiments on real data show that the amount of information loss is correlated with temporal precision. Our information-theoretic study highlights the potentials of neuromorphic asynchronous visual sensors for both practical applications and theoretical

  7. 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

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. Successive spike times predicted by a stochastic neuronal model with a variable input signal.

    PubMed

    D'Onofrio, Giuseppe; Pirozzi, Enrica

    2016-06-01

    Two different stochastic processes are used to model the evolution of the membrane voltage of a neuron exposed to a time-varying input signal. The first process is an inhomogeneous Ornstein-Uhlenbeck process and its first passage time through a constant threshold is used to model the first spike time after the signal onset. The second process is a Gauss-Markov process identified by a particular mean function dependent on the first passage time of the first process. It is shown that the second process is also of a diffusion type. The probability density function of the maximum between the first passage time of the first and the second process is considered to approximate the distribution of the second spike time. Results obtained by simulations are compared with those following the numerical and asymptotic approximations. A general equation to model successive spike times is given. Finally, examples with specific input signals are provided.

  18. 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.

  19. 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.

  20. Identification of time-varying neural dynamics from spike train data using multiwavelet basis functions.

    PubMed

    Xu, Song; Li, Yang; Guo, Qi; Yang, Xiao-Feng; Chan, Rosa H M

    2017-02-15

    Tracking the changes of neural dynamics based on neuronal spiking activities is a critical step to understand the neurobiological basis of learning from behaving animals. These dynamical neurobiological processes associated with learning are also time-varying, which makes the modeling problem challenging. We developed a novel multiwavelet-based time-varying generalized Laguerre-Volterra (TVGLV) modeling framework to study the time-varying neural dynamical systems using natural spike train data. By projecting the time-varying parameters in the TVGLV model onto a finite sequence of multiwavelet basis functions, the time-varying identification problem is converted into a time invariant linear-in-the-parameters one. An effective forward orthogonal regression (FOR) algorithm aided by mutual information (MI) criterion is then applied for the selection of significant model regressors or terms and the refinement of model structure. A generalized linear model fit approach is finally employed for parameter estimation from spike train data. The proposed multiwavelet-based TVGLV approach is used to identify both synthetic input-output spike trains and spontaneous retinal spike train recordings. The proposed method gives excellent the performance of tracking either sharply or slowly changing parameters with high sensitivity and accuracy regardless of the a priori knowledge of spike trains, which these results indicate that the proposed method is shown to deal well with spike train data. The proposed multiwavelet-based TVGLV approach was compared with several state-of-art parametric estimation methods like the steepest descent point process filter (SDPPF) or Chebyshev polynomial expansion method. The conventional SDPPF algorithm, or SDPPF with B-splines wavelet expansion method was shown to have the poor performance of tracking the time-varying system changes with the synthetic spike train data due to the slow convergence of the adaptive filter methods. Although the Chebyshev

  1. 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

  2. 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.

  3. Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

    PubMed

    Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

    2014-09-30

    This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Vibrational resonance in adaptive small-world neuronal networks with spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

    The phenomenon of vibrational resonance is investigated in adaptive Newman-Watts small-world neuronal networks, where the strength of synaptic connections between neurons is modulated based on spike-timing-dependent plasticity. Numerical results demonstrate that there exists appropriate amplitude of high-frequency driving which is able to optimize the neural ensemble response to the weak low-frequency periodic signal. The effect of networked vibrational resonance can be significantly affected by spike-timing-dependent plasticity. It is shown that spike-timing-dependent plasticity with dominant depression can always improve the efficiency of vibrational resonance, and a small adjusting rate can promote the transmission of weak external signal in small-world neuronal networks. In addition, the network topology plays an important role in the vibrational resonance in spike-timing-dependent plasticity-induced neural systems, where the system response to the subthreshold signal is maximized by an optimal network structure. Furthermore, it is demonstrated that the introduction of inhibitory synapses can considerably weaken the phenomenon of vibrational resonance in the hybrid small-world neuronal networks with spike-timing-dependent plasticity.

  5. 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

  6. 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

  7. 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

  8. 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.

  9. Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony?

    PubMed Central

    Knoblauch, Andreas; Hauser, Florian; Gewaltig, Marc-Oliver; Körner, Edgar; Palm, Günther

    2012-01-01

    Spike synchronization is thought to have a constructive role for feature integration, attention, associative learning, and the formation of bidirectionally connected Hebbian cell assemblies. By contrast, theoretical studies on spike-timing-dependent plasticity (STDP) report an inherently decoupling influence of spike synchronization on synaptic connections of coactivated neurons. For example, bidirectional synaptic connections as found in cortical areas could be reproduced only by assuming realistic models of STDP and rate coding. We resolve this conflict by theoretical analysis and simulation of various simple and realistic STDP models that provide a more complete characterization of conditions when STDP leads to either coupling or decoupling of neurons firing in synchrony. In particular, we show that STDP consistently couples synchronized neurons if key model parameters are matched to physiological data: First, synaptic potentiation must be significantly stronger than synaptic depression for small (positive or negative) time lags between presynaptic and postsynaptic spikes. Second, spike synchronization must be sufficiently imprecise, for example, within a time window of 5–10 ms instead of 1 ms. Third, axonal propagation delays should not be much larger than dendritic delays. Under these assumptions synchronized neurons will be strongly coupled leading to a dominance of bidirectional synaptic connections even for simple STDP models and low mean firing rates at the level of spontaneous activity. PMID:22936909

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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

  18. Minimum Requirements for Accurate and Efficient Real-Time On-Chip Spike Sorting

    PubMed Central

    Navajas, Joaquin; Barsakcioglu, Deren Y.; Eftekhar, Amir; Jackson, Andrew; Constandinou, Timothy G.; Quiroga, Rodrigo Quian

    2014-01-01

    Background Extracellular recordings are performed by inserting electrodes in the brain, relaying the signals to external power-demanding devices, where spikes are detected and sorted in order to identify the firing activity of different putative neurons. A main caveat of these recordings is the necessity of wires passing through the scalp and skin in order to connect intracortical electrodes to external amplifiers. The aim of this paper is to evaluate the feasibility of an implantable platform (i.e. a chip) with the capability to wirelessly transmit the neural signals and perform real-time on-site spike sorting. New Method We computationally modelled a two-stage implementation for online, robust, and efficient spike sorting. In the first stage, spikes are detected on-chip and streamed to an external computer where mean templates are created and sent back to the chip. In the second stage, spikes are sorted in real-time through template matching. Results We evaluated this procedure using realistic simulations of extracellular recordings and describe a set of specifications that optimise performance while keeping to a minimum the signal requirements and the complexity of the calculations. Comparison with Existing Methods A key bottleneck for the development of long-term BMIs is to find an inexpensive method for real-time spike sorting. Here, we simulated a solution to this problem that uses both offline and online processing of the data. Conclusions Hardware implementations of this method therefore enable low-power long-term wireless transmission of multiple site extracellular recordings, with application to wireless BMIs or closed-loop stimulation designs. PMID:24769170

  19. 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

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

    PubMed Central

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

    2016-01-01

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

  1. 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.

  2. Transfer entropy in continuous time, with applications to jump and neural spiking processes

    NASA Astrophysics Data System (ADS)

    Spinney, Richard E.; Prokopenko, Mikhail; Lizier, Joseph T.

    2017-03-01

    Transfer entropy has been used to quantify the directed flow of information between source and target variables in many complex systems. While transfer entropy was originally formulated in discrete time, in this paper we provide a framework for considering transfer entropy in continuous time systems, based on Radon-Nikodym derivatives between measures of complete path realizations. To describe the information dynamics of individual path realizations, we introduce the pathwise transfer entropy, the expectation of which is the transfer entropy accumulated over a finite time interval. We demonstrate that this formalism permits an instantaneous transfer entropy rate. These properties are analogous to the behavior of physical quantities defined along paths such as work and heat. We use this approach to produce an explicit form for the transfer entropy for pure jump processes, and highlight the simplified form in the specific case of point processes (frequently used in neuroscience to model neural spike trains). Finally, we present two synthetic spiking neuron model examples to exhibit the pertinent features of our formalism, namely, that the information flow for point processes consists of discontinuous jump contributions (at spikes in the target) interrupting a continuously varying contribution (relating to waiting times between target spikes). Numerical schemes based on our formalism promise significant benefits over existing strategies based on discrete time formalisms.

  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. Augmented feedback reduces ground reaction forces in the landing phase of the volleyball spike jump.

    PubMed

    Cronin, John B; Bressel, Eadric; Fkinn, Loren

    2008-05-01

    Frequency and magnitude of ground reaction forces (GRF) have been implicated in causing injuries such as "jumpers knee." To investigate whether a single session of augmented feedback concerning landing technique would decrease GRF. Pretest posttest experimental design. University biomechanics laboratory. Fifteen female Division 1 intercollegiate volleyball players. Participants were required to land on a force platform after spiking a volleyball from a four-step approach before and after an intervention involving visual and aural augmented feedback on correct jumping and landing technique. Mediolateral (ML), anterioposterior (AP), and vertical (V) GRF normalized to body weight (BW). Augmented feedback was found to significantly (P = 0.01) decrease VGRF by 23.6% but not ML (25%, P = 0.16) and AP (4.9%, P = 0.40) peak GRF. A single session of augmented feedback may be effective in reducing VGRF in collegiate athletes.

  5. Measuring multisensory integration: from reaction times to spike counts.

    PubMed

    Colonius, Hans; Diederich, Adele

    2017-06-08

    A neuron is categorized as "multisensory" if there is a statistically significant difference between the response evoked, e.g., by a crossmodal stimulus combination and that evoked by the most effective of its components separately. Being responsive to multiple sensory modalities does not guarantee that a neuron has actually engaged in integrating its multiple sensory inputs: it could simply respond to the stimulus component eliciting the strongest response in a given trial. Crossmodal enhancement is commonly expressed as a proportion of the strongest mean unisensory response. This traditional index does not take into account any statistical dependency between the sensory channels under crossmodal stimulation. We propose an alternative index measuring by how much the multisensory response surpasses the level obtainable by optimally combining the unisensory responses, with optimality defined as probability summation under maximal negative stochastic dependence. The new index is analogous to measuring crossmodal enhancement in reaction time studies by the strength of violation of the "race model inequality', a numerical measure of multisensory integration. Since the new index tends to be smaller than the traditional one, neurons previously labeled as "multisensory' may lose that property. The index is easy to compute and it is sensitive to variability in data.

  6. 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

  7. 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…

  8. 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

  9. 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…

  10. Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series

    PubMed Central

    Zeldenrust, Fleur; de Knecht, Sicco; Wadman, Wytse J.; Denève, Sophie; Gutkin, Boris

    2017-01-01

    Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., “the neural code”) lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new (in vitro) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing “sensory stimulus”: the hidden state. The sum of this network activity is injected as current input into the neuron under investigation. The mutual information between the hidden state on the one hand and spike trains of the artificial network or the recorded spike train on the other hand can easily be estimated due to the binary shape of the hidden state. The characteristics of the input current, such as the time constant as a result of the (dis)appearance rate of the hidden state or the amplitude of the input current (the firing frequency of the neurons in the artificial network), can independently be varied. As an example, we apply this method to pyramidal neurons in the CA1 of mouse hippocampi and compare the recorded spike trains to the optimal response of the “Bayesian neuron” (BN). We conclude that like in the BN, information transfer in hippocampal pyramidal cells is non-linear and amplifying: the information loss between the artificial input and the output spike train is high if the input to the neuron (the firing of the artificial network) is not very informative about the hidden state. If the input to the neuron does contain a lot of information about the hidden state, the information loss is low. Moreover, neurons increase their firing rates in case the (dis)appearance rate is high, so that the (relative) amount of transferred information stays constant. PMID:28663729

  11. 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.

  12. 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…

  13. 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…

  14. Essential role of axonal VGSC inactivation in time-dependent deceleration and unreliability of spike propagation at cerebellar Purkinje cells

    PubMed Central

    2014-01-01

    Background The output of the neuronal digital spikes is fulfilled by axonal propagation and synaptic transmission to influence postsynaptic cells. Similar to synaptic transmission, spike propagation on the axon is not secure, especially in cerebellar Purkinje cells whose spiking rate is high. The characteristics, mechanisms and physiological impacts of propagation deceleration and infidelity remain elusive. The spike propagation is presumably initiated by local currents that raise membrane potential to the threshold of activating voltage-gated sodium channels (VGSC). Results We have investigated the natures of spike propagation and the role of VGSCs in this process by recording spikes simultaneously on the somata and axonal terminals of Purkinje cells in cerebellar slices. The velocity and fidelity of spike propagation decreased during long-lasting spikes, to which the velocity change was more sensitive than fidelity change. These time-dependent deceleration and infidelity of spike propagation were improved by facilitating axonal VGSC reactivation, and worsen by intensifying VGSC inactivation. Conclusion Our studies indicate that the functional status of axonal VGSCs is essential to influencing the velocity and fidelity of spike propagation. PMID:24382121

  15. 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.

  16. Timing of sound evoked potentials and spike responses in the inferior colliculus of awake bats

    PubMed Central

    Voytenko, S.V.; Galazyuk, A.V.

    2008-01-01

    Neurons in the inferior colliculus (IC), one of the major integrative centers of the auditory system, process acoustic information converging from almost all nuclei of the auditory brain stem. During this integration, excitatory and inhibitory inputs arrive to auditory neurons at different time delays. Result of this integration determines timing of IC neuron firing. In the mammalian IC, the range of the first spike latencies is very large (5 – 50 ms). At present, a contribution of excitatory and inhibitory inputs in controlling neurons’ firing in the IC is still under debate. In the present study we assess the role of excitation and inhibition in determining first spike response latency in the IC. Postsynaptic responses were recorded to pure tones presented at neuron’s characteristic frequency or to downward FM sweeps in awake bats. There are three main results emerging from the present study: (1) the most common response pattern in the IC is hyperpolarization followed by depolarization followed by hyperpolarization (2) latencies of depolarizing or hyperpolarizing responses to tonal stimuli are short (3 to 7 ms) whereas the first spike latencies may vary to a great extent (4 to 26 ms) from one neuron to another (3) high threshold hyperpolarization preceded long latency spikes in IC neurons exhibiting paradoxical latency shift (PLS). Our data also show that the onset hyperpolarizing potentials in the IC have very small jitter (<100 μs) across repeated stimulus presentations. The results of this study suggest that inhibition, arriving earlier than excitation, may play a role as a mechanism for delaying the first spike latency in IC neurons. PMID:18621102

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

    PubMed Central

    Zheng, Y.

    2013-01-01

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

  18. Asynchronous inputs alter excitability, spike timing, and topography in primary auditory cortex

    PubMed Central

    Pandya, Pritesh K.; Moucha, Raluca; Engineer, Navzer D.; Rathbun, Daniel L.; Vazquez, Jessica; Kilgard, Michael P.

    2010-01-01

    Correlation-based synaptic plasticity provides a potential cellular mechanism for learning and memory. Studies in the visual and somatosensory systems have shown that behavioral and surgical manipulation of sensory inputs leads to changes in cortical organization that are consistent with the operation of these learning rules. In this study, we examine how the organization of primary auditory cortex (A1) is altered by tones designed to decrease the average input correlation across the frequency map. After one month of separately pairing nucleus basalis stimulation with 2 and 14 kHz tones, a greater proportion of A1 neurons responded to frequencies below 2 kHz and above 14 kHz. Despite the expanded representation of these tones, cortical excitability was specifically reduced in the high and low frequency regions of A1, as evidenced by increased neural thresholds and decreased response strength. In contrast, in the frequency region between the two paired tones, driven rates were unaffected and spontaneous firing rate was increased. Neural response latencies were increased across the frequency map when nucleus basalis stimulation was associated with asynchronous activation of the high and low frequency regions of A1. This set of changes did not occur when pulsed noise bursts were paired with nucleus basalis stimulation. These results are consistent with earlier observations that sensory input statistics can shape cortical map organization and spike timing. PMID:15855025

  19. Distinct roles for I(T) and I(H) in controlling the frequency and timing of rebound spike responses.

    PubMed

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

    2011-11-15

    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 (I(T)) and hyperpolarization-activated HCN channels (I(H)) 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 I(T) and I(H) 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 I(T) and I(H) in a rebound response. We find that physiological levels of hyperpolarization make only small proportions of the total I(T) and I(H) 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 I(T)-mediated depolarization. An additional frequency increase is provided by I(H) in reducing the time constant and thus the extent of I(T) inactivation as the membrane returns from a hyperpolarized state to the resting level. An I(H)-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. I(T) and I(H) can thus be activated by physiologically relevant stimuli and have distinct roles in the frequency, timing and precision of rebound responses.

  20. On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex.

    PubMed

    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

  1. 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

  2. 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.

  3. 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.

  4. Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

    PubMed Central

    Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

    2013-01-01

    Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity. PMID:23592970

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

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

    PubMed Central

    Lisberger, Stephen G.

    2013-01-01

    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. PMID:23155171

  11. 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.

  12. 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.

  13. Mechanisms of Induction and Maintenance of Spike-Timing Dependent Plasticity in Biophysical Synapse Models

    PubMed Central

    Graupner, Michael; Brunel, Nicolas

    2010-01-01

    We review biophysical models of synaptic plasticity, with a focus on spike-timing dependent plasticity (STDP). The common property of the discussed models is that synaptic changes depend on the dynamics of the intracellular calcium concentration, which itself depends on pre- and postsynaptic activity. We start by discussing simple models in which plasticity changes are based directly on calcium amplitude and dynamics. We then consider models in which dynamic intracellular signaling cascades form the link between the calcium dynamics and the plasticity changes. Both mechanisms of induction of STDP (through the ability of pre/postsynaptic spikes to evoke changes in the state of the synapse) and of maintenance of the evoked changes (through bistability) are discussed. PMID:20948584

  14. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

    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.

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

    PubMed

    Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo

    2013-12-01

    How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. Probabilistic properties of neuron spiking time-series obtained in vivo

    NASA Astrophysics Data System (ADS)

    Bershadskii, A.; Dremencov, E.; Fukayama, D.; Yadid, G.

    2001-12-01

    Probabilistic properties of spiking time-series obtained in vivo from singular neurons belonging to Red Nucleus of brain are analyzed for two groups of rats: genetically defined rat model of depression (Flinders Sensitive Rat Line - FSL) and a control (healthy) group. The FSL group shows a distribution of interspike intervals with a much longer tail than that found for normal rats. The former distribution (for the FSL group) indicates a power-law with exponent α = - 1+/-0.1. A simple thermodynamic (noise) model is elaborated to explain obtained results.

  8. 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.

  9. 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

  10. Spiking Neurons Learning Phase Delays: How Mammals May Develop Auditory Time-Difference Sensitivity

    NASA Astrophysics Data System (ADS)

    Leibold, Christian; van Hemmen, J. Leo

    2005-04-01

    Time differences between the two ears are an important cue for animals to azimuthally locate a sound source. The first binaural brainstem nucleus, in mammals the medial superior olive, is generally believed to perform the necessary computations. Its cells are sensitive to variations of interaural time differences of about 10 μs. The classical explanation of such a neuronal time-difference tuning is based on the physical concept of delay lines. Recent data, however, are inconsistent with a temporal delay and rather favor a phase delay. By means of a biophysical model we show how spike-timing-dependent synaptic learning explains precise interplay of excitation and inhibition and, hence, accounts for a physical realization of a phase delay.

  11. Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

    PubMed

    Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

    2015-12-01

    We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

  12. 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.

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

    PubMed Central

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

    2011-01-01

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

  15. A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity.

    PubMed

    Rachmuth, Guy; Shouval, Harel Z; Bear, Mark F; Poon, Chi-Sang

    2011-12-06

    Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.

  16. Token-passing communication protocol in hardware-based real-time spiking neural networks

    NASA Astrophysics Data System (ADS)

    Belhadj, B.; Tomas, J.; Malot, O.; Bornat, Y.; N'Kaoua, G.; Renaud, S.

    2009-05-01

    Biological neural networks are based upon axonal point-to-point connections which inspire connectionist architecture. As we attempt to engineer ever larger analogues of these neural networks we are forced to multiplex neural signals over time shared paths. This can alter timing of neural information, which is critical in real-time oscillatory networks. Because shared paths induce extra delay due to multiplexing signals, traveling on the channel and passing through routing devices, guaranteeing event arrival deadlines across the communication process becomes crucial. This paper addresses issues related to the guarantee of event timings with arbitrary deadline constraints in real-time distributed spiking neural network systems based on token-ring architecture. To achieve this objective, we propose an integrated method in selecting key system parameters. We show that several parameters must be set carefully if event deadlines are to be satisfied. The token holding time (THT) parameter controls the bandwidth allocation for each node in the token-ring network, and must be set properly to avoid deadline misses. The target token rotation time (TTRT) determines both the speed of token circulation and the network utilization available to nodes. TTRT should also be chosen carefully to ensure that the token circulates fast enough while maintaining a high available utilization. As prove of concept, the proposed method is applied to a multi-board spiking neural network system hosting up to 140 analog neurons spread across 7 circuit-boards. Experimental analysis shows that deadline constraints are guaranteed along with bandwidth allocation fairness when applying the proposed method.

  17. Recovery cycle times of inferior colliculus neurons in the awake bat measured with spike counts and latencies

    PubMed Central

    Sayegh, Riziq; Aubie, Brandon; Fazel-Pour, Siavosh; Faure, Paul A.

    2012-01-01

    Neural responses in the mammalian auditory midbrain (inferior colliculus; IC) arise from complex interactions of synaptic excitation, inhibition, and intrinsic properties of the cell. Temporally selective duration-tuned neurons (DTNs) in the IC are hypothesized to arise through the convergence of excitatory and inhibitory synaptic inputs offset in time. Synaptic inhibition can be inferred from extracellular recordings by presenting pairs of pulses (paired tone stimulation) and comparing the evoked responses of the cell to each pulse. We obtained single unit recordings from the IC of the awake big brown bat (Eptesicus fuscus) and used paired tone stimulation to measure the recovery cycle times of DTNs and non-temporally selective auditory neurons. By systematically varying the interpulse interval (IPI) of the paired tone stimulus, we determined the minimum IPI required for a neuron's spike count or its spike latency (first- or last-spike latency) in response to the second tone to recover to within ≥50% of the cell's baseline count or to within 1 SD of it's baseline latency in response to the first tone. Recovery times of shortpass DTNs were significantly shorter than those of bandpass DTNs, and recovery times of bandpass DTNs were longer than allpass neurons not selective for stimulus duration. Recovery times measured with spike counts were positively correlated with those measured with spike latencies. Recovery times were also correlated with first-spike latency (FSL). These findings, combined with previous studies on duration tuning in the IC, suggest that persistent inhibition is a defining characteristic of DTNs. Herein, we discuss measuring recovery times of neurons with spike counts and latencies. We also highlight how persistent inhibition could determine neural recovery times and serve as a potential mechanism underlying the precedence effect in humans. Finally, we explore implications of recovery times for DTNs in the context of bat hearing and

  18. Is a 4-bit synaptic weight resolution enough? - constraints on enabling spike-timing dependent plasticity in neuromorphic hardware.

    PubMed

    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.

  19. 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

  20. 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.

  1. 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

  2. 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).

  3. 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

  4. 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.

  5. 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.

  6. Predictions of Speech Chimaera Intelligibility Using Auditory Nerve Mean-Rate and Spike-Timing Neural Cues.

    PubMed

    Wirtzfeld, Michael R; Ibrahim, Rasha A; Bruce, Ian C

    2017-07-26

    Perceptual studies of speech intelligibility have shown that slow variations of acoustic envelope (ENV) in a small set of frequency bands provides adequate information for good perceptual performance in quiet, whereas acoustic temporal fine-structure (TFS) cues play a supporting role in background noise. However, the implications for neural coding are prone to misinterpretation because the mean-rate neural representation can contain recovered ENV cues from cochlear filtering of TFS. We investigated ENV recovery and spike-time TFS coding using objective measures of simulated mean-rate and spike-timing neural representations of chimaeric speech, in which either the ENV or the TFS is replaced by another signal. We (a) evaluated the levels of mean-rate and spike-timing neural information for two categories of chimaeric speech, one retaining ENV cues and the other TFS; (b) examined the level of recovered ENV from cochlear filtering of TFS speech; (c) examined and quantified the contribution to recovered ENV from spike-timing cues using a lateral inhibition network (LIN); and (d) constructed linear regression models with objective measures of mean-rate and spike-timing neural cues and subjective phoneme perception scores from normal-hearing listeners. The mean-rate neural cues from the original ENV and recovered ENV partially accounted for perceptual score variability, with additional variability explained by the recovered ENV from the LIN-processed TFS speech. The best model predictions of chimaeric speech intelligibility were found when both the mean-rate and spike-timing neural cues were included, providing further evidence that spike-time coding of TFS cues is important for intelligibility when the speech envelope is degraded.

  7. 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

  8. 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.

  9. Automatic identification of spike-wave events and non-convulsive seizures with a reduced set of electrodes.

    PubMed

    Jacquin, Arnaud; Causevic, Elvir; John, E Roy

    2007-01-01

    Epileptiform activity in the brain, whether localized or generalized, constitutes an important category of abnormal electroencephalogram (EEG). Seizures are episodes of relatively brief disturbances of mental, motor or sensory activity caused by paroxysmal cerebral activity. They are not always accompanied by the characteristic convulsions that we commonly associate with the word epilepsy. In this case, they may be referred to as non-convulsive status epilepticus (NCSE) or as absence seizures (formerly called "petit mal" seizures). They often manifest themselves in scalp-recorded EEG as large-amplitude spike-wave "patterns" (or "events"), usually occurring in bursts. If left undetected and untreated, they can potentially cause significant brain and behavioral dysfunctions, interfere with information processing, or otherwise contribute to altered mental status. In this paper, we describe an algorithm to be implemented in a prototype BrainScope_ED instrument meant to alert to a detected seizure in an emergency department (ED) or other clinical setting. BrainScope_ED uses a reduced electrode set (8 instead of 19). The proposed signal processing algorithm is based on the detection of spike-wave events obtained from a wavelet analysis of the EEG signal, combined with an analysis of the complexity of the EEG using fractal dimension estimates. We show that this algorithm has excellent sensitivity and specificity. In particular, the fractal analysis is a key factor in the removal of falsely detected spike-wave events (false positives) that can be caused by voluntary or involuntary artifacts such as fast eyelid flutter.

  10. 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

  11. 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

  12. Effect of spike-timing-dependent plasticity on coherence resonance and synchronization transitions by time delay in adaptive neuronal networks

    NASA Astrophysics Data System (ADS)

    Xie, Huijuan; Gong, Yubing; Wang, Qi

    2016-06-01

    In this paper, we numerically study how time delay induces multiple coherence resonance (MCR) and synchronization transitions (ST) in adaptive Hodgkin-Huxley neuronal networks with spike-timing dependent plasticity (STDP). It is found that MCR induced by time delay STDP can be either enhanced or suppressed as the adjusting rate Ap of STDP changes, and ST by time delay varies with the increase of Ap, and there is optimal Ap by which the ST becomes strongest. It is also found that there are optimal network randomness and network size by which ST by time delay becomes strongest, and when Ap increases, the optimal network randomness and optimal network size increase and related ST is enhanced. These results show that STDP can either enhance or suppress MCR and optimal STDP can enhance ST induced by time delay in the adaptive neuronal networks. These findings provide a new insight into STDP's role for the information processing and transmission in neural systems.

  13. Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity

    PubMed Central

    Osogami, Takayuki; Otsuka, Makoto

    2015-01-01

    An artificial neural network, such as a Boltzmann machine, can be trained with the Hebb rule so that it stores static patterns and retrieves a particular pattern when an associated cue is presented to it. Such a network, however, cannot effectively deal with dynamic patterns in the manner of living creatures. Here, we design a dynamic Boltzmann machine (DyBM) and a learning rule that has some of the properties of spike-timing dependent plasticity (STDP), which has been postulated for biological neural networks. We train a DyBM consisting of only seven neurons in a way that it memorizes the sequence of the bitmap patterns in an alphabetical image “SCIENCE” and its reverse sequence and retrieves either sequence when a partial sequence is presented as a cue. The DyBM is to STDP as the Boltzmann machine is to the Hebb rule. PMID:26374672

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. A reliable and time-saving semiautomatic spike-template-based analysis of interictal EEG-fMRI.

    PubMed

    Tousseyn, Simon; Dupont, Patrick; Robben, David; Goffin, Karolien; Sunaert, Stefan; Van Paesschen, Wim

    2014-12-01

    A prerequisite for the implementation of interictal electroencephalography-correlated functional magnetic resonance imaging (EEG-fMRI) in the presurgical work-up for epilepsy surgery is straightforward processing. We propose a new semi-automatic method as alternative for the challenging and time-consuming visual spike identification. Our method starts from a patient-specific spike-template, built by averaging spikes recorded on the EEG outside the scanner. Spatiotemporal cross-correlations between the template and the EEG measured during fMRI were calculated. To minimize false-positive detections, this time course of cross-correlations was binarized by means of a spike-template-specific threshold determined in healthy controls. To inform our model for statistical parametric mapping, this binarized regressor was convolved with the canonical hemodynamic response function. We validated our "template-based" method in 21 adult patients with refractory focal epilepsy with a well-defined epileptogenic zone and interictal spikes during EEG-fMRI. Sensitivity and specificity for detecting the epileptogenic zone were calculated and represented in receiver operating characteristic (ROC) curves. Our approach was compared with a previously proposed semiautomatic "topography-based" method that used the topographic amplitude distribution of spikes as a starting point for correlation-based fitting. Good diagnostic performance could be reached with our template-based method. The optimal area under the ROC curve was 0.77. Diagnostic performance of the topography-based method was overall low. Our new template-based method is more standardized and time-saving than visual spike identification on intra-scanner EEG recordings, and preserves good diagnostic performance for detecting the epileptogenic zone. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

  19. 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.

  20. 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

  1. Topological dynamics in spike-timing dependent plastic model neural networks

    PubMed Central

    Stone, David B.; Tesche, Claudia D.

    2013-01-01

    Spike-timing dependent plasticity (STDP) is a biologically constrained unsupervised form of learning that potentiates or depresses synaptic connections based on the precise timing of pre-synaptic and post-synaptic firings. The effects of on-going STDP on the topology of evolving model neural networks were assessed in 50 unique simulations which modeled 2 h of activity. After a period of stabilization, a number of global and local topological features were monitored periodically to quantify on-going changes in network structure. Global topological features included the total number of remaining synapses, average synaptic strengths, and average number of synapses per neuron (degree). Under a range of different input regimes and initial network configurations, each network maintained a robust and highly stable global structure across time. Local topology was monitored by assessing state changes of all three-neuron subgraphs (triads) present in the networks. Overall counts and the range of triad configurations varied little across the simulations; however, a substantial set of individual triads continued to undergo rapid state changes and revealed a dynamic local topology. In addition, specific small-world properties also fluctuated across time. These findings suggest that on-going STDP provides an efficient means of selecting and maintaining a stable yet flexible network organization. PMID:23616750

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. Time-frequency analysis of spike-wave discharges using a modified wavelet transform.

    PubMed

    Bosnyakova, Daria; Gabova, Alexandra; Kuznetsova, Galina; Obukhov, Yuri; Midzyanovskaya, Inna; Salonin, Dmitrij; van Rijn, Clementina; Coenen, Anton; Tuomisto, Leene; van Luijtelaar, Gilles

    2006-06-30

    The continuous Morlet wavelet transform was used for the analysis of the time-frequency pattern of spike-wave discharges (SWD) as can be recorded in a genetic animal model of absence epilepsy (rats of the WAG/Rij strain). We developed a new wavelet transform that allows to obtain the time-frequency dynamics of the dominating rhythm during the discharges. SWD were analyzed pre- and post-administration of certain drugs. SWD recorded predrug demonstrate quite uniform time-frequency dynamics of the dominant rhythm. The beginning of the discharge has a short period with the highest frequency value (up to 15 Hz). Then the frequency decreases to 7-9 Hz and frequency modulation occurs during the discharge in this range with a period of 0.5-0.7 s. Specific changes of SWD time-frequency dynamics were found after the administration of psychoactive drugs, addressing different brain mediator and modulator systems. Short multiple SWDs appeared under low (0.5 mg/kg) doses of haloperidol, they are characterized by a fast frequency decrease to 5-6 Hz at the end of every discharge. The frequency of the dominant frequency of SWD was not stable in long lasting SWD after 1.0 mg/kg or more haloperidol: then two periodicities were found. Long lasting SWD seen after the administration of vigabatrin showed a stable frequency of the discharge. The EEG after Ketamin showed a distinct 5 s quasiperiodicity. No clear changes of time-frequency dynamics of SWD were found after perilamine. It can be concluded that the use of the modified Morlet wavelet transform allows to describe significant parameters of the dynamics in the time-frequency domain of the dominant rhythm of SWD that were not previously detected.

  9. 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.

  10. 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

  11. Effects of spatiotemporal stimulus properties on spike timing correlations in owl monkey primary somatosensory cortex

    PubMed Central

    Pouget, Pierre; Qi, Hui-Xin; Zhou, Zhiyi; Bernard, Melanie R.; Burish, Mark J.; Kaas, Jon H.

    2012-01-01

    The correlated discharges of cortical neurons in primary somatosensory cortex are a potential source of information about somatosensory stimuli. One aspect of neuronal correlations that has not been well studied is how the spatiotemporal properties of tactile stimuli affect the presence and magnitude of correlations. We presented single- and dual-point stimuli with varying spatiotemporal relationships to the hands of three anesthetized owl monkeys and recorded neuronal activity from 100-electrode arrays implanted in primary somatosensory cortex. Correlation magnitudes derived from joint peristimulus time histogram (JPSTH) analysis of single neuron pairs were used to determine the level of spike timing correlations under selected spatiotemporal stimulus conditions. Correlated activities between neuron pairs were commonly observed, and the proportions of correlated pairs tended to decrease with distance between the recorded neurons. Distance between stimulus sites also affected correlations. When stimuli were presented simultaneously at two sites, ∼37% of the recorded neuron pairs showed significant correlations when adjacent phalanges were stimulated, and ∼21% of the pairs were significantly correlated when nonadjacent digits were stimulated. Spatial proximity of paired stimuli also increased the average correlation magnitude. Stimulus onset asynchronies in the paired stimuli had small effects on the correlation magnitude. These results show that correlated discharges between neurons at the first level of cortical processing provide information about the relative locations of two stimuli on the hand. PMID:23019003

  12. 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

  13. Detailed Characterization of Local Field Potential Oscillations and Their Relationship to Spike Timing in the Antennal Lobe of the Moth Manduca sexta

    PubMed Central

    Daly, Kevin C.; Galán, Roberto F.; Peters, Oakland J.; Staudacher, Erich M.

    2011-01-01

    The transient oscillatory model of odor identity encoding seeks to explain how odorants with spatially overlapped patterns of input into primary olfactory networks can be discriminated. This model provides several testable predictions about the distributed nature of network oscillations and how they control spike timing. To test these predictions, 16 channel electrode arrays were placed within the antennal lobe (AL) of the moth Manduca sexta. Unitary spiking and multi site local field potential (LFP) recordings were made during spontaneous activity and in response to repeated presentations of an odor panel. We quantified oscillatory frequency, cross correlations between LFP recording sites, and spike–LFP phase relationships. We show that odor-driven AL oscillations in Manduca are frequency modulating (FM) from ∼100 to 30 Hz; this was odorant and stimulus duration dependent. FM oscillatory responses were localized to one or two recording sites suggesting a localized (perhaps glomerular) not distributed source. LFP cross correlations further demonstrated that only a small (r < 0.05) distributed and oscillatory component was present. Cross spectral density analysis demonstrated the frequency of these weakly distributed oscillations was state dependent (spontaneous activity = 25–55 Hz; odor-driven = 55–85 Hz). Surprisingly, vector strength analysis indicated that unitary phase locking of spikes to the LFP was strongest during spontaneous activity and dropped significantly during responses. Application of bicuculline, a GABAA receptor antagonist, significantly lowered the frequency content of odor-driven distributed oscillatory activity. Bicuculline significantly reduced spike phase locking generally, but the ubiquitous pattern of increased phase locking during spontaneous activity persisted. Collectively, these results indicate that oscillations perform poorly as a stimulus-mediated spike synchronizing mechanism for Manduca and hence are

  14. 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

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. Spike Time-Dependent Plasticity Induced by Intra-Cortical Microstimulation in the Auditory Cortex

    NASA Astrophysics Data System (ADS)

    Takahashi, Hirokazu; Yokota, Ryo; Suzrikawa, Jun; Kanzaki, Ryohei

    Intrinsic plastic properties in the auditory cortex can cause dynamic remodeling of the functional organization according to trainings. Neurorehabilitation will therefore potentially benefit from electrical stimulation that can modify synaptic strength as desired. Here we show that the auditory cortex of rats can be modified by intracortical microstimulation (ICMS) associated with tone stimuli on the basis of the spike time-dependent plasticity (STDP). Two kinds of ICMS were applied; a pairing ICMS following a tone-induced excitatory synaptic input and an anti-paring ICMS preceding a tone-induced input. The pairing and anti-pairing ICMS produced potentiation and depression, respectively, in responses to the paired tones with a particular test frequency, and thereby modified the tuning property of the auditory cortical neurons. In addition, we demonstrated that our experimental setup has a potential to directly measure how anesthetic agents and pharmacological manipulation affect ICMS-induced plasticity, and thus will serve as a powerful platform to investigate the neural basis of the plasticity.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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.

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

    PubMed Central

    Luz, Yotam; Shamir, Maoz

    2016-01-01

    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. PMID

  11. 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.

  12. 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.

  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. Systems and methods for reducing transient voltage spikes in matrix converters

    SciTech Connect

    Kajouke, Lateef A.; Perisic, Milun; Ransom, Ray M.

    2013-06-11

    Systems and methods are provided for delivering energy using an energy conversion module that includes one or more switching elements. An exemplary electrical system comprises a DC interface, an AC interface, an isolation module, a first conversion module between the DC interface and the isolation module, and a second conversion module between the AC interface and the isolation module. A control module is configured to operate the first conversion module to provide an injection current to the second conversion module to reduce a magnitude of a current through a switching element of the second conversion module before opening the switching element.

  15. Spike Timing Matters in Novel Neuronal Code Involved in Vibrotactile Frequency Perception.

    PubMed

    Birznieks, Ingvars; Vickery, Richard M

    2017-05-22

    Skin vibrations sensed by tactile receptors contribute significantly to the perception of object properties during tactile exploration [1-4] and to sensorimotor control during object manipulation [5]. Sustained low-frequency skin vibration (<60 Hz) evokes a distinct tactile sensation referred to as flutter whose frequency can be clearly perceived [6]. How afferent spiking activity translates into the perception of frequency is still unknown. Measures based on mean spike rates of neurons in the primary somatosensory cortex are sufficient to explain performance in some frequency discrimination tasks [7-11]; however, there is emerging evidence that stimuli can be distinguished based also on temporal features of neural activity [12, 13]. Our study's advance is to demonstrate that temporal features are fundamental for vibrotactile frequency perception. Pulsatile mechanical stimuli were used to elicit specified temporal spike train patterns in tactile afferents, and subsequently psychophysical methods were employed to characterize human frequency perception. Remarkably, the most salient temporal feature determining vibrotactile frequency was not the underlying periodicity but, rather, the duration of the silent gap between successive bursts of neural activity. This burst gap code for frequency represents a previously unknown form of neural coding in the tactile sensory system, which parallels auditory pitch perception mechanisms based on purely temporal information where longer inter-pulse intervals receive higher perceptual weights than short intervals [14]. Our study also demonstrates that human perception of stimuli can be determined exclusively by temporal features of spike trains independent of the mean spike rate and without contribution from population response factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Monitoring spike train synchrony.

    PubMed

    Kreuz, Thomas; Chicharro, Daniel; Houghton, Conor; Andrzejak, Ralph G; Mormann, Florian

    2013-03-01

    Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.

  17. 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

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

    PubMed

    Bennett, James E M; Bair, Wyeth

    2015-08-01

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

  19. 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.

  20. 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.

  1. 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.

  2. From the statistics of connectivity to the statistics of spike times in neuronal networks.

    PubMed

    Ocker, Gabriel Koch; Hu, Yu; Buice, Michael A; Doiron, Brent; Josić, Krešimir; Rosenbaum, Robert; Shea-Brown, Eric

    2017-08-29

    An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles underlying collective spiking activity in neural circuits. The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network. The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity. We close by showing how these ideas, together with plasticity rules, can help to close the loop between network structure and activity statistics. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. 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

  4. High phosphate reduces host ability to develop arbuscular mycorrhizal symbiosis without affecting root calcium spiking responses to the fungus

    PubMed Central

    Balzergue, Coline; Chabaud, Mireille; Barker, David G.; Bécard, Guillaume; Rochange, Soizic F.

    2013-01-01

    The arbuscular mycorrhizal symbiosis associates soil fungi with the roots of the majority of plants species and represents a major source of soil phosphorus acquisition. Mycorrhizal interactions begin with an exchange of molecular signals between the two partners. A root signaling pathway is recruited, for which the perception of fungal signals triggers oscillations of intracellular calcium concentration. High phosphate availability is known to inhibit the establishment and/or persistence of this symbiosis, thereby favoring the direct, non-symbiotic uptake of phosphorus by the root system. In this study, Medicago truncatula plants were used to investigate the effects of phosphate supply on the early stages of the interaction. When plants were supplied with high phosphate fungal attachment to the roots was drastically reduced. An experimental system was designed to individually study the effects of phosphate supply on the fungus, on the roots, and on root exudates. These experiments revealed that the most important effects of high phosphate supply were on the roots themselves, which became unable to host mycorrhizal fungi even when these had been appropriately stimulated. The ability of the roots to perceive their fungal partner was then investigated by monitoring nuclear calcium spiking in response to fungal signals. This response did not appear to be affected by high phosphate supply. In conclusion, high levels of phosphate predominantly impact the plant host, but apparently not in its ability to perceive the fungal partner. PMID:24194742

  5. 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.

  6. Feature extraction using first and second derivative extrema (FSDE) for real-time and hardware-efficient spike sorting.

    PubMed

    Paraskevopoulou, Sivylla E; Barsakcioglu, Deren Y; Saberi, Mohammed R; Eftekhar, Amir; Constandinou, Timothy G

    2013-04-30

    Next generation neural interfaces aspire to achieve real-time multi-channel systems by integrating spike sorting on chip to overcome limitations in communication channel capacity. The feasibility of this approach relies on developing highly efficient algorithms for feature extraction and clustering with the potential of low-power hardware implementation. We are proposing a feature extraction method, not requiring any calibration, based on first and second derivative features of the spike waveform. The accuracy and computational complexity of the proposed method are quantified and compared against commonly used feature extraction methods, through simulation across four datasets (with different single units) at multiple noise levels (ranging from 5 to 20% of the signal amplitude). The average classification error is shown to be below 7% with a computational complexity of 2N-3, where N is the number of sample points of each spike. Overall, this method presents a good trade-off between accuracy and computational complexity and is thus particularly well-suited for hardware-efficient implementation. Copyright © 2013 Elsevier B.V. All rights reserved.

  7. 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

  8. Variability and coding efficiency of noisy neural spike encoders.

    PubMed

    Steinmetz, P N; Manwani, A; Koch, C

    2001-01-01

    Encoding synaptic inputs as a train of action potentials is a fundamental function of nerve cells. Although spike trains recorded in vivo have been shown to be highly variable, it is unclear whether variability in spike timing represents faithful encoding of temporally varying synaptic inputs or noise inherent in the spike encoding mechanism. It has been reported that spike timing variability is more pronounced for constant, unvarying inputs than for inputs with rich temporal structure. This could have significant implications for the nature of neural coding, particularly if precise timing of spikes and temporal synchrony between neurons is used to represent information in the nervous system. To study the potential functional role of spike timing variability, we estimate the fraction of spike timing variability which conveys information about the input for two types of noisy spike encoders--an integrate and fire model with randomly chosen thresholds and a model of a patch of neuronal membrane containing stochastic Na(+) and K(+) channels obeying Hodgkin-Huxley kinetics. The quality of signal encoding is assessed by reconstructing the input stimuli from the output spike trains using optimal linear mean square estimation. A comparison of the estimation performance of noisy neuronal models of spike generation enables us to assess the impact of neuronal noise on the efficacy of neural coding. The results for both models suggest that spike timing variability reduces the ability of spike trains to encode rapid time-varying stimuli. Moreover, contrary to expectations based on earlier studies, we find that the noisy spike encoding models encode slowly varying stimuli more effectively than rapidly varying ones.

  9. 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

  10. 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.

  11. Comparison of DNA Extraction Kits for Detection of Burkholderia pseudomallei in Spiked Human Whole Blood Using Real-Time PCR

    PubMed Central

    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 (CT) 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 5.5×104 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. PMID:23460920

  12. 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.

  13. 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

  14. 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.

  15. 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.

  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. 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.

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. Group I mGluR regulates the polarity of spike-timing dependent plasticity in substantia gelatinosa neurons.

    PubMed

    Jung, Sung Jun; Kim, Sang Jeong; Park, Yun Kyung; Oh, Seog Bae; Cho, Kwangwook; Kim, Jun

    2006-08-25

    The spinal synaptic plasticity is associated with a central sensitization of nociceptive input, which accounts for the generation of hyperalgesia in chronic pain. However, how group I metabotropic glutamate receptors (mGluRs) may operate spinal plasticity remains essentially unexplored. Here, we have identified spike-timing dependent synaptic plasticity in substantia gelatinosa (SG) neurons, using perforated patch-clamp recordings of SG neuron in a spinal cord slice preparation. In the presence of bicuculline and strychnine, long-term potentiation (LTP) was blocked by AP-5 and Ca2+ chelator BAPTA-AM. The group I mGluR antagonist AIDA, PLC inhibitor U-73122, and IP3 receptor blocker 2-APB shifted LTP to long-term depression (LTD) without affecting acute synaptic transmission. These findings provide a link between postsynaptic group I mGluR/PLC/IP3-gated Ca2+ store regulating the polarity of synaptic plasticity and spinal central sensitization.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. Space-Time Dynamics of Membrane Currents Evolve to Shape Excitation, Spiking, and Inhibition in the Cortex at Small and Large Scales.

    PubMed

    Roland, Per E

    2017-06-07

    In the cerebral cortex, membrane currents, i.e., action potentials and other membrane currents, express many forms of space-time dynamics. In the spontaneous asynchronous irregular state, their space-time dynamics are local non-propagating fluctuations and sparse spiking appearing at unpredictable positions. After transition to active spiking states, larger structured zones with active spiking neurons appear, propagating through the cortical network, driving it into various forms of widespread excitation, and engaging the network from microscopic scales to whole cortical areas. At each engaged cortical site, the amount of excitation in the network, after a delay, becomes matched by an equal amount of space-time fine-tuned inhibition that might be instrumental in driving the dynamics toward perception and action. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. 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

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

    PubMed

    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).

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. 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

  6. 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.

  7. The time window for generation of dendritic spikes by coincidence of action potentials and EPSPs is layer specific in somatosensory cortex.

    PubMed

    Ledergerber, Debora; Larkum, Matthew Evan

    2012-01-01

    The precise timing of events in the brain has consequences for intracellular processes, synaptic plasticity, integration and network behaviour. Pyramidal neurons, the most widespread excitatory neuron of the neocortex have multiple spike initiation zones, which interact via dendritic and somatic spikes actively propagating in all directions within the dendritic tree. For these neurons, therefore, both the location and timing of synaptic inputs are critical. The time window for which the backpropagating action potential can influence dendritic spike generation has been extensively studied in layer 5 neocortical pyramidal neurons of rat somatosensory cortex. Here, we re-examine this coincidence detection window for pyramidal cell types across the rat somatosensory cortex in layers 2/3, 5 and 6. We find that the time-window for optimal interaction is widest and shifted in layer 5 pyramidal neurons relative to cells in layers 6 and 2/3. Inputs arriving at the same time and locations will therefore differentially affect spike-timing dependent processes in the different classes of pyramidal neurons.

  8. 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.

  9. 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

  10. 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

  11. 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.

  12. 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.

  13. Feasibility of reducing older adults' sedentary time.

    PubMed

    Gardiner, Paul A; Eakin, Elizabeth G; Healy, Genevieve N; Owen, Neville

    2011-08-01

    Sedentary time (too much sitting, as distinct from lack of exercise) is a prevalent risk to health among older adults. Examine the feasibility of an intervention to reduce and break up sedentary time in older adults. A pre-experimental (pre-post) study. A total of 59 participants aged ≥60 years from Brisbane, Australia. Data were collected between May and December 2009 and analyzed in 2010. One face-to-face goal-setting consultation and one individually tailored mailing providing feedback on accelerometer-derived sedentary time, grounded in social cognitive theory and behavioral choice theory. Program reach and retention; changes in accelerometer-derived sedentary time, light-intensity physical activity (LIPA), and moderate-to-vigorous-intensity physical activity (MVPA) (assessed over 6 days in pre- and post-intervention periods); and participant satisfaction. Reach was 87.5% of those screened and eligible; retention was 100%. From pre- to post-intervention, participants decreased their sedentary time [-3.2% (95% CI= -4.18, -2.14), p<0.001], increased their breaks in sedentary time per day [4.0 (1.48, 6.52), p=0.003], and increased their LIPA [2.2% (1.40, 2.99), p<0.001] and MVPA [1.0% (0.55, 1.38), p<0.001]. Significantly greater reductions in sedentary time were made after 10:00am, with significantly greater number of breaks occurring between 7:00pm and 9:00pm. Participants reported high satisfaction with the program (median 9/10). Sedentary time in older adults can be reduced following a brief intervention based on goal setting and behavioral self-monitoring. Copyright © 2011 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

  14. 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.

  15. 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.

  16. 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.

  17. Spike timing-dependent plasticity: a learning rule for dendritic integration in rat CA1 pyramidal neurons

    PubMed Central

    Campanac, Emilie; Debanne, Dominique

    2008-01-01

    Long-term plasticity of dendritic integration is induced in parallel with long-term potentiation (LTP) or depression (LTD) based on presynaptic activity patterns. It is, however, not clear whether synaptic plasticity induced by temporal pairing of pre- and postsynaptic activity is also associated with synergistic modification in dendritic integration. We show here that the spike timing-dependent plasticity (STDP) rule accounts for long-term changes in dendritic integration in CA1 pyramidal neurons in vitro. Positively correlated pre- and postsynaptic activity (delay: +5/+50 ms) induced LTP and facilitated dendritic integration. Negatively correlated activity (delay: −5/−50 ms) induced LTD and depressed dendritic integration. These changes were not observed following positive or negative pairing with long delays (> ±50 ms) or when NMDA receptors were blocked. The amplitude–slope relation of the EPSP was facilitated after LTP and depressed after LTD. These effects could be mimicked by voltage-gated channel blockers, suggesting that the induced changes in EPSP waveform involve the regulation of voltage-gated channel activity. Importantly, amplitude–slope changes induced by STDP were found to be input specific, indicating that the underlying changes in excitability are restricted to a limited portion of the dendrites. We conclude that STDP is a common learning rule for long-term plasticity of both synaptic transmission and dendritic integration, thus constituting a form of functional redundancy that insures significant changes in the neuronal output when synaptic plasticity is induced. PMID:18048448

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. Analog modulation of spike-evoked transmission in CA3 circuits is determined by axonal Kv1.1 channels in a time-dependent manner.

    PubMed

    Bialowas, Andrzej; Rama, Sylvain; Zbili, Mickaël; Marra, Vincenzo; Fronzaroli-Molinieres, Laure; Ankri, Norbert; Carlier, Edmond; Debanne, Dominique

    2015-02-01

    Synaptic transmission usually depends on action potentials (APs) in an all-or-none (digital) fashion. Recent studies indicate, however, that subthreshold presynaptic depolarization may facilitate spike-evoked transmission, thus creating an analog modulation of spike-evoked synaptic transmission, also called analog-digital (AD) synaptic facilitation. Yet, the underlying mechanisms behind this facilitation remain unclear. We show here that AD facilitation at rat CA3-CA3 synapses is time-dependent and requires long presynaptic depolarization (5-10 s) for its induction. This depolarization-induced AD facilitation (d-ADF) is blocked by the specific Kv1.1 channel blocker dendrotoxin-K. Using fast voltage-imaging of the axon, we show that somatic depolarization used for induction of d-ADF broadened the AP in the axon through inactivation of Kv1.1 channels. Somatic depolarization enhanced spike-evoked calcium signals in presynaptic terminals, but not basal calcium. In conclusion, axonal Kv1.1 channels determine glutamate release in CA3 neurons in a time-dependent manner through the control of the presynaptic spike waveform. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  4. A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

    PubMed

    Wang, Quan; Rothkopf, Constantin A; Triesch, Jochen

    2017-08-01

    The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN

  5. A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity

    PubMed Central

    2017-01-01

    The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network’s changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network’s sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and

  6. 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

  7. 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

  8. 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.

  9. The Computational Structure of Spike Trains

    PubMed Central

    Haslinger, Robert; Klinkner, Kristina Lisa; Shalizi, Cosma Rohilla

    2010-01-01

    Neurons perform computations, and convey the results of those computations through the statistical structure of their output spike trains. Here we present a practical method, grounded in the information-theoretic analysis of prediction, for inferring a minimal representation of that structure and for characterizing its complexity. Starting from spike trains, our approach finds their causal state models (CSMs), the minimal hidden Markov models or stochastic automata capable of generating statistically-identical time series. We then use these CSMs to objectively quantify both the generalizable structure and the idiosyncratic randomness of the spike train. Specifically, we show that the expected algorithmic information content (the information needed to describe the spike train exactly) can be split into three parts describing (1) the time-invariant structure (complexity) of the minimal spike-generating process, which describes the spike train statistically, (2) the randomness (internal entropy rate) of the minimal spike-generating process, and (3) a residual pure noise term not described by the minimal spike generating process. We use CSMs to approximate each of these quantities. The CSMs are inferred non-parametrically from the data, making only mild regularity assumptions, via the Causal State Splitting Reconstruction (CSSR) algorithm. The methods presented here complement more traditional spike train analyses by describing not only spiking probability, and spike train entropy, but also the complexity of a spike train’s structure. We demonstrate our approach using both simulated spike trains and experimental data recorded in rat barrel cortex during vibrissa stimulation. PMID:19764880

  10. 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

  11. 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.

  12. 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.

  13. Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains

    PubMed Central

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

    2011-01-01

    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. PMID:22215995

  14. Scalable hybrid computation with spikes.

    PubMed

    Sarpeshkar, Rahul; O'Halloran, Micah

    2002-09-01

    We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moderate-precision analog units to collectively compute a precise answer to a computation. Second, frequent discrete signal restoration of the analog information prevents analog noise and offset from degrading the computation. And, third, a state machine enables complex computations to be created using a sequence of elementary computations. A natural choice for implementing this hybrid scheme is one based on spikes because spike-count codes are digital, while spike-time codes are analog. We illustrate how spikes afford easy ways to implement all three components of scalable hybrid computation. First, as an important example of distributed analog computation, we show how spikes can create a distributed modular representation of an analog number by implementing digital carry interactions between spiking analog neurons. Second, we show how signal restoration may be performed by recursive spike-count quantization of spike-time codes. And, third, we use spikes from an analog dynamical system to trigger state transitions in a digital dynamical system, which reconfigures the analog dynamical system using a binary control vector; such feedback interactions between analog and digital dynamical systems create a hybrid state machine (HSM). The HSM extends and expands the concept of a digital finite-state-machine to the hybrid domain. We present experimental data from a two-neuron HSM on a chip that implements error-correcting analog-to-digital conversion with the concurrent use of spike-time and spike-count codes. We also present experimental data from silicon circuits that implement HSM-based pattern recognition using spike-time synchrony. We outline how HSMs may be

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

    PubMed

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

    2016-09-29

    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

  16. 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

  17. Detecting joint pausiness in parallel spike trains.

    PubMed

    Gärtner, Matthias; Duvarci, Sevil; Roeper, Jochen; Schneider, Gaby

    2017-06-15

    Transient periods with reduced neuronal discharge - called 'pauses' - have recently gained increasing attention. In dopamine neurons, pauses are considered important teaching signals, encoding negative reward prediction errors. Particularly simultaneous pauses are likely to have increased impact on information processing. Available methods for detecting joint pausing analyze temporal overlap of pauses across spike trains. Such techniques are threshold dependent and can fail to identify joint pauses that are easily detectable by eye, particularly in spike trains with different firing rates. We introduce a new statistic called pausiness that measures the degree of synchronous pausing in spike train pairs and avoids threshold-dependent identification of specific pauses. A new graphic termed the cross-pauseogram compares the joint pausiness of two spike trains with its time shifted analogue, such that a (pausiness) peak indicates joint pausing. When assessing significance of pausiness peaks, we use a stochastic model with synchronous spikes to disentangle joint pausiness arising from synchronous spikes from additional 'joint excess pausiness' (JEP). Parameter estimates are obtained from auto- and cross-correlograms, and statistical significance is assessed by comparison to simulated cross-pauseograms. Our new method was applied to dopamine neuron pairs recorded in the ventral tegmental area of awake behaving mice. Significant JEP was detected in about 20% of the pairs. Given the neurophysiological importance of pauses and the fact that neurons integrate multiple inputs, our findings suggest that the analysis of JEP can reveal interesting aspects in the activity of simultaneously recorded neurons. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Preparation of Cr(VI) and Cr(III) isotopic spike solutions from 50Cr and 53Cr enriched oxides without the use of oxidizing and/or reducing agents.

    PubMed

    Novotnik, Breda; Zuliani, Tea; Martinčič, Anže; Ščančar, Janez; Milačič, Radmila

    2012-09-15

    The use of enriched stable isotopes as tracers in speciation procedures by ion-exchange chromatography coupled to ICP-MS enables to follow the oxidation-reduction processes of Cr. The most commonly available Cr stable isotopes are (50)Cr and (53)Cr enriched oxides or metallic Cr. For application of Cr enriched stable isotopes, adequate preparation of isotopic spike solutions is necessary. To ensure that Cr species present in the sample investigated are not compromised, no excess of the reducing neither oxidizing agents should remain in the isotopic spike solutions. Cr(VI) isotopic solutions are mostly prepared by dissolving of Cr oxide in HClO(4), followed by the addition of ammonia and H(2)O(2) to quantitatively oxidize Cr, while the excess of H(2)O(2) is removed by boiling or UV irradiation. If traces of H(2)O(2) still remains, such isotopic spike solution may cause artefacts in Cr speciation in the sample investigated. In the present work, new procedure based on alkaline melting of (50)Cr enriched oxide for preparation of pure (50)Cr(VI) spike solution was developed. Cr(III) was quantitatively oxidized to Cr(VI) with air oxygen without use of other oxidizing agents. Moreover, the microwave assisted digestion procedure of (53)Cr enriched oxide was applied for preparation of (53)Cr(III) spike solution without use of reducing agents. The purity of (50)Cr(VI) and (53)Cr(III) isotopic spike solutions was verified by the speciation analysis applying hyphenation of anion-exchange FPLC to ICP-MS. Speciation analysis demonstrated suitability of the proposed procedures for preparation of Cr isotopic spike solutions. In addition, the artefacts in Cr speciation, which may be initiated by traces of oxidizing and/or reducing agents present in Cr spike solutions, were demonstrated. The outcomes of our investigation highlighted the importance of the adequate preparation of spike solutions of Cr isotopes that may be used as reliable tracers in the investigations of the oxidation

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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…

  4. 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

  5. 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

  6. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2017-04-01

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

  8. 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.

  9. High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity.

    PubMed

    Franke, Felix; Jäckel, David; Dragas, Jelena; Müller, Jan; Radivojevic, Milos; Bakkum, Douglas; Hierlemann, Andreas

    2012-01-01

    Understanding plasticity of neural networks is a key to comprehending their development and function. A powerful technique to study neural plasticity includes recording and control of pre- and post-synaptic neural activity, e.g., by using simultaneous intracellular recording and stimulation of several neurons. Intracellular recording is, however, a demanding technique and has its limitations in that only a small number of neurons can be stimulated and recorded from at the same time. Extracellular techniques offer the possibility to simultaneously record from larger numbers of neurons with relative ease, at the expenses of increased efforts to sort out single neuronal activities from the recorded mixture, which is a time consuming and error prone step, referred to as spike sorting. In this mini-review, we describe recent technological developments in two separate fields, namely CMOS-based high-density microelectrode arrays, which also allow for extracellular stimulation of neurons, and real-time spike sorting. We argue that these techniques, when combined, will provide a powerful tool to study plasticity in neural networks consisting of several thousand neurons in vitro.

  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. 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

  12. 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.

  13. Comparison of Classifier Architectures for Online Neural Spike Sorting.

    PubMed

    Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

    2017-04-01

    High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

  14. Robust spike-train learning in spike-event based weight update.

    PubMed

    Shrestha, Sumit Bam; Song, Qing

    2017-09-12

    Supervised learning algorithms in a spiking neural network either learn a spike-train pattern for a single neuron receiving input spike-train from multiple input synapses or learn to output the first spike time in a feedforward network setting. In this paper, we build upon spike-event based weight update strategy to learn continuous spike-train in a spiking neural network with a hidden layer using a dead zone on-off based adaptive learning rate rule which ensures convergence of the learning process in the sense of weight convergence and robustness of the learning process to external disturbances. Based on different benchmark problems, we compare this new method with other relevant spike-train learning algorithms. The results show that the speed of learning is much improved and the rate of successful learning is also greatly improved. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. 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

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

    PubMed

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

    2013-06-01

    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. 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). 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. 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. 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

  18. 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

  19. 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.

  20. Learning Precise Spike Train-to-Spike Train Transformations in Multilayer Feedforward Neuronal Networks.

    PubMed

    Banerjee, Arunava

    2016-05-01

    We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron. This formulation sidesteps the need for spike alignment and leads to closed-form solutions for all quantities of interest. Second, virtual assignment of weights to spikes rather than synapses enables a perturbation analysis of individual spike times and synaptic weights of the output, as well as all intermediate neurons in the network, which yields the gradients of the error functional with respect to the said entities. Learning proceeds via a gradient descent mechanism that leverages these quantities. Simulation experiments demonstrate the efficacy of the proposed learning framework. The experiments also highlight asymmetries between synapses on excitatory and inhibitory neurons.

  1. 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.

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

    PubMed Central

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

    2013-01-01

    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. PMID:23574919

  3. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

    PubMed Central

    Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

  4. 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.

  5. 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

  6. 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.

  7. NASA F-15B #836 landing with Quiet Spike attached

    NASA Image and Video Library

    2006-10-03

    NASA F-15B #836 landing with Quiet Spike attached. The project seeks to verify the structural integrity of the multi-segmented, articulating spike attachment designed to reduce and control a sonic boom.

  8. 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

  9. 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.

  10. Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability.

    PubMed

    Li, Meng; Tsien, Joe Z

    2017-01-01

    A major stumbling block to cracking the real-time neural code is neuronal variability - neurons discharge spikes with enormous variability not only across trials within the same experiments but also in resting states. Such variability is widely regarded as a noise which is often deliberately averaged out during data analyses. In contrast to such a dogma, we put forth the Neural Self-Information Theory that neural coding is operated based on the self-information principle under which variability in the time durations of inter-spike-intervals (ISI), or neuronal silence durations, is self-tagged with discrete information. As the self-information processor, each ISI carries a certain amount of information based on its variability-probability distribution; higher-probability ISIs which reflect the balanced excitation-inhibition ground state convey minimal information, whereas lower-probability ISIs which signify rare-occurrence surprisals in the form of extremely transient or prolonged silence carry most information. These variable silence durations are naturally coupled with intracellular biochemical cascades, energy equilibrium and dynamic regulation of protein and gene expression levels. As such, this silence variability-based self-information code is completely intrinsic to the neurons themselves, with no need for outside observers to set any reference point as typically used in the rate code, population code and temporal code models. Moreover, temporally coordinated ISI surprisals across cell population can inherently give rise to robust real-time cell-assembly codes which can be readily sensed by the downstream neural clique assemblies. One immediate utility of this self-information code is a general decoding strategy to uncover a variety of cell-assembly patterns underlying external and internal categorical or continuous variables in an unbiased manner.

  11. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

    PubMed Central

    Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

    2015-01-01

    A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193

  12. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.

    PubMed

    Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

    2015-08-13

    A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

  13. 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

  14. 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.

  15. 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].

  16. 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

  17. Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding.

    PubMed

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

    2016-06-01

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

  18. Representing spike trains using constant sampling intervals.

    PubMed

    Hirata, Yoshito; Aihara, Kazuyuki

    2009-10-15

    Sensory neurons encode external information by a series of times of action potentials, which is called a spike train. However, since it is a point process, it is hard to analyze. Here we propose a method for converting a spike train into a real-valued time series with a fixed sampling interval under the assumption of temporal codes. The proposed method yields time series that represent encoded signals. Especially when the method is applied to spike trains generated using integrate-and-fire models, the yielded time series look very similar to those of encoded information. The method works robustly even when a spike train is contaminated with noise. Since unlike filters it does not use its original signals for the conversion, the proposed method can be widely used for investigating spike train data in the real world.

  19. Bayesian population decoding of spiking neurons.

    PubMed

    Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

    2009-01-01

    The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

  20. 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

  1. Reducing outpatient waiting time: a simulation modeling approach.

    PubMed

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

    2013-09-01

    The objective of this study was to provide a model for reducing outpatient waiting time by using simulation. 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. 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. 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.

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. Dendritic spikes veto inhibition.

    PubMed

    Stuart, Greg J

    2012-09-06

    How inhibition regulates dendritic excitability is critical to an understanding of the way neurons integrate the many thousands of synaptic inputs they receive. In this issue of Neuron, Müller et al. (2012) show that inhibition blocks the generation of weak dendritic spikes, leaving strong dendritic spikes intact. Copyright © 2012 Elsevier Inc. All rights reserved.

  9. 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.

  10. 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.

  11. 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.

  12. The relationship between seizures, interictal spikes and antiepileptic drugs.

    PubMed

    Goncharova, Irina I; Alkawadri, Rafeed; Gaspard, Nicolas; Duckrow, Robert B; Spencer, Dennis D; Hirsch, Lawrence J; Spencer, Susan S; Zaveri, Hitten P

    2016-09-01

    A considerable decrease in spike rate accompanies antiepileptic drug (AED) taper during intracranial EEG (icEEG) monitoring. Since spike rate during icEEG monitoring can be influenced by surgery to place intracranial electrodes, we studied spike rate during long-term scalp EEG monitoring to further test this observation. We analyzed spike rate, seizure occurrence and AED taper in 130 consecutive patients over an average of 8.9days (range 5-17days). We observed a significant relationship between time to the first seizure, spike rate, AED taper and seizure occurrence (F (3,126)=19.77, p<0.0001). A high spike rate was related to a longer time to the first seizure. Further, in a subset of 79 patients who experienced seizures on or after day 4 of monitoring, spike rate decreased initially from an on- to off-AEDs epoch (from 505.0 to 382.3 spikes per hour, p<0.00001), and increased thereafter with the occurrence of seizures. There is an interplay between seizures, spikes and AEDs such that spike rate decreases with AED taper and increases after seizure occurrence. The direct relationship between spike rate and AEDs and between spike rate and time to the first seizure suggests that spikes are a marker of inhibition rather than excitation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  13. Long-term depression of inhibitory synaptic transmission induced by spike-timing dependent plasticity requires coactivation of endocannabinoid and muscarinic receptors.

    PubMed

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

    2013-12-01

    The precise timing of pre-postsynaptic activity is vital for the induction of long-term potentiation (LTP) or depression (LTD) at many central synapses. We show in synapses of rat CA1 pyramidal neurons in vitro that spike timing dependent plasticity (STDP) protocols that induce LTP at glutamatergic synapses can evoke LTD of inhibitory postsynaptic currents or STDP-iLTD. The STDP-iLTD requires a postsynaptic Ca(2+) increase, a release of endocannabinoids (eCBs), the activation of type-1 endocananabinoid receptors and presynaptic muscarinic receptors that mediate a decreased probability of GABA release. In contrast, the STDP-iLTD is independent of the activation of nicotinic receptors, GABAB Rs and G protein-coupled postsynaptic receptors at pyramidal neurons. We determine that the downregulation of presynaptic Cyclic adenosine monophosphate/protein Kinase A pathways is essential for the induction of STDP-iLTD. These results suggest a novel mechanism by which the activation of cholinergic neurons and retrograde signaling by eCBs can modulate the efficacy of GABAergic synaptic transmission in ways that may contribute to information processing and storage in the hippocampus. Copyright © 2013 Wiley Periodicals, Inc.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. Hierarchical spike coding of sound

    PubMed Central

    Karklin, Yan; Ekanadham, Chaitanya; Simoncelli, Eero P.

    2014-01-01

    Natural sounds exhibit complex statistical regularities at multiple scales. Acoustic events underlying speech, for example, are characterized by precise temporal and frequency relationships, but they can also vary substantially according to the pitch, duration, and other high-level properties of speech production. Learning this structure from data while capturing the inherent variability is an important first step in building auditory processing systems, as well as understanding the mechanisms of auditory perception. Here we develop Hierarchical Spike Coding, a two-layer probabilistic generative model for complex acoustic structure. The first layer consists of a sparse spiking representation that encodes the sound using kernels positioned precisely in time and frequency. Patterns in the positions of first layer spikes are learned from the data: on a coarse scale, statistical regularities are encoded by a second-layer spiking representation, while fine-scale structure is captured by recurrent interactions within the first layer. When fit to speech data, the second layer acoustic features include harmonic stacks, sweeps, frequency modulations, and precise temporal onsets, which can be composed to represent complex acoustic events. Unlike spectrogram-based methods, the model gives a probability distribution over sound pressure waveforms. This allows us to use the second-layer representation to synthesize sounds directly, and to perform model-based denoising, on which we demonstrate a significant improvement over standard methods. PMID:25356065

  19. Stochastic variational learning in recurrent spiking networks.

    PubMed

    Jimenez Rezende, Danilo; Gerstner, Wulfram

    2014-01-01

    The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  20. Stochastic variational learning in recurrent spiking networks

    PubMed Central

    Jimenez Rezende, Danilo; Gerstner, Wulfram

    2014-01-01

    The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about “novelty” on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal. PMID:24772078

  1. 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.

  2. Direct imaging of small scatterers using reduced time dependent data

    NASA Astrophysics Data System (ADS)

    Cakoni, Fioralba; Rezac, Jacob D.

    2017-06-01

    We introduce qualitative methods for locating small objects using time dependent acoustic near field waves. These methods have reduced data collection requirements compared to typical qualitative imaging techniques. In particular, we only collect scattered field data in a small region surrounding the location from which an incident field was transmitted. The new methods are partially theoretically justified and numerical simulations demonstrate their efficacy. We show that these reduced data techniques give comparable results to methods which require full multistatic data and that these time dependent methods require less scattered field data than their time harmonic analogs.

  3. 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.

  4. 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.

  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. 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.

  7. 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.

  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. Reducing Recreational Sedentary Screen Time: A Community Guide Systematic Review.

    PubMed

    Ramsey Buchanan, Leigh; Rooks-Peck, Cherie R; Finnie, Ramona K C; Wethington, Holly R; Jacob, Verughese; Fulton, Janet E; Johnson, Donna B; Kahwati, Leila C; Pratt, Charlotte A; Ramirez, Gilbert; Mercer, Shawna L; Glanz, Karen

    2016-03-01

    Sedentary time spent with screen media is associated with obesity among children and adults. Obesity has potentially serious health consequences, such as heart disease and diabetes. This Community Guide systematic review examined the effectiveness and economic efficiency of behavioral interventions aimed at reducing recreational (i.e., neither school- nor work-related) sedentary screen time, as measured by screen time, physical activity, diet, and weight-related outcomes. For this review, an earlier ("original") review (search period, 1966 through July 2007) was combined with updated evidence (search period, April 2007 through June 2013) to assess effectiveness of behavioral interventions aimed at reducing recreational sedentary screen time. Existing Community Guide systematic review methods were used. Analyses were conducted in 2013-2014. The review included 49 studies. Two types of behavioral interventions were evaluated that either (1) focus on reducing recreational sedentary screen time only (12 studies); or (2) focus equally on reducing recreational sedentary screen time and improving physical activity or diet (37 studies). Most studies targeted children aged ≤13 years. Children's composite screen time (TV viewing plus other forms of recreational sedentary screen time) decreased 26.4 (interquartile interval= -74.4, -12.0) minutes/day and obesity prevalence decreased 2.3 (interquartile interval= -4.5, -1.2) percentage points versus a comparison group. Improvements in physical activity and diet were reported. Three study arms among adults found composite screen time decreased by 130.2 minutes/day. Among children, these interventions demonstrated reduced screen time, increased physical activity, and improved diet- and weight-related outcomes. More research is needed among adolescents and adults. Published by Elsevier Inc.

  10. 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.

  11. 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%.

  12. 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.

  13. 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.

  14. 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,…

  15. Sorting and tracking neuronal spikes via simple thresholding.

    PubMed

    Aghagolzadeh, Mehdi; Mohebi, Ali; Oweiss, Karim G

    2014-07-01

    A fundamental goal in systems neuroscience is to assess the individual as well as the synergistic roles of single neurons in a recorded ensemble as they relate to an observed behavior. A mandatory step to achieve this goal is to sort spikes in an extracellularly recorded mixture that belong to individual neurons through feature extraction and clustering techniques. Here, we propose an approach for approximating the often nonlinear and time varying decision boundaries between spike-derived feature classes based on a simple, yet optimal thresholding mechanism. Because thresholding is a binary classifier, we show that the complex nonlinear decision boundaries required for spike class discrimination can be achieved by adequately fusing a set of weak binary classifiers. The thresholds for these binary classifiers are adaptively estimated through a learning algorithm that maximizes the separability between the sparsely represented classes. Based on our previous work, the approach substantially reduces the computational complexity of extracting, aligning and sorting multiple single unit activity early in the data stream. Here, we also show its ability to track changes in spike features over extended periods of time, making it highly suitable for basic neuroscience studies as well as for implementation in miniaturized, fully implantable electronics in brain-machine interface applications.

  16. 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

  17. Regulation of spontaneous rhythmic activity and organization of pacemakers as memory traces by spike-timing-dependent synaptic plasticity in a hippocampal model

    NASA Astrophysics Data System (ADS)

    Yoshida, Motoharu; Hayashi, Hatsuo

    2004-01-01

    It is widely believed that memory traces can be stored through synaptic conductance modification of dense excitatory recurrent connections (ERCs) in the hippocampal CA3 region, namely associative memory. ERCs, on the other hand, are crucial to maintain spontaneous rhythmic activity in CA3. Since it is experimentally suggested that synaptic conductances of ERCs are modified through spike-timing-dependent synaptic plasticity (STDP), rhythmic activity might modify ERCs with the presence of STDP because rhythmic activity involves discharges of pyramidal cells. Memory patterns that are stored using ERCs might thus be modified or even destroyed. Rhythmic activity itself might also be modified. In this study, we assumed that the synaptic modification in the hippocampal CA3 was subject to STDP, and examined the coexistence of memory traces and rhythmic activity. The activity of the network was dominated by radially propagating burst activities (radial activities) that initiated at local regions and acted as pacemakers. The frequency of the rhythmic activity converged into one specific frequency with time, depending on the shape of the STDP functions. This indicates that rhythmic activity could be regulated by STDP. By applying theta burst stimulation locally to the network, we found that the stimulation whose frequency was higher than that of the spontaneous rhythmic activity could organize a new radial activity at the stimulus site. Newly organized radial activities were preserved for seconds after the termination of the stimulation. These results imply that CA3 with STDP has an ability to self-regulate rhythmic activity and that memory traces can coexist with the rhythmic activity by means of radial activity.

  18. 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

  19. Measuring multiple spike train synchrony.

    PubMed

    Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I

    2009-10-15

    Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.

  20. Evidence that TMPRSS2 Activates the Severe Acute Respiratory Syndrome Coronavirus Spike Protein for Membrane Fusion and Reduces Viral Control by the Humoral Immune Response▿

    PubMed Central

    Glowacka, Ilona; Bertram, Stephanie; Müller, Marcel A.; Allen, Paul; Soilleux, Elizabeth; Pfefferle, Susanne; Steffen, Imke; Tsegaye, Theodros Solomon; He, Yuxian; Gnirss, Kerstin; Niemeyer, Daniela; Schneider, Heike; Drosten, Christian; Pöhlmann, Stefan

    2011-01-01

    The spike (S) protein of the severe acute respiratory syndrome coronavirus (SARS-CoV) can be proteolytically activated by cathepsins B and L upon viral uptake into target cell endosomes. In contrast, it is largely unknown whether host cell proteases located in the secretory pathway of infected cells and/or on the surface of target cells can cleave SARS S. We along with others could previously show that the type II transmembrane protease TMPRSS2 activates the influenza virus hemagglutinin and the human metapneumovirus F protein by cleavage. Here, we assessed whether SARS S is proteolytically processed by TMPRSS2. Western blot analysis revealed that SARS S was cleaved into several fragments upon coexpression of TMPRSS2 (cis-cleavage) and upon contact between SARS S-expressing cells and TMPRSS2-positive cells (trans-cleavage). cis-cleavage resulted in release of SARS S fragments into the cellular supernatant and in inhibition of antibody-mediated neutralization, most likely because SARS S fragments function as antibody decoys. trans-cleavage activated SARS S on effector cells for fusion with target cells and allowed efficient SARS S-driven viral entry into targets treated with a lysosomotropic agent or a cathepsin inhibitor. Finally, ACE2, the cellular receptor for SARS-CoV, and TMPRSS2 were found to be coexpressed by type II pneumocytes, which represent important viral target cells, suggesting that SARS S is cleaved by TMPRSS2 in the lung of SARS-CoV-infected individuals. In summary, we show that TMPRSS2 might promote viral spread and pathogenesis by diminishing viral recognition by neutralizing antibodies and by activating SARS S for cell-cell and virus-cell fusion. PMID:21325420

  1. Reducing current reversal time in electric motor control

    SciTech Connect

    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.

  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. 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

  4. 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.

  5. 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.

  6. Spike latency and jitter of neuronal membrane patches with stochastic Hodgkin-Huxley channels.

    PubMed

    Ozer, Mahmut; Uzuntarla, Muhammet; Perc, Matjaz; Graham, Lyle J

    2009-11-07

    Ion channel stochasticity can influence the voltage dynamics of neuronal membrane, with stronger effects for smaller patches of membrane because of the correspondingly smaller number of channels. We examine this question with respect to first spike statistics in response to a periodic input of membrane patches including stochastic Hodgkin-Huxley channels, comparing these responses to spontaneous firing. Without noise, firing threshold of the model depends on frequency-a sinusoidal stimulus is subthreshold for low and high frequencies and suprathreshold for intermediate frequencies. When channel noise is added, a stimulus in the lower range of subthreshold frequencies can influence spike output, while high subthreshold frequencies remain subthreshold. Both input frequency and channel noise strength influence spike timing. Specifically, spike latency and jitter have distinct minima as a function of input frequency, showing a resonance like behavior. With either no input, or low frequency subthreshold input, or input in the low or high suprathreshold frequency range, channel noise reduces latency and jitter, with the strongest impact for the lowest input frequencies. In contrast, for an intermediate range of suprathreshold frequencies, where an optimal input gives a minimum latency, the noise effect reverses, and spike latency and jitter increase with channel noise. Thus, a resonant minimum of the spike response as a function of frequency becomes more pronounced with less noise. Spike latency and jitter also depend on the initial phase of the input, resulting in minimal latencies at an optimal phase, and depend on the membrane time constant, with a longer time constant broadening frequency tuning for minimal latency and jitter. Taken together, these results suggest how stochasticity of ion channels may influence spike timing and thus coding for neurons with functionally localized concentrations of channels, such as in "hot spots" of dendrites, spines or axons.

  7. 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.

  8. Correcting the bias of spike field coherence estimators due to a finite number of spikes.

    PubMed

    Grasse, D W; Moxon, K A

    2010-07-01

    The coherence between oscillatory activity in local field potentials (LFPs) and single neuron action potentials, or spikes, has been suggested as a neural substrate for the representation of information. The power spectrum of a spike-triggered average (STA) is commonly used to estimate spike field coherence (SFC). However, when a finite number of spikes is used to construct the STA, the coherence estimator is biased. We introduce here a correction for the bias imposed by the limited number of spikes available in experimental conditions. In addition, we present an alternative method for estimating SFC from an STA by using a filter bank approach. This method is shown to be more appropriate in some analyses, such as comparing coherence across frequency bands. The proposed bias correction is a linear transformation derived from an idealized model of spike-field interaction but is shown to hold in more realistic settings. Uncorrected and corrected SFC estimates from both estimation methods are compared across multiple simulated spike-field models and experimentally collected data. The bias correction was shown to reduce the bias of the estimators, but add variance. However, the corrected estimates had a reduced or unchanged mean squared error in the majority of conditions evaluated. The bias correction provides an effective way to reduce bias in an SFC estimator without increasing the mean squared error.

  9. 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.

  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. Spike Decomposition Technique: Modeling and Performance Tests

    NASA Astrophysics Data System (ADS)

    Nita, G. M.; Fleishman, G. D.; Gary, D. E.

    2008-05-01

    We develop an automated technique for fitting the spectral components of solar microwave spike bursts characterized by narrow-band (1-50~MHz) features of 1-10~ms duration, which are thought to be due to Electron-Cyclotron Maser emission. The algorithm is especially useful for periods when the spikes occur in densely packed clusters, where the algorithm is capable of decomposing overlapping spike structures into individual spectral components. To test the performance and applicability limits of this forward fitting algorithm, we perform comprehensive modeling of spike clusters characterized by various typical bandwidths, spike densities, and amplitude distributions. We find that, for a wide range of input parameters, the algorithm is able to recover the characteristic features of the modeled distributions within reasonable confidence intervals. Having model-tested the algorithm comprehensively against spike overlap, broadband spectral background, noise contamination, and possible contamination of cross-channel polarization, we apply the technique to observational data obtained from different instruments in different frequency ranges. Specifically, we studied spike clusters recorded by a Chinese Purple Mountain Observatory (PMO) spectrometer above 4.5 GHz and by Owens Valley Solar Array's FASR Subsystem Testbed instrument above 1 GHz. We study variation of the spike distribution parameters, such as amplitude, bandwidth and related derived physical parameters as a function of frequency and time. We discuss the implications of our results for the choice between competing models of spike generation and underlying physical processes. The method can be further applied to observations from other instruments and to other types of radio spectral fine structures. This work was supported in part by NSF grants AST-0607544 and ATM-0707319 and NASA grant NNG06GJ40G to New Jersey Institute of Technology.

  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. Quiet Spike(TradeMark) Build-up Ground Vibration Testing Approach

    NASA Technical Reports Server (NTRS)

    Spivey, Natalie D.; Herrera, Claudia Y.; Truax, Roger; Pak, Chan-gi; Freund, Donald

    2007-01-01

    Flight tests of Gulfstream Aerospace Corporation s Quiet Spike(TradeMark) hardware were recently completed on the NASA Dryden Flight Research Center F-15B airplane. NASA Dryden uses a modified F-15B airplane as a testbed aircraft to cost-effectively fly flight research experiments that are typically mounted underneath the F-15B airplane, along the fuselage centerline. For the Quiet Spike(TradeMark) experiment, however, instead of a centerline mounting, a relatively long forward-pointing boom was attached to the radar bulkhead of the F-15B airplane. The Quiet Spike(TradeMark) experiment is a stepping-stone to airframe structural morphing technologies designed to mitigate the sonic-boom strength of business jets over land. The Quiet Spike(TradeMark) boom is a concept in which an aircraft s noseboom would be extended prior to supersonic acceleration. This morphing effectively lengthens the aircraft, thus reducing the peak sonic-boom amplitude, but is also expected to partition the otherwise strong bow shock into a series of reduced-strength, noncoalescing shocklets. Prior to flying the Quiet Spike(TradeMark) experiment on the F-15B airplane several ground vibration tests were required to understand the Quiet Spike(TradeMark) modal characteristics and coupling effects with the F-15B airplane. However, due to the flight hardware availability and compressed schedule requirements, a "traditional" ground vibration test of the mated F-15B Quiet Spike(TradeMark) ready-for- flight configuration did not leave sufficient time available for the finite element model update and flutter analyses before flight testing. Therefore, a "nontraditional" ground vibration testing approach was taken. This paper provides an overview of each phase of the "nontraditional" ground vibration testing completed for the Quiet Spike(TradeMark) project which includes the test setup details, instrumentation layout, and modal results obtained in support of the structural dynamic modeling and flutter

  15. Active learning reduces annotation time for clinical concept extraction.

    PubMed

    Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

    2017-10-01

    To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. 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. In the planning of a movement, the brain has the opportunity to delay the incorporation of accuracy requirements of the motor plan in order to reduce the reaction time by up to 100 ms (average: 32 ms). Such shortening of reaction time is observed here when the first phase of the movement consists of a transport phase. This forces us to reconsider the hypothesis that motor plans are fully defined before movement onset. Copyright © 2017 the American Physiological Society.

  17. 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.

  18. A new supervised learning algorithm for spiking neurons.

    PubMed

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

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

  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. 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…

  1. Survival-time statistics for sample space reducing stochastic processes

    NASA Astrophysics Data System (ADS)

    Yadav, Avinash Chand

    2016-04-01

    Stochastic processes wherein the size of the state space is changing as a function of time offer models for the emergence of scale-invariant features observed in complex systems. I consider such a sample-space reducing (SSR) stochastic process that results in a random sequence of strictly decreasing integers {x (t )},0 ≤t ≤τ , with boundary conditions x (0 )=N and x (τ ) = 1. This model is shown to be exactly solvable: PN(τ ) , the probability that the process survives for time τ is analytically evaluated. In the limit of large N , the asymptotic form of this probability distribution is Gaussian, with mean and variance both varying logarithmically with system size: <τ >˜lnN and στ2˜lnN . Correspondence can be made between survival-time statistics in the SSR process and record statistics of independent and identically distributed random variables.

  2. Survival-time statistics for sample space reducing stochastic processes.

    PubMed

    Yadav, Avinash Chand

    2016-04-01

    Stochastic processes wherein the size of the state space is changing as a function of time offer models for the emergence of scale-invariant features observed in complex systems. I consider such a sample-space reducing (SSR) stochastic process that results in a random sequence of strictly decreasing integers {x(t)},0≤t≤τ, with boundary conditions x(0)=N and x(τ) = 1. This model is shown to be exactly solvable: P_{N}(τ), the probability that the process survives for time τ is analytically evaluated. In the limit of large N, the asymptotic form of this probability distribution is Gaussian, with mean and variance both varying logarithmically with system size: 〈τ〉∼lnN and σ_{τ}^{2}∼lnN. Correspondence can be made between survival-time statistics in the SSR process and record statistics of independent and identically distributed random variables.

  3. 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.

  4. Interventions to reduce waiting times for elective procedures.

    PubMed

    Ballini, Luciana; Negro, Antonella; Maltoni, Susanna; Vignatelli, Luca; Flodgren, Gerd; Simera, Iveta; Holmes, Jane; Grilli, Roberto

    2015-02-23

    Long waiting times for elective healthcare procedures may cause distress among patients, may have adverse health consequences and may be perceived as inappropriate delivery and planning of health care. To assess the effectiveness of interventions aimed at reducing waiting times for elective care, both diagnostic and therapeutic. We searched the following electronic databases: Cochrane Effective Practice and Organisation of Care (EPOC) Group Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (1946-), EMBASE (1947-), the Cumulative Index to Nursing and Allied Health Literature (CINAHL), ABI Inform, the Canadian Research Index, the Science, Social Sciences and Humanities Citation Indexes, a series of databases via Proquest: Dissertations & Theses (including UK & Ireland), EconLit, PAIS (Public Affairs International), Political Science Collection, Nursing Collection, Sociological Abstracts, Social Services Abstracts and Worldwide Political Science Abstracts. We sought related reviews by searching the Cochrane Database of Systematic Reviews and the Database of Abstracts of Reviews of Effectiveness (DARE). We searched trial registries, as well as grey literature sites and reference lists of relevant articles. We considered randomised controlled trials (RCTs), controlled before-after studies (CBAs) and interrupted time series (ITS) designs that met EPOC minimum criteria and evaluated the effectiveness of any intervention aimed at reducing waiting times for any type of elective procedure. We considered studies reporting one or more of the following outcomes: number or proportion of participants whose waiting times were above or below a specific time threshold, or participants' mean or median waiting times. Comparators could include any type of active intervention or standard practice. Two review authors independently extracted data from, and assessed risk of bias of, each included study, using a standardised form and the EPOC 'Risk

  5. 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.

  6. 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.

  7. Introduction to spiking neural networks: Information processing, learning and applications.

    PubMed

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

    The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

  8. 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.

  9. 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.

  10. Reducing time to surgery after anterior cruciate ligament injury.

    PubMed

    Sapsford, H; Sutherland, A G

    2016-05-01

    Recent work suggests that reconstruction of the ruptured anterior cruciate ligament within 12 months of injury results in better outcomes. We present a complete audit cycle examining the effect of establishment of an Acute Knee Clinic on time to surgery. Records of 20 anterior cruciate ligament reconstructions undertaken by the senior author between June 2003 and May 2004 were examined to identify the time to surgery. The Acute Knee Clinic was established in December 2004. Prospectively collected data on patients attending the Acute Knee Clinic between May 2005 and July 2007 and patients undergoing anterior cruciate ligament reconstruction from September 2006 to 2007 were reviewed with respect to referral route, time from injury to specialist review and time to surgery. Mean time from injury to surgery of the initial cohort was 14 months (range 3-56). After establishment of the Acute Knee Clinic, 90% of referrals from Accident and Emergency (A&E) were seen by a specialist within four weeks. Between September 2006 and September 2007, 49 patients underwent anterior cruciate ligament reconstruction: 21 came via the Acute Knee Clinic, with a mean time from injury to surgery of 6 months; 28 patients from the elective clinic had a mean time to surgery of 25 months. 95% of Acute Knee Clinic patients and 53 % of elective clinic patients had surgery within 12 months of injury. The Acute Knee Clinic has been shown to reduce the time from injury to anterior cruciate ligament reconstruction. The Acute Knee Clinic only accounts for the referral of 40% of anterior cruciate ligament reconstructions in this series: Further education work is required with A&E staff and GPs regarding the referral of knee injuries. Access to the Acute Knee Clinic could be extended to GPs, although this could create service overload. © The Author(s) 2016.

  11. Applying Lean methodologies reduces ED laboratory turnaround times.

    PubMed

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

    2015-11-01

    Increasing the value of health care 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. We examined the hypothesis that Lean-based reorganization of laboratory process flow would improve laboratory turnaround times (TAT) and reduce waste in the system. 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 6 tests previously performed in an ED kiosk. After 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 minutes; 99% confidence interval, 30-35 minutes) and urine sedimentation TAT by 88 minutes (117 to 29 minutes; 99% confidence interval, 87-90 minutes). In addition, troponin I TAT was reduced by 12 minutes, urinalysis by 9 minutes, and urine human chorionic gonadotropin by 10 minutes. Microbiology rapid testing TAT, a "control," did not change. In this study, Lean-based reorganization of laboratory process flow significantly increased process efficiency. Broader application of systems engineering science might further improve health care quality and capacity while reducing waste and cost. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Spike latency coding in biologically inspired microelectronic nose.

    PubMed

    Hung Tat Chen; Kwan Ting Ng; Bermak, A; Law, M K; Martinez, D

    2011-04-01

    Recent theoretical and experimental findings suggest that biological olfactory systems utilize relative latencies or time-to-first spikes for fast odor recognition. These time-domain encoding methods exhibit reduced computational requirements and improved classification robustness. In this paper, we introduce a microcontroller-based electronic nose system using time-domain encoding schemes to achieve a power-efficient, compact, and robust gas identification system. A compact (4.5 cm × 5 cm × 2.2 cm) electronic nose, which is integrated with a tin-oxide gas-sensor array and capable of wireless communication with computers or mobile phones through Bluetooth, was implemented and characterized by using three different gases (ethanol, carbon monoxide, and hydrogen). During operation, the readout circuit digitizes the gas-sensor resistances into a concentration-independent spike timing pattern, which is unique for each individual gas. Both sensing and recognition operations have been successfully demonstrated in hardware. Two classification algorithms (rank order and spike distance) have been implemented. Both algorithms do not require any explicit knowledge of the gas concentration to achieve simplified training procedures, and exhibit comparable performances with conventional pattern-recognition algorithms while enabling hardware-friendly implementation.

  13. 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

  14. Real-time absorption reduced surface fluorescence imaging

    NASA Astrophysics Data System (ADS)

    Yang, Bin; Tunnell, James W.

    2014-09-01

    We introduce a technique that limits absorption effects in fluorescence imaging and does not require extensive imaging processing, thus allowing for video rate imaging. The absorption minimization is achieved using spatial frequency domain imaging at a single high spatial frequency with standard three-phase demodulation. At a spatial frequency f=0.5 mm-1, we demonstrated in both in-vitro phantoms and ex-vivo tissue that the absorption can be significantly reduced. In the real-time implementation, we achieved a video rate of 19 frames/s. This technique has potential in cancer visualization and tumor margin detection.

  15. Real-time absorption reduced surface fluorescence imaging.

    PubMed

    Yang, Bin; Tunnell, James W

    2014-09-01

    We introduce a technique that limits absorption effects in fluorescence imaging and does not require extensive imaging processing, thus allowing for video rate imaging. The absorption minimization is achieved using spatial frequency domain imaging at a single high spatial frequency with standard three-phase demodulation. At a spatial frequency f ¼ 0.5 mm−1, we demonstrated in both in-vitro phantoms and ex-vivo tissue that the absorption can be significantly reduced. In the real-time implementation, we achieved a video rate of 19 frames∕s. This technique has potential in cancer visualization and tumor margin detection.

  16. Nuclear detector system with reduced dead-time processor circuit

    SciTech Connect

    Schwartz, R.J.

    1987-01-06

    This patent describes for use in a well logging system which has a nuclear event detector forming an output signal of randomly space peaks having amplitudes within a specified range, a reduced dead-time processor circuit which comprises: (a) input means adapted to be connected with a nuclear event detector in a sonde to provide an input signal having arranged for deflecting at least a part of the beam of electrons during passage from a surface of the anode grid structure facing the photocathode to the target.

  17. 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

  18. 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.

  19. Criterion-based laparoscopic training reduces total training time.

    PubMed

    Brinkman, Willem M; Buzink, Sonja N; Alevizos, Leonidas; de Hingh, Ignace H J T; Jakimowicz, Jack J

    2012-04-01

    The benefits of criterion-based laparoscopic training over time-oriented training are unclear. The purpose of this study is to compare these types of training based on training outcome and time efficiency. During four training sessions within 1 week (one session per day) 34 medical interns (no laparoscopic experience) practiced on two basic tasks on the Simbionix LAP Mentor virtual-reality (VR) simulator: 'clipping and grasping' and 'cutting'. Group C (criterion-based) (N = 17) trained to reach predefined criteria and stopped training in each session when these criteria were met, with a maximum training time of 1 h. Group T (time-based) (N = 17) trained for a fixed time of 1 h each session. Retention of skills was assessed 1 week after training. In addition, transferability of skills was established using the Haptica ProMIS augmented-reality simulator. Both groups improved their performance significantly over the course of the training sessions (Wilcoxon signed ranks, P < 0.05). Both groups showed skill transferability and skill retention. When comparing the performance parameters of group C and group T, their performances in the first, the last and the retention training sessions did not differ significantly (Mann-Whitney U test, P > 0.05). The average number of repetitions needed to meet the criteria also did not differ between the groups. Overall, group C spent less time training on the simulator than did group T (74:48 and 120:10 min, respectively; P < 0.001). Group C performed significantly fewer repetitions of each task, overall and in session 2, 3 and 4. Criterion-based training of basic laparoscopic skills can reduce the overall training time with no impact on training outcome, transferability or retention of skills. Criterion-based should be the training of choice in laparoscopic skills curricula.

  20. 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.

  1. Production of a microcapsule agent of chromate-reducing Lysinibacillus fusiformis ZC1 and its application in remediation of chromate-spiked soil.

    PubMed

    Huang, Jun; Li, Jingxin; Wang, Gejiao

    2016-01-01

    Lysinibacillus fusiformis ZC1 is an efficient Cr(VI)-reducing bacterium that can transform the toxic and soluble chromate [Cr(VI)] form to the less toxic and precipitated chromite form [Cr(III)]. As such, this strain might be applicable for bioremediation of Cr(VI) in soil by reducing its bioavailability. The study objective was to prepare a microcapsule agent of strain ZC1 for bioremediation of Cr(VI)-contaminated soil. Using a single-factor orthogonal array design, the optimal fermentation medium was obtained and consisted of 6 g/L corn flour, 12 g/L soybean flour, 8 g/L NH4Cl and 6 g/L CaCl2. After enlarged fermentation, the cell and spore densities were 5.9 × 10(9) and 1.7 × 10(8) cfu/mL, respectively. The fermentation products were collected and embedded with 1 % gum arabic and 1 % sorbitol as the microcapsule carriers and were subsequently spray-dried. Strain ZC1 exhibited viable cell counts of (3.6 ± 0.44) × 10(10) cfu/g dw after 50-day storage at room temperature. In simulated soil bioremediation experiments, 67 % of Cr(VI) was reduced in 5 days with the inoculation of this microcapsule agent, and the Cr(VI) concentration was below the soil Cr(VI) standard level. The results demonstrated that the microcapsule agent of strain ZC1 is efficient for bioremediation of Cr(VI)-contaminated soil.

  2. 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

  3. 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.

  4. 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%.

  5. Neuronal spike trains and stochastic point processes. I. The single spike train.

    PubMed

    Perkel, D H; Gerstein, G L; Moore, G P

    1967-07-01

    In a growing class of neurophysiological experiments, the train of impulses ("spikes") produced by a nerve cell is subjected to statistical treatment involving the time intervals between spikes. The statistical techniques available for the analysis of single spike trains are described and related to the underlying mathematical theory, that of stochastic point processes, i.e., of stochastic processes whose realizations may be described as series of point events occurring in time, separated by random intervals. For single stationary spike trains, several orders of complexity of statistical treatment are described; the major distinction is that between statistical measures that depend in an essential way on the serial order of interspike intervals and those that are order-independent. The interrelations among the several types of calculations are shown, and an attempt is made to ameliorate the current nomenclatural confusion in this field. Applications, interpretations, and potential difficulties of the statistical techniques are discussed, with special reference to types of spike trains encountered experimentally. Next, the related types of analysis are described for experiments which involve repeated presentations of a brief, isolated stimulus. Finally, the effects of nonstationarity, e.g. long-term changes in firing rate, on the various statistical measures are discussed. Several commonly observed patterns of spike activity are shown to be differentially sensitive to such changes. A companion paper covers the analysis of simultaneously observed spike trains.

  6. Multichannel sparse spike inversion

    NASA Astrophysics Data System (ADS)

    Pereg, Deborah; Cohen, Israel; Vassiliou, Anthony A.

    2017-10-01

    In this paper, we address the problem of sparse multichannel seismic deconvolution. We introduce multichannel sparse spike inversion as an iterative procedure, which deconvolves the seismic data and recovers the Earth two-dimensional reflectivity image, while taking into consideration the relations between spatially neighboring traces. We demonstrate the improved performance of the proposed algorithm and its robustness to noise, compared to competitive single-channel algorithm through simulations and real seismic data examples.

  7. 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.

  8. How to Reduce Computational Time in Distributed Hydrological Modeling?

    NASA Astrophysics Data System (ADS)

    Khan, U.; Tuteja, N. K.; Ajami, H.; Sharma, A.

    2012-12-01

    One of the key limitations of distributed hydrologic modeling for large scale simulations of soil moisture and land surface fluxes is the computational time spent in simulating hydrological processes. It is for this reason that applications involving assessment of model uncertainty, or simulating multiple input realizations as often needed to assess climate change impacts on a catchment, are not attempted, and models applied to understand hydrological processes in small sized, experimental catchments. The questions asked in this presentation are (a) whether one can simulate the catchment hydrology by simulating across multiple cross sections in a hillslope ; and (b) can one improve these simulations further by simulating on a single (or selected few) "Equivalent" cross-sections in the catchment. This new concept of an Equivalent Cross-section informed by the catchment landform is developed for upland catchments, to reduce computational time while maintaining the same order of accuracy in simulating hydrologic fluxes. The Unsaturated Soil Moisture Movement model (U3M-2d), based on a 2-dimensional solution of the Richards' equation, is used to simulate hydrologic fluxes. In this method, simulations with U3M-2d are first done for a number of uniformly spaced cross-sections in each Strahler's first order sub-basin and the total fluxes are estimated (reference case). Single or multiple Equivalent Cross-sections are then derived for each Strahler's first order sub-basin and results are compared against the reference case. To formulate the Equivalent Cross-section, the catchment is divided into four m