Spiking neural networks for reconfigurable POEtic tissue

Authors:
Jan Eriksson;Oriol Torres;Andrew Mitchell;Gayle Tucker;Ken Lindsay;David Halliday;Jay Rosenberg;Juan-Manuel Moreno;Alessandro E . P. Villa
Affiliations:
Lab. of Neuroheuristics, University of Lausanne, Lausanne, Switzerland;Technical University of Catalunya, Barcelona, Spain;University of York, York, UK;University of Glasgow, UK;University of Glasgow, UK;University of York, York, UK;University of Glasgow, UK;Technical University of Catalunya, Barcelona, Spain;Lab. of Neuroheuristics, University of Lausanne, Lausanne, Switzerland and University Joseph-Fourier, Grenoble, France
Venue:
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Year:
2003

Citing 6
Cited 6

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

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Abstract

Vertebrate and most invertebrate organisms interact with their environment through processes of adaptation and learning. Such processes are generally controlled by complex networks of nerve cells, or neurons, and their interactions. Neurons are characterized by all-or-none discharges - the spikes - and the time series corresponding to the sequences of the discharges - the spike trains - carry most of the information used for intercellular communication. This paper describes biologically inspired spiking neural network models suitable for digital hardware implementation. We consider bio-realism, hardware friendliness, and performance as factors which influence the ability of these models to integrate into a flexible computational substrate inspired by evolutionary, developmental and learning aspects of living organisms. Both software and hardware simulations have been used to assess and compare the different models to determine the most suitable spiking neural network model.