Depending on the connectivity, recurrent networks of simple computational units can show
very different types of dynamics, ranging from totally ordered to chaotic. We analyze how the …

Experiences of animal and human beings are structured by the continuity of space and time
coupled with the uni-directionality of time. In addition to its pivotal position in spatial …

We present some recent investigations resulting from the modeling of neural networks as
dynamical systems, and deal with the following questions, adressed in the context of specific …

We derive rigorous results describing the asymptotic dynamics of a discrete time model of
spiking neurons introduced in Soula et al.(Neural Comput. 18, 1, 2006). Using symbolic …

To understand possible strategies of temporal spike coding in the central nervous system,
we study functional neuromimetic models of visual processing for static images. We will first …

In a previous paper we introduced the notion of frustrated chaos occurring in Hopfield
networks [Neural Networks 11 (1998) 1017]. It is a dynamical regime which appears in a …

The aim of the present paper is to study the effects of Hebbian learning in random recurrent
neural networks with biological connectivity, ie sparse connections and separate …

This paper presents an overview of some techniques and concepts coming from dynamical
system theory and used for the analysis of dynamical neural networks models. In a first …

This study evaluated the effects of an electromyography–functional electrical stimulation
interface (EMG–FES interface) combined with real-time balance and gait feedback on ankle …

This article deals with the links between the enaction paradigm and artificial intelligence.
Enaction is considered a metaphor for artificial intelligence, as a number of the notions …