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Learning and generation of long-range correlated sequences

A. Priel·I. Kanter
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Abstract

We study the capability to learn and to generate long-range, power-law correlated sequences by a fully connected asymmetric network. The focus is set on the ability of neural networks to extract statistical features from a sequence. We demonstrate that the average power-law behavior is learnable, namely, the sequence generated by the trained network obeys the same statistical behavior. The interplay between a correlated weight matrix and the sequence generated by such a network is explored. A weight matrix with a power-law correlation function along the vertical direction, gives rise to a sequence with a similar statistical behavior.

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A. Priel, I. Kanter. 2000-07-05. Learning and generation of long-range correlated sequences. https://doi.org/10.1103/physreve.62.1617

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