Abstract

Spike-timing-dependent plasticity (STDP) is an important driving force of self-organization in neural systems. With properly chosen input signals, STDP can yield a synaptic pruning process, whose functional role needs to be further investigated. We explore this issue from an information theoretic standpoint. Temporally correlated stimuli are introduced to neurons of an input layer. Then synapses on the dendrite, and thus the receptive field, of an output neuron are refined by STDP. The mutual information between input and output spike trains is calculated with the context tree method. The results show that synapse removal can enhance information transfer, i.e., that "less can be more" under certain constraints that stress the balance between potentiation and depression dictated by the parameters of the STDP rule, as well as the temporal scale of the input correlation.

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Publisher Full Text

Authors

Ren Q, Zhang Z, Zhao J

Institution

School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, People's Republic of China.

Source

Physical review. E, Statistical, nonlinear, and soft matter physics 85:2 Pt 1 2012 Feb pg 022901

MeSH

Action Potentials
Animals
Biological Clocks
Computer Simulation
Humans
Information Storage and Retrieval
Models, Neurological
Nerve Net
Neuronal Plasticity
Neurons
Synaptic Transmission

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

22463266