ROSTRO-GONZALEZ , Horacio

In this thesis we study at a concrete practical level how computation with action potentials (spikes) can be performed. We address the problem of pro- gramming a dynamical system modeled as a neural network and considering both, hardware and software implementations. For this, we use a discrete- time spiking neuron model, which has been...

This paper presents a reverse engineering approach for parameter estimation in spiking neural networks (SNNs). We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate and fire type. Our approach aims at by-passing...

International audience

International audience

We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate-and-fire (gIF) type. The purpose is to study a class of algorithmic methods allowing one to calculate the proper parameters to reproduce exactly a given...

We consider the deterministic evolution of a time-discretized spiking network of neurons with connection weights having delays, modeled as a discretized neural network of the generalized integrate and fire (gIF) type. The purpose is to study a class of algorithmic methods allowing to calculate the proper parameters to reproduce exactly a given...

In this paper, both GPU (Graphing Processing Unit) based and FPGA (Field Programmable Gate Array) based hardware implementations for a discrete-time spiking neuron model are presented. This generalized model is highly adapted for large scale neural network implementations, since its dynamics are entirely represented by a spike train (binary...

We review here the basics of the formalism of Gibbs distributions and its numerical implementation, (its details published elsewhere \cite{vasquez-cessac-etal:10}, in order to characterizing the statistics of multi-unit spike trains. We present this here with the aim to analyze and modeling synthetic data, especially bio-inspired simulated data...