KIP publications

 
year 2006
author(s) Johannes Schemmel, Andreas Gruebl, Karlheinz Meier, Eilif Mueller
title Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model
KIP-Nummer HD-KIP 06-33
KIP-Gruppe(n) F9
document type Paper
source Proceedings of the 2006 International Joint Conference on Neural Networks (IJCNN 2006), 1-6, IEEE Press (2006)
Abstract (en) This paper describes an area-efficient mixed-signal implementation of synapse-based long term plasticity realized in a VLSI model of a spiking neural network. The artificial synapses are based on an implementation of spike time dependent plasticity (STDP). In the biological specimen, STDP is a mechanism acting locally in each synapse. The presented electronic implementation succeeds in maintaining this high level of parallelism and simultaneously achieves a synapse density of more than 9k synapses per mm^2 in a 180 nm technology. This allows the construction of neural micro-circuits close to the biological specimen while maintaining a speed several orders of magnitude faster than biological real time. The large acceleration factor enhances the possibilities to investigate key aspects of plasticity, e.g. by performing extensive parameter searches.
Datei schemmel_ijcnn2006
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