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Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Minglin Ma, Kangling 康灵 Xiong 熊, Zhijun 志军 Li 李, Shaobo He

2023Chinese Physics B54 citationsDOIOpen Access PDF

Abstract

Synaptic crosstalk is a prevalent phenomenon among neuronal synapses, playing a crucial role in the transmission of neural signals. Therefore, considering synaptic crosstalk behavior and investigating the dynamical behavior of discrete neural networks are highly necessary. In this paper, we propose a heterogeneous discrete neural network (HDNN) consisting of a three-dimensional KTz discrete neuron and a Chialvo discrete neuron. These two neurons are coupled mutually by two discrete memristors and the synaptic crosstalk is considered. The impact of crosstalk strength on the firing behavior of the HDNN is explored through bifurcation diagrams and Lyapunov exponents. It is observed that the HDNN exhibits different coexisting attractors under varying crosstalk strengths. Furthermore, the influence of different crosstalk strengths on the synchronized firing of the HDNN is investigated, revealing a gradual attainment of phase synchronization between the two discrete neurons as the crosstalk strength decreases.

Topics & Concepts

MemristorCrosstalkArtificial neural networkComputer sciencePhysicsArtificial intelligenceQuantum mechanicsOpticsAdvanced Memory and Neural ComputingNeural dynamics and brain functionstochastic dynamics and bifurcation