Weakly connected oscillatory networks for dynamic pattern recognition

Recent studies on the thalamo-cortical system have shown that weakly connected oscillatory networks (WCNs) exhibit associative properties and can be exploited for dynamic pattern recognition. In this manuscript we focus on WCNs, composed of oscillators that admit of a Lurpsilae like description and are organized in such a way that they communicate one another, through a common medium. The main dynamic features are investigated by exploiting the phase deviation equation (i.e. the equation that describes the phase deviation due to the weak coupling). Furthermore, by using a simple learning algorithm, the phase-deviation equation is designed in such a way that given sets of patterns can be stored and recalled. In particular, two models of WCNs associative and dynamic memories are provided.