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Emergence of bursting in a network of memory dependent excitable and spiking leech-heart neurons
Author(s) -
Sanjeev Kumar Sharma,
Argha Mondal,
Arnab Mondal,
Ranjit Kumar Upadhyay,
Chittaranjan Hens
Publication year - 2020
Publication title -
journal of the royal society interface
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 139
eISSN - 1742-5689
pISSN - 1742-5662
DOI - 10.1098/rsif.2019.0859
Subject(s) - bursting , leech , neuron , neuroscience , physics , reentrancy , biological neuron model , coupling (piping) , alternation (linguistics) , synchronization (alternating current) , biological system , computer science , topology (electrical circuits) , biology , mathematics , combinatorics , materials science , linguistics , philosophy , world wide web , metallurgy , condensed matter physics
Excitable cells often produce different oscillatory activities that help us to understand the transmitting and processing of signals in the neural system. The diverse excitabilities of an individual neuron can be reproduced by a fractional-order biophysical model that preserves several previous memory effects. However, it is not completely clear to what extent the fractional-order dynamics changes the firing properties of excitable cells. In this article, we investigate the alternation of spiking and bursting phenomena of an uncoupled and coupled fractional leech-heart (L-H) neurons. We show that a complete graph of heterogeneous de-synchronized neurons in the backdrop of diverse memory settings (a mixture of integer and fractional exponents) can eventually lead to bursting with the formation of cluster synchronization over a certain threshold of coupling strength, however, the uncoupled L-H neurons cannot reveal bursting dynamics. Using the stability analysis in fractional domain, we demarcate the parameter space where the quiescent or steady-state emerges in uncoupled L-H neuron. Finally, a reduced-order model is introduced to capture the activities of the large network of fractional-order model neurons.

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