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

Tópicos

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Resumo

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

Resumo Limpo

vivo cortic record reveal indirect driven neural assembl can produc reliabl tempor precis spike pattern respons stereotyp stimul suggest despit fundament noisi collect activ neuron convey inform tempor code stochast integrateandfir model delin natur theoret framework studi interplay intrins neural nois spike time precis howev inher difficulti simul network dynam silico standard numer discret scheme inde wellposed evolut network requir tempor order everi neuron interact wherea order interact high sensit random variabl spike time answer issu perfect stochast integrateandfir neuron design exact eventdriven algorithm simul recurr network delay diraclik interact addit exact mathemat standpoint propos method high effici numer envis algorithm especi indic studi emerg polychron motif network evolv spiketimingdepend plastic intrins nois

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