The Open Cybernetics & Systemics Journal

2007, 1 : 1-4
Published online 2007 August 7. DOI: 10.2174/1874110X00701010001
Publisher ID: TOCSJ-1-1

Theoretical Analysis of Cross-Correlation of Time-Series Signals Computed by a Time-Delayed Hebbian Associative Learning Neural Network

David Tam
Department of Biological Sciences, University of North Texas, Denton, Texas 76203, USA.

ABSTRACT

A theoretical proof of the computational function performed by a time-delayed neural network implementing a Hebbian associative learning-rule is shown to compute the equivalent of cross-correlation of time-series functions, showing the relationship between correlation coefficients and connection-weights. The values of the computed correlation coefficients can be retrieved from the connection-weights.

Keywords:

Time-delayed neural networks, cross-correlation function, Hebbian learning rule.