Learning Effects in Coupled Arrays of Cellular Neural Oscillators

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.