Roles

Student author: Ritwik K. Niyogi

Document Type

Article

Publication Date

12-2009

Department

Physics

Language

English

Publication Title

Physical Review E

Abstract

We investigate the role of the learning rate in a Kuramoto Model of coupled phase oscillators in which the coupling coefficients dynamically vary according to a Hebbian learning rule. According to the Hebbian theory, a synapse between two neurons is strengthened if they are simultaneously coactive. Two stable synchronized clusters in antiphase emerge when the learning rate is larger than a critical value. In such a fast learning scenario, the network eventually constructs itself into an all-to-all coupled structure, regardless of initial conditions in connectivity. In contrast, when learning is slower than this critical value, only a single synchronized cluster can develop. Extending our analysis, we explore whether self-development of neuronal networks can be achieved through an interaction between spontaneous neural synchronization and Hebbian learning. We find that self-development of such neural systems is impossible if learning is too slow. Finally, we demonstrate that similar to the acquisition and consolidation of long-term memory, this network is capable of generating and remembering stable patterns.

Comments

This published version is made available on Dickinson Scholar with the permission of the publisher. For more information on the published version, visit American Physical Society's Website.

©2009 The American Physical Society

DOI

10.1103/PhysRevE.80.066213

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