Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains

PLOS ONE, Jan 2008

Experimental studies have observed Long Term synaptic Potentiation (LTP) when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD) when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependant Plasticity (STDP). When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn ‘early spike patterns’, that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense ‘distractor’ spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it.

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Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains

Thorpe SJ (2008) Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains. PLoS ONE 3(1): e1377. doi:10.1371/journal.pone.0001377 Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains Timothe e Masquelier 0 1 Rudy Guyonneau 0 1 Simon J. Thorpe 0 1 0 Academic Editor: Olaf Sporns, Indiana University , United States of America 1 1 Centre de Recherche Cerveau et Cognition, Universite Toulouse 3, Centre National de la Recherche Scientifique (CNRS), Facult e de Me decine de Rangueil , Toulouse, France, 2 SpikeNet Technology SARL, Prologue 1 La Pyre ne enne, Labe`ge , France Experimental studies have observed Long Term synaptic Potentiation (LTP) when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD) when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependant Plasticity (STDP). When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn 'early spike patterns', that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense 'distractor' spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it. - INTRODUCTION Electrophysiologists report the existence of repeating spatiotemporal spike patterns with millisecond precision, both in vitro and in vivo, lasting from a few tens of ms to several seconds[13]. In this study we assess the difficult problem of detecting them, and suggest how neurons could solve it. The problem is made particularly difficult when only a fraction of the recorded neurons are involved in the pattern. Fig. 1 illustrates such a situation. There is a pattern of spikes (indicated by the red dots) that repeats at irregular intervals, but is hidden within the variable background firing of the whole population (shown in blue). The problem is made hard because nothing in terms of population firing rate characterizes the periods when the pattern is present, nor is there anything unusual about the firing rates of the neurons involved in the pattern. In such a situation detecting the pattern clearly requires taking the spike times into account. However direct comparison of each spike time to one another over the entire recording period and across the entire set of afferents is extremely computationally expensive. In this article we will see how a single neuron equipped with STDP can solve the problem in a different manner, taking advantage of the fact that a pattern is a succession of spike coincidences. STDP is now a widely accepted physiological mechanism of activity-driven synaptic regulation. It has been observed extensively in vitro[47], and more recently in vivo in Xenopuss visual system[8,9], in the locusts mushroom body[10], and in the rats visual cortex[11] and barrel cortex[12]. An exponential update rule fits well the synaptic modifications observed experimentally[13] (see Fig. 2). Very recently, it has also been shown that cortical reorganization in cat primary visual cortex is in accordance with STDP[14]. Note that STDP is in agreement with Hebbs postulate because it reinforces the connections with the presynaptic neurons that fired slightly before the postsynaptic neuron, which are those that took part in firing it. It thereby reinforces causality links. When a neuron is presented successively with similar volleys of input spikes STDP is known to have the effect of concentrating synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases[1518]. This theoretical observation is in accordance with recordings in rats hippocampus showing that the so called place cells fire earlier relative to the cycle of the theta oscillation in hippocampus after the animal has repeatedly traversed the corresponding area[19]. STDP has also been studied in an oscillatory mode, and was shown to be able to select only phase-locked inputs among a broad population with random phases, turning the postsynaptic neuron into a coincidence detector[20]. The main limitation of these studies is the assumption that the input spikes arrive in discrete volleys (sometimes also called spike waves). They assume an explicit time reference usually the presentation of a stimulus[15,17,18], or the maximum (or minimum) of an oscillatory drive[20,21] that allows the specification (...truncated)


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Timothée Masquelier, Rudy Guyonneau, Simon J. Thorpe. Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains, PLOS ONE, 2008, 1, DOI: 10.1371/journal.pone.0001377