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 pattern

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

MARINARO, Maria;SCARPETTA, Silvia;YOSHIOKA, MASAHIKO
2007-01-01

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 pattern
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/1850457
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