GIRAU , Bernard

SOMA is a France-Switzerland collaborative project which aims to develop a computing machine with self-organizing properties inspired by the functioning of the brain. The SOMA project addresses this challenge by lying at the intersection of four main research fields, namely adaptive reconfigurable computing, cellular computing, computational...

This paper presents a numerical analysis of the role of asymptotic dynamics in the design of hardware-based implementations of the generalised integrate-and-fire (gIF) neuron models. These proposed implementations are based on extensions of the discrete-time spiking neuron model, which was introduced by Soula et al., and have been implemented...

This paper presents the self-organized neuromorphic architecture named SOMA. The objective is to study neural-based self-organization in computing systems and to prove the feasibility of a self-organizing hardware structure. Considering that these properties emerge from large scale and fully connected neural maps, we will focus on the...

Self-organization is a bio-inspired feature that has been poorly developed when it comes to talking about hardware architectures. Cellular computing approaches have tackled it without considering input data. This paper introduces the SOMA architecture, which proposes an approach for self-organizing machine architectures. In...

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...