Abstract We discuss how to characterize long-time dynamics of non-smooth dynamical systems, such as integrate-and-fire (I&F) like neuronal network, using Lyapunov exponents and present a stable numerical method for the accurate evaluation of the spectrum of Lyapunov exponents for this large class of dynamics. These dynamics contain (i) jump conditions as in the firing-reset dynamics and (ii) degeneracy such as in the refractory period in which voltage-like variables of the network collapse to a single constant value. Using the networks of linear I&F neurons, exponential I&F neurons, and I&F neurons with adaptive threshold, we illustrate our method and discuss the rich dynamics of these networks
The paper considers a class of additive neural networks where the neuron activations are modeled by ...
Lyapunov exponents of a dynamical system give information about its long-term evolution. Exponents e...
The paper considers a class of additive neural networks where the neuron activations are modeled by ...
The aim of this work is to introduce and study simple neuron models with a dynamic threshold: The in...
The aim of this work is to introduce and study simple neuron models with a dynamic threshold: The in...
We discuss the notion of Liapunov exponent for integrate-and-fire (IF) type dynamical systems. In co...
We discuss the notion of Liapunov exponent for integrate-and-fire (IF) type dynamical systems. In co...
Typically, to estimate the whole spectrum of n Lyapunov Exponents (LEs), it is necessary to integrat...
We discuss the notion of Liapunov exponent for integrate-and-fire (IF) type dynamical systems. In co...
Lyaponov exponents are a generalization of the eigenvalues of a dynamical system at an equilibrium p...
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do ...
Because of the complex properties of high-dimensional nonlinear systems, e.g, neural networks and ca...
In this article, we consider chaotic behavior happened in nonsmooth dynamical systems. To quantify s...
The time-interval sequences and the spatiotemporal patterns of the firings of a coupled neuronal net...
Lyapunov exponents of a dynamical system give information about its long-term evolution. Exponents e...
The paper considers a class of additive neural networks where the neuron activations are modeled by ...
Lyapunov exponents of a dynamical system give information about its long-term evolution. Exponents e...
The paper considers a class of additive neural networks where the neuron activations are modeled by ...
The aim of this work is to introduce and study simple neuron models with a dynamic threshold: The in...
The aim of this work is to introduce and study simple neuron models with a dynamic threshold: The in...
We discuss the notion of Liapunov exponent for integrate-and-fire (IF) type dynamical systems. In co...
We discuss the notion of Liapunov exponent for integrate-and-fire (IF) type dynamical systems. In co...
Typically, to estimate the whole spectrum of n Lyapunov Exponents (LEs), it is necessary to integrat...
We discuss the notion of Liapunov exponent for integrate-and-fire (IF) type dynamical systems. In co...
Lyaponov exponents are a generalization of the eigenvalues of a dynamical system at an equilibrium p...
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do ...
Because of the complex properties of high-dimensional nonlinear systems, e.g, neural networks and ca...
In this article, we consider chaotic behavior happened in nonsmooth dynamical systems. To quantify s...
The time-interval sequences and the spatiotemporal patterns of the firings of a coupled neuronal net...
Lyapunov exponents of a dynamical system give information about its long-term evolution. Exponents e...
The paper considers a class of additive neural networks where the neuron activations are modeled by ...
Lyapunov exponents of a dynamical system give information about its long-term evolution. Exponents e...
The paper considers a class of additive neural networks where the neuron activations are modeled by ...