Adaptive synapse-based neuron model with heterogeneous multistability and riddled basins

Biological neurons can exhibit complex coexisting multiple firing patterns dependent on initial conditions. To this end, this paper presents a novel adaptive synapse-based neuron (ASN) model with sine activation function. The ASN model has time-varying equilibria with the variation of externally app...

Full description

Saved in:
Bibliographic Details
Published in:Chaos (Woodbury, N.Y.) N.Y.), 2022-12, Vol.32 (12), p.123101-123101
Main Authors: Bao, H., Zhang, J., Wang, N., Kuznetsov, N. V., Bao, B. C.
Format: Article
Language:English
Subjects:
Algorithms
Attraction
Basins
Computer Simulation
Hopf bifurcation
Initial conditions
Microcontrollers
Neural Networks, Computer
Neurons - physiology
Stability
Synapses
Trigonometric functions
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Biological neurons can exhibit complex coexisting multiple firing patterns dependent on initial conditions. To this end, this paper presents a novel adaptive synapse-based neuron (ASN) model with sine activation function. The ASN model has time-varying equilibria with the variation of externally applied current and its equilibrium stability involves transitions between stable and unstable points through fold and Hopf bifurcations, resulting in complex distributions of attractive regions with heterogeneous multi-stability. Globally coexisting heterogeneous behaviors are studied by bifurcation diagram, phase portrait, dynamical distribution, and basin of attraction. The results show that the number of coexisting heterogeneous attractors can be up to 12, but for a simple neuron model, such a large number of coexisting heterogeneous attractors has not been reported in the relevant literature. Most interestingly, the ASN model also has riddled-like complex basins of attraction and four illustrative examples are depicted by the phase portraits with small changes of the initial conditions. Besides, the ASN model is implemented using a simple microcontroller platform, and various heterogeneous coexisting attractors are acquired experimentally to validate the numerical results.
ISSN:1054-1500
1089-7682
DOI:10.1063/5.0125611