Statistical Mechanics of Temporal Association in Neural Networks
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We study the representation of static patterns and temporal associations in neural networks with a broad distribution of signal delays. For a certain class of such systems, a simple intuitive understanding of the spatio-temporal computation becomes possible with the help of a novel Lyapunov functional. It allows a quantitative study of the asymptotic network behavior through a statistical mechanical analysis. We present analytic calculations of both retrieval quality and storage capacity and compare them with simulation results.
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