Information retrieval from modified Kuramoto network by resonant synchronization

School of Physics and Information Technology, Shaanxi Normal University - Xi'an 710061, PRC

^(a) zhanglincn@snnu.edu.cn

Received: 9 July 2019
Accepted: 3 January 2020

Abstract

A collective behavior of resonant synchronization (RS) in an inhomogeneous network is investigated and its function to retrieve information from encoding networks is proposed based on the RS mechanism. We use modified Kuramoto phase oscillators to simulate normal neurons in self-oscillation state, and investigate the collective responses of a fully connected neuronal network to the stimulus signals when the network is in a critical state just below the synchronization threshold. The stimulus driving on one node at resonant frequencies can stimulate the unsynchronized rotators across the network into collective synchronized states locked to similar frequencies, and thus recover the memorized locations through the synchronized patterns related to the predetermined frequency distributions among the oscillators. This model suggests a potential mechanism to explain how brain stores and retrieves information from resonant synchronization patterns emerging from an inhomogeneous critical neuronal network stimulated by the resonant external driving.