Learning and generation of long-range correlated sequences

A. Priel, I. Kanter

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)1617-1621
Number of pages5
JournalPhysical Review E
Issue number2
StatePublished - 2000


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