Abstract
Several studies in neuroscience have shown that nonlinear oscillatory networks represent bio-inspired models for information and image processing. Recent studies on the thalamo-cortical system have shown that weakly connected oscillatory networks (WCONs) exhibit associative properties and can be exploited for dynamic pattern recognition. In this manuscript we focus on WCONs, composed of oscillators that adhere to a Lur'e 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 variation of each oscillator, due to weak coupling). Firstly a very accurate analytic expression of the phase deviation equation is derived, by jointly describing the function technique and the Malkin's Theorem. 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 WCONs are presented as examples of associative and dynamic memories.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
