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Forced phase-locked states and information retrieval in a two-layer network of oscillatory neurons with directional connectivity

Victor Kazantsev

Alexey Pimashkin

  • Institute of Applied Physics of RAS, 46 Uljanov street, 603950 Nizhny Novgorod, Russia and Department of Neurodynamics and Neurobiology, Nizhny Novgorod State University, 23 Gagarin Ave., 603950 Nizhny Novgorod, Russia

  • Department of Neurodynamics and Neurobiology, Nizhny Novgorod State University, 23 Gagarin Ave., 603950 Nizhny Novgorod, Russia

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Phys. Rev. E 76, 031912 – Published 12 September, 2007

DOI: https://doi.org/10.1103/PhysRevE.76.031912

Abstract

We propose two-layer architecture of associative memory oscillatory network with directional interlayer connectivity. The network is capable to store information in the form of phase-locked (in-phase and antiphase) oscillatory patterns. The first (input) layer takes an input pattern to be recognized and their units are unidirectionally connected with all units of the second (control) layer. The connection strengths are weighted using the Hebbian rule. The output (retrieved) patterns appear as forced-phase locked states of the control layer. The conditions are found and analytically expressed for pattern retrieval in response on incoming stimulus. It is shown that the system is capable to recover patterns with a certain level of distortions or noises in their profiles. The architecture is implemented with the Kuramoto phase model and using synaptically coupled neural oscillators with spikes. It is found that the spiking model is capable to retrieve patterns using the spiking phase that translates memorized patterns into the spiking phase shifts at different time scales.

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Outline

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Phys. Rev. E 76, 031912– Published 12 September, 2007
  • Received 11 March 2007

© 2007 American Physical Society

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