Modeling biological rhythmic patterns using asymmetric Hopfield neural networks
Zhijun Yang, Weiping Lu, Felipe M. G. França · 2001
In this report we present a novel approach to the modelling of the collective behaviour of inhibitory neuronal networks through combining Hopfield neural networks with a distributed algorithm of Scheduling by Multiple Edge Reversal (SMER). We show that this new model can conveniently replicate sophisticatedly coordinated, spatio-temporal dynamics of biological rhythms, and have been applied to predict or reproduce the interesting behaviours of almost all biological oscillatory neuronal networks of arbitrary topology. To illustrate this new approach, we choose three quadrupedal common rhythmic gait patterns as case study. Key words: Biological rhythms, Central pattern generators, Distributed algorithms, Hopfield neural networks, Artificial intelligence. 1