期刊
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
卷 30, 期 2, 页码 501-513出版社
SPRINGER
DOI: 10.1007/s10827-010-0275-y
关键词
Recurrent network; Mean field theory; Synaptic plasticity; Spontaneous activity; Reinforcement learning
资金
- NINDS NIH HHS [P01 NS038310-10, P01 NS038310] Funding Source: Medline
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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