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Learning Effects in Coupled Arrays of Cellular Neural Oscillators

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Abstract

We analyze spatio-temporal dynamics of coupled neural oscillatory arrays. The interconnected oscillators can produce a wide range of dynamics, including quasi-periodic limit cycles, chaotic waveforms, and intermittent chaotic oscillations. We study the role of distributed input bias and develop methods for learning the input patterns. After learning, the coupled oscillators produce large-scale synchronized, narrow-band oscillations in response to the learned patterns. We study patterns of amplitude modulations that span the whole lattice graph. The presented results correspond to Freeman’s 6th building block of neurodynamics.

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Acknowledgments

This work is supported in part by Defense Advanced Research Projects Agency (DARPA) Physical Intelligence Program in an HRL subcontract and by Air Force Office of Scientific Research (AFOSR) in the Mathematics and Cognition Program.

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Authors and Affiliations

  1. University of Memphis, Memphis, TN, USA

    Robert Kozma

  2. Tulane University, New Orleans, LA, USA

    Marko Puljic

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  1. Robert Kozma
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  2. Marko Puljic
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Correspondence to Robert Kozma.

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Kozma, R., Puljic, M. Learning Effects in Coupled Arrays of Cellular Neural Oscillators. Cogn Comput 5, 164–169 (2013). https://doi.org/10.1007/s12559-012-9182-z

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  • DOI: https://doi.org/10.1007/s12559-012-9182-z

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