Synchronization of Neuronal Circuits with Ring Connection on PSpice

Abstract

The modes of electric activities in neurons and wave propagation between neurons can be changed by using appropriate external forcing or coupling type. Based on the PSpice tool, the coupling synchronization between three Hindmarsh-Rose neurons is investigated. The neuronal circuit is also designed, and the Schmitt trigger circuit is used as peak detector by transmitting the analog signal into digital signal; thus, the rhythm of electric activities of neurons can be analyzed. By selecting different initial states (spiking or bursting), different synchronization states are found. In the case of spiking state, appropriate coupling intensity is effective in realizing rhythm synchronization and then homogenous state is observed by further increasing the coupling intensity. In the case of bursting state, rhythm synchronization is enhanced under appropriate coupling intensity while desynchronization occurs by further increasing the coupling intensity.

Robust Adaptive Synchronization of Ring Configured Uncertain Chaotic FitzHugh–Nagumo Neurons under Direction-Dependent Coupling

“…The purpose of this study is to model the neuronal behavior and to provide a synchronization control remedy for attaining the coherent behavior of the neurons. The proposed network model of ring configured four FHN neurons under direction-dependent coupling [by accounting the results of Iqbal et al ( 2014 ) and Yuan et al ( 2016 )] is given by where x 1 and y 1 are the model states of the master FHN neuron, namely, the activation potential and the recovery variable, respectively. The x 2 and y 2 represent the states of the first slave neuron, x 3 and y 3 correspond to the second slave neuron states, and x 4 and y 4 are the states for the fourth neuron.…”

“…To a certain extent, efforts have been dedicated to the study of the dynamics of the neuronal networks coupled in a ring fashion, specifically by exploiting the impact of time delays (Campbell et al, 2005 ; Xu, 2008 ; Song and Xu, 2012 ; Zhang, 2014 ; Wang et al, 2015 ; Mao and Wang, 2016 ; Yuan et al, 2016 ; Mao, 2017 ). A recent work by Zhou et al ( 2009 ) extended the synchronization problem to a network of coupled FHN neurons and explored the impact of the gap junctions on the network.…”