Learning temporal clusters with synaptic facilitation and lateral inhibition

Short-term synaptic plasticity has been proposed as a way for cortical neurons to process temporal information. We present a model network that uses short-term plasticity to implement a temporal clustering algorithm. The model's facilitory synapses learn temporal signals drawn from mixtures of...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 65; pp. 877 - 884
Main Authors Baker, Chris L., Shon, Aaron P., Rao, Rajesh P.N.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.06.2005
Subjects
Dynamic synapses
Facilitation
Mixture models
Temporal filtering
Facilitation
Temporal filtering
Dynamic synapses
Mixture models
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ISSN0925-2312
1872-8286
DOI10.1016/j.neucom.2004.10.086

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Summary:Short-term synaptic plasticity has been proposed as a way for cortical neurons to process temporal information. We present a model network that uses short-term plasticity to implement a temporal clustering algorithm. The model's facilitory synapses learn temporal signals drawn from mixtures of nonlinear processes. Units in the model correspond to populations of cortical pyramidal cells arranged in columns; each column consists of neurons with similar spatiotemporal receptive fields. Clustering is based on mutual inhibition similar to Kohonen's SOMs. A generalized expectation maximization (GEM) algorithm, guaranteed to increase model likelihood with each iteration, learns the synaptic parameters.
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2004.10.086