Competitive STDP-Based Spike Pattern Learning

2009 ◽  
Vol 21 (5) ◽  
pp. 1259-1276 ◽  
Author(s):  
Timothée Masquelier ◽  
Rudy Guyonneau ◽  
Simon J. Thorpe
Keyword(s):  
Single Neuron ◽  
Spike Trains ◽  
Spike Timing ◽  
Temporal Coding ◽  
Spike Pattern ◽  
Distributed Coding ◽  
Coding Scheme ◽  
Winner Take All ◽  
The Brain ◽  
Repeating Patterns

Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern. Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons “listening” to the incoming spike trains. These “listening” neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges. Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them.

Precise Spatiotemporal Repeating Patterns in Monkey Primary and Supplementary Motor Areas Occur at Chance Levels

2000 ◽  
Vol 84 (4) ◽  
pp. 1770-1780 ◽  
Author(s):  
Stuart N. Baker ◽  
Roger N. Lemon
Keyword(s):  
Experimental Data ◽  
Firing Rate ◽  
Interspike Interval ◽  
Precision Grip ◽  
Surrogate Data ◽  
Spike Trains ◽  
Temporal Coding ◽  
Confidence Limits ◽  
Task Dependence ◽  
Repeating Patterns

Precise spatiotemporal patterns in neural discharge are a possible mechanism for information encoding in the brain. Previous studies have found that such patterns repeat and appear to relate to key behavioral events. Whether these patterns occur above chance levels remains controversial. To address this question, we have made simultaneous recordings from between two and nine neurons in the primary motor cortex and supplementary motor area of three monkeys while they performed a precision grip task. Out of a total of 67 neurons, 46 were antidromically identified as pyramidal tract neurons. Sections of recordings 60 s long were searched for patterns involving three or more spikes that repeated at least twice. The allowed jitter for pattern repetition was 3 ms, and the pattern length was limited to 192 ms. In all 11 recordings analyzed, large numbers of repeating patterns were found. To assess the expected chance level of patterns, “surrogate” datasets were generated. These had the same moment-by-moment modulation in firing rate as the experimental spike trains, and matched their interspike interval distribution, but did not preserve the precise timing of individual spikes. The number of repeating patterns in 10 randomly generated surrogates was used to form 99% confidence limits on the repeating pattern count expected by chance. There was close agreement between these confidence limits and the number of patterns seen in the experimental data. Analysis of high complexity patterns was carried out in four long recordings (mean duration 23.2 min, mean number of neurons simultaneously recorded 7.5). This analysis logged only patterns composed of a larger number (7–11) of spikes. The number of patterns seen in the surrogate datasets showed a small but significant excess over those seen in the original experimental data; this is discussed in the context of surrogate generation. The occurrence of repeating patterns in the experimental data were strongly associated with particular phases of the precision grip task; however, a similar task dependence was seen for the surrogate data. When a repeating pattern was used as a template to find inexact matches, in which up to half of the component spikes could be missing, similar numbers of matches were found in experimental and surrogate data, and the time of occurrence of such matches showed the same task dependence. We conclude that the existence of precise repeating patterns in our data are not due to cortical mechanisms that favor this form of coding, since as many, if not more, patterns are produced by spike trains constructed only to modulate their firing rate in the same way as the experimental data, and to match the interspike interval histograms. The task dependence of pattern occurrence is explicable as an artifact of the modulation of neural firing rate. The consequences for theories of temporal coding in the cortex are discussed.

scholarly journals A bidirectional associative memory based on cortical spiking neurons using temporal coding

2021 ◽  
Author(s):  
Masood Zamani
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Action Potentials ◽  
Storage Capacity ◽  
Spike Timing ◽  
Temporal Coding ◽  
Spiking Neurons ◽  
Biological Plausibility ◽  
Bidirectional Associative Memory ◽  
Coding Scheme

In this thesis, we proposed a spiking bidirectional associative memory (BAM) using temporal coding. The information processing in biological neurons is beyond of[sic] that applied in the current Artificial Neural Networks (ANNs). The coding scheme used in ANNs known as “mean firing rate” cannot answer the fast and complex computations occurring in the cortex. In biological neural networks the information is coded and processed based on the timing of action potentials. To improve the biological plausibility of the standard BAM, we employed spiking neurons for its processing units, and information is presented to the BAM in the form of temporal coding. The neurons employed in the model are heterogeneous, and being able to generate various spike-timing patterns. Genetic Algorithm and Co-evolution are used for training, and the experiment results of the proposed BAM are compared to those of the standard BAM. The results show improvements in recall, storage capacity and convergence which are of interest to design a BAM.

scholarly journals Intrinsic and Extrinsic Neuronal Mechanisms in Temporal Coding: A Further Look at Neuronal Oscillations

1999 ◽  
Vol 6 (4) ◽  
pp. 173-189 ◽  
Author(s):  
Rémy Lestienne
Keyword(s):  
Temporal Coding ◽  
Phase Locked Loops ◽  
Fixed Frequency ◽  
Frequency Coding ◽  
Neuronal Mechanisms ◽  
The Central Nervous System ◽  
Neuronal Oscillators ◽  
Fast Oscillations ◽  
The Brain ◽  
Repeating Patterns

Many studies in recent years have been devoted to the detection of fast oscillations in the Central Nervous System (CNS), interpreting them as synchronizing devices. We should, however, refrain from associating too closely the two concepts of synchronization and oscillation. Whereas synchronization is a relatively well-defined concept, by contrast oscillation of a population of neurones in the CNS looks loosely defined, in the sense that both its frequency sharpness and the duration of the oscillatory episodes vary widely from case to case. Also, the functions of oscillations in the brain are multiple and are not confined to synchronization. The paradigmatic instantiation of oscillation in physics is given by the harmonic oscillator, a device particularly suited to tell the time, as in clocks. We will thus examine first the case of oscillations or cycling discharges of neurones, which provide a clock or impose a “tempo” for various kinds of information processing. Neuronal oscillators are rarely just clocks clicking at a fixed frequency. Instead, their frequency is often adjustable and controllable, as in the example of the “chattering cells” discovered in the superficial layers of the visual cortex. Moreover, adjustable frequency oscillators are suitable for use in “phase locked loops” (PLL) networks, a device that can convert time coding to frequency coding; such PLL units have been found in the somatosensory cortex of guinea pigs. Finally, are oscillations stricto sensu necessary to induce synchronization in the discharges of downstream neurones? We know that this is not the case, at least not for local populations of neurones. As a contribution to this question, we propose that repeating patterns in neuronal discharges production may be looked at as one such alternative solution in relation to the processing of information. We review here the case of precisely repeating triplets, detected in the discharges of olfactory mitral cells of a freely breathing rat under odor stimulation.

scholarly journals A bidirectional associative memory based on cortical spiking neurons using temporal coding

2021 ◽  
Author(s):  
Masood Zamani
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Action Potentials ◽  
Storage Capacity ◽  
Spike Timing ◽  
Temporal Coding ◽  
Spiking Neurons ◽  
Biological Plausibility ◽  
Bidirectional Associative Memory ◽  
Coding Scheme

In this thesis, we proposed a spiking bidirectional associative memory (BAM) using temporal coding. The information processing in biological neurons is beyond of[sic] that applied in the current Artificial Neural Networks (ANNs). The coding scheme used in ANNs known as “mean firing rate” cannot answer the fast and complex computations occurring in the cortex. In biological neural networks the information is coded and processed based on the timing of action potentials. To improve the biological plausibility of the standard BAM, we employed spiking neurons for its processing units, and information is presented to the BAM in the form of temporal coding. The neurons employed in the model are heterogeneous, and being able to generate various spike-timing patterns. Genetic Algorithm and Co-evolution are used for training, and the experiment results of the proposed BAM are compared to those of the standard BAM. The results show improvements in recall, storage capacity and convergence which are of interest to design a BAM.

scholarly journals Vision and the representation of the surroundings in spatial memory

2011 ◽  
Vol 366 (1564) ◽  
pp. 596-610 ◽  
Author(s):  
Benjamin W. Tatler ◽  
Michael F. Land
Keyword(s):  
Spatial Memory ◽  
Visual Environment ◽  
Coding Scheme ◽  
Perceptual Stability ◽  
The World ◽  
Scene Viewing ◽  
Memory Representations ◽  
The Brain ◽  
Allocentric Representations ◽  
And Task

One of the paradoxes of vision is that the world as it appears to us and the image on the retina at any moment are not much like each other. The visual world seems to be extensive and continuous across time. However, the manner in which we sample the visual environment is neither extensive nor continuous. How does the brain reconcile these differences? Here, we consider existing evidence from both static and dynamic viewing paradigms together with the logical requirements of any representational scheme that would be able to support active behaviour. While static scene viewing paradigms favour extensive, but perhaps abstracted, memory representations, dynamic settings suggest sparser and task-selective representation. We suggest that in dynamic settings where movement within extended environments is required to complete a task, the combination of visual input, egocentric and allocentric representations work together to allow efficient behaviour. The egocentric model serves as a coding scheme in which actions can be planned, but also offers a potential means of providing the perceptual stability that we experience.

scholarly journals Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 500 ◽  
Author(s):  
Sergey A. Lobov ◽  
Andrey V. Chernyshov ◽  
Nadia P. Krilova ◽  
Maxim O. Shamshin ◽  
Victor B. Kazantsev
Keyword(s):  
Supervised Learning ◽  
Hebbian Learning ◽  
Back Propagation ◽  
Spike Timing ◽  
Temporal Coding ◽  
Competitive Learning ◽  
Neuron Activity ◽  
Synaptic Competition ◽  
Rate Coding ◽  
Emg Patterns

One of the modern trends in the design of human–machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the “winner takes all” principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

scholarly journals GluA1 Phosphorylation Alters Evoked Firing PatternIn Vivo

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Balázs Barkóczi ◽  
Gábor Juhász ◽  
Robert G. Averkin ◽  
Imre Vörös ◽  
Petra Vertes ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Spike Trains ◽  
Temporal Coding ◽  
Relative Contribution ◽  
Ampa Receptor Subunit ◽  
Ampa And Nmda Receptors ◽  
Characteristic Features ◽  
Evoked Activity

AMPA and NMDA receptors convey fast synaptic transmission in the CNS. Their relative contribution to synaptic output and phosphorylation state regulate synaptic plasticity. The AMPA receptor subunit GluA1 is central in synaptic plasticity. Phosphorylation of GluA1 regulates channel properties and trafficking. The firing rate averaged over several hundred ms is used to monitor cellular input. However, plasticity requires the timing of spiking within a few ms; therefore, it is important to understand how phosphorylation governs these events. Here, we investigate whether the GluA1 phosphorylation (p-GluA1) alters the spiking patterns of CA1 cellsin vivo. The antidepressant Tianeptine was used for inducing p-GluA1, which resulted in enhanced AMPA-evoked spiking. By comparing the spiking patterns of AMPA-evoked activity with matched firing rates, we show that the spike-trains after Tianeptine application show characteristic features, distinguishing from spike-trains triggered by strong AMPA stimulation. The interspike-interval distributions are different between the two groups, suggesting that neuronal output may differ when new inputs are activated compared to increasing the gain of previously activated receptors. Furthermore, we also show that NMDA evokes spiking with different patterns to AMPA spike-trains. These results support the role of the modulation of NMDAR/AMPAR ratio and p-GluA1 in plasticity and temporal coding.

scholarly journals Flexible categorization in perceptual decision making

2020 ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Large Body ◽  
Network Models ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Winner Take All ◽  
The Brain

AbstractPerceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization, in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making.

scholarly journals Sequence memory in recurrent neuronal network can develop without structured input

2020 ◽  
Author(s):  
Matthias Loidolt ◽  
Lucas Rudelt ◽  
Viola Priesemann
Keyword(s):  
Sensory Input ◽  
Building Blocks ◽  
Spike Timing ◽  
Synaptic Competition ◽  
Structured Input ◽  
Recurrent Neuronal Network ◽  
Sequence Memory ◽  
The Brain ◽  
Specific Sequences ◽  
Intrinsic Feature

AbstractHow does spontaneous activity during development prepare cortico-cortical connections for sensory input? We here analyse the development of sequence memory, an intrinsic feature of recurrent networks that supports temporal perception. We use a recurrent neural network model with homeostatic and spike-timing-dependent plasticity (STDP). This model has been shown to learn specific sequences from structured input. We show that development even under unstructured input increases unspecific sequence memory. Moreover, networks “pre-shaped” by such unstructured input subsequently learn specific sequences faster. The key structural substrate is the emergence of strong and directed synapses due to STDP and synaptic competition. These construct self-amplifying preferential paths of activity, which can quickly encode new input sequences. Our results suggest that memory traces are not printed on a tabula rasa, but instead harness building blocks already present in the brain.

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