A network of spiking neurons that can represent interval timing: mean field analysis.

Department of Neurobiology and Anatomy, The University of Texas Medical School at Houston, 6431 Fannin St., Houston, TX 77030, USA.
Journal of Computational Neuroscience (Impact Factor: 2.09). 04/2011; 30(2):501-13. DOI: 10.1007/s10827-010-0275-y
Source: PubMed

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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Available from: Harel Z Shouval, May 16, 2014
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