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

作者：Gavornik Jeffrey P; Shouval Harel Z^*

来源：Journal of Computational Neuroscience, 2011, 30(2): 501-513.

DOI：10.1007/s10827-010-0275-y

• Recurrent network
• Mean field theory
• Synaptic plasticity
• Spontaneous activity
• Reinforcement learning

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.

• 出版日期2011-4