The Nagumo-Sato model is a simple mathematical expression of a single neuron, and it is categorized as a discrete-time hybrid dynamical system. To compute bifurcation sets in such a discrete-time hybrid dynamical system accurately, conditions for periodic solutions and bifurcations are formulated herewith as a boundary value problem, and Newton's method is implemented to solve that problem. As the results of the analysis, the following properties are obtained: border-collision bifurcations play a dominant role in dynamical behavior of the model; chaotic regions are distinguished by tangent bifurcations; and multi-stable attractors are observed in its coupled system. We demonstrate several bifurcation diagrams and corresponding topological p...
Map-based neuron models are an important tool in modelling neural dynamics and sometimes can be cons...
The Fitzhugh-Nagumo model originally consists of two non-linear differential equations, which simula...
AbstractIn this paper, we consider a discrete-time tabu learning single neuron model. After investig...
The main features and components of a new so-called bifurcation theory of nonlinear dynamics and cha...
The behavior of neurons can be modeled by the FitzHugh-Nagumo oscillator model, consisting...
Neurons are the central biological objects in understanding how the brain works. The famous Hodgkin-...
We propose a computation method to obtain bifurcation sets of periodic solutions in non-autonomous s...
A lot of works are dedicated to studying different mathematical models of competition with goal of d...
A theoretical bifurcation control strategy is presented for a single Fitzhugh-Nagumo (FN) type neuro...
A class of discrete-time system modelling a network with two neurons is considered. First, we invest...
The importance of piecewise-smooth and especially that of discontinuous system models is well-establ...
In this short paper we present a detailed analysis of the dynamics of a system of two coupled Fitzhu...
We study the local dynamics and bifurcation analysis of a discrete-time modified Nicholson–Bailey mo...
AbstractWe consider a discrete-time system modelling a network of two neurons. The situationwith sel...
A new approach for the global bifurcation analysis, based on the ideas of Poincare, Birkho_ and Andr...
Map-based neuron models are an important tool in modelling neural dynamics and sometimes can be cons...
The Fitzhugh-Nagumo model originally consists of two non-linear differential equations, which simula...
AbstractIn this paper, we consider a discrete-time tabu learning single neuron model. After investig...
The main features and components of a new so-called bifurcation theory of nonlinear dynamics and cha...
The behavior of neurons can be modeled by the FitzHugh-Nagumo oscillator model, consisting...
Neurons are the central biological objects in understanding how the brain works. The famous Hodgkin-...
We propose a computation method to obtain bifurcation sets of periodic solutions in non-autonomous s...
A lot of works are dedicated to studying different mathematical models of competition with goal of d...
A theoretical bifurcation control strategy is presented for a single Fitzhugh-Nagumo (FN) type neuro...
A class of discrete-time system modelling a network with two neurons is considered. First, we invest...
The importance of piecewise-smooth and especially that of discontinuous system models is well-establ...
In this short paper we present a detailed analysis of the dynamics of a system of two coupled Fitzhu...
We study the local dynamics and bifurcation analysis of a discrete-time modified Nicholson–Bailey mo...
AbstractWe consider a discrete-time system modelling a network of two neurons. The situationwith sel...
A new approach for the global bifurcation analysis, based on the ideas of Poincare, Birkho_ and Andr...
Map-based neuron models are an important tool in modelling neural dynamics and sometimes can be cons...
The Fitzhugh-Nagumo model originally consists of two non-linear differential equations, which simula...
AbstractIn this paper, we consider a discrete-time tabu learning single neuron model. After investig...