Sigmoidal synaptic learning produces mutual stabilization in chaotic FitzHugh-Nagumo model.
Chaos
; 30(6): 063108, 2020 Jun.
Article
en En
| MEDLINE
| ID: mdl-32611069
ABSTRACT
This paper investigates the interaction between two coupled neurons at the terminal end of a long chain of neurons. Specifically, we examine a bidirectional, two-cell FitzHugh-Nagumo neural model capable of exhibiting chaotic dynamics. Analysis of this model shows how mutual stabilization of the chaotic dynamics can occur through sigmoidal synaptic learning. Initially, this paper begins with a bifurcation analysis of an adapted version of a previously studied FitzHugh-Nagumo model that indicates regions of periodic and chaotic behaviors. Through allowing the synaptic properties to change dynamically via neural learning, it is shown how the system can evolve from chaotic to stable periodic behavior. The driving factor between this transition is representative of a stimulus coming down a long neural pathway. The result that two chaotic neurons can mutually stabilize via a synaptic learning implies that this may be a mechanism whereby neurons can transition from a disordered, chaotic state to a stable, ordered periodic state that persists. This approach shows that even at the simplest level of two terminal neurons, chaotic behavior can become stable, sustained periodic behavior. This is achieved without the need for a large network of neurons.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Potenciales de Acción
/
Transmisión Sináptica
/
Neuronas
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Año:
2020
Tipo del documento:
Article