On The Synchronization Of Memristive Neural Networks
Keywords:
Neuron models, Synchronization, Memristive SynapsesAbstract
Neural systems consist of neurons connected by synapses that can be modeled as circuits to represent its electrical behavior. In particular, when memristors are used to capture features of biological neurons or synapses, the resulting model is called a memristive neural network (MNN). In this contribution we propose a class of MNN, based on Hindmarsh–Rose (HR) neuron models coupled by memristor as synapses with parameters chosen to produce typical responses of chemical synapses. We show that identical synchronization in this class of MNN is achieved for a sufficiently large coupling strength while the features of passive memristors as chemical synapses affect the emergence of synchronization. Our results are illustrated with numerical simulations.
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Copyright (c) 2025 Eduardo Elpidio Rodríguez Martínez, José de Jesús Esquivel Gómez, Juan Gonzalo Barajas Ramírez
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