Can dynamic neural filters produce pseudo-random sequences?

Authors:
Yishai M. Elyada;David Horn
Affiliations:
Max-Planck Institute of Neurobiology, Department of Systems and Computational Neurobiology, Martinsried, Germany;School of Physics and Astronomy, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv, Israel
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

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Abstract

Dynamic neural filters (DNFs) are recurrent networks of binary neurons. Under proper conditions of their synaptic matrix they are known to generate exponentially large cycles. We show that choosing the synaptic matrix to be a random orthogonal one, the average cycle length becomes close to that of a random map. We then proceed to investigate the inversion problem and argue that such a DNF could be used to construct a pseudo-random generator. Subjecting this generator’s output to a battery of tests we demonstrate that the sequences it generates may indeed be regarded as pseudo-random.