Learning of oscillatory correlated patterns in a cortical network by a STDP-based learning rule.

Abstract:

:In this paper, we propose an iterative learning rule that allows the imprinting of correlated oscillatory patterns in a model of the hippocampus able to work as an associative memory for oscillatory spatio-temporal patterns. We analyze the dynamics in the Fourier domain, showing how the network selectively amplify or distort the Fourier components of the input, in a manner which depends on the imprinted patterns. We also prove that the proposed iterative local rule converges to the pseudo-inverse rule generalized to oscillatory patterns.

journal_name

Math Biosci

journal_title

Mathematical biosciences

authors

Marinaro M,Scarpetta S,Yoshioka M

doi

10.1016/j.mbs.2006.10.001

subject

Has Abstract

pub_date

2007-06-01 00:00:00

pages

322-35

issue

2

eissn

0025-5564

issn

1879-3134

pii

S0025-5564(06)00194-5

journal_volume

207

pub_type

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