Dynamical robustness and firing modes in multilayer memristive neural networks of nonidentical neurons

作者：

Highlights：

• Dynamical robustness and the transition of firing modes of multilayer memristive neural network consisting of nonidentical neurons have been investigated in detail.

• The dynamic effects of memristive synapses coupling which are either cubic order flux or quadratic flux are detected.

• We found that the ratio of inactive neurons switches the firing patterns of the active neuron among periodic bursting, chaotic bursting and spiking-like.

• The dynamics of memristive neural network is also verified in the circuits built on Multisim.

摘要

•Dynamical robustness and the transition of firing modes of multilayer memristive neural network consisting of nonidentical neurons have been investigated in detail.•The dynamic effects of memristive synapses coupling which are either cubic order flux or quadratic flux are detected.•We found that the ratio of inactive neurons switches the firing patterns of the active neuron among periodic bursting, chaotic bursting and spiking-like.•The dynamics of memristive neural network is also verified in the circuits built on Multisim.

论文关键词：Dynamical robustness,Firing modes,Multilayer neural networks,Memristor

论文评审过程：Received 16 December 2020, Revised 22 April 2021, Accepted 13 May 2021, Available online 3 June 2021, Version of Record 3 June 2021.

论文官网地址：https://doi.org/10.1016/j.amc.2021.126384