Implementation of biologically plausible spiking neural networks models on the POEtic tissue

Authors:
J. Manuel Moreno;Jan Eriksson;Javier Iglesias;Alessandro E. P. Villa
Affiliations:
Dept. of Electronic Engineering, Technical University of Catalunya, Barcelona, Spain;Laboratory of Neuroheuristics, Information Systems Department INFORGE, University of Lausanne, Lausanne, Switzerland;Laboratory of Neuroheuristics, Information Systems Department INFORGE, University of Lausanne, Lausanne, Switzerland;INSERM U318, University Joseph-Fourier Grenoble 1, Pavillon B, CHUG Michallon, Grenoble Cedex 9, France
Venue:
ICES'05 Proceedings of the 6th international conference on Evolvable Systems: from Biology to Hardware
Year:
2005

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Abstract

Recent experimental findings appear to confirm that the nature of the states governing synaptic plasticity is discrete rather than continuous. This means that learning models based on discrete dynamics have more chances to provide a ground basis for modelling the underlying mechanisms associated with plasticity processes in the brain. In this paper we shall present the physical implementation of a learning model for Spiking Neural Networks (SNN) that is based on discrete learning variables. After optimizing the model to facilitate its hardware realization it is physically mapped on the POEtic tissue, a flexible hardware platform for the implementation of bio-inspired models. The implementation estimates obtained show that is possible to conceive a large-scale implementation of the model able to handle real-time visual recognition tasks.