Abstract
Despite the vital importance of our ability to accurately process and encode temporal information, the underlying neural mechanisms are largely unknown. We have previously described a theoretical framework that explains how temporal representations, similar to those reported in the visual cortex, can form in locally recurrent cortical networks as a function of reward modulated synaptic plasticity. This framework allows networks of both linear and spiking neurons to learn the temporal interval between a stimulus and paired reward signal presented during training. Here we use a mean field approach to analyze the dynamics of non-linear stochastic spiking neurons in a network trained to encode specific time intervals. This analysis explains how recurrent excitatory feedback allows a network structure to encode temporal representations.
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Gavornik, J.P., Shouval, H.Z. A network of spiking neurons that can represent interval timing: mean field analysis. J Comput Neurosci 30, 501–513 (2011). https://doi.org/10.1007/s10827-010-0275-y
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DOI: https://doi.org/10.1007/s10827-010-0275-y