Weights Convergence and Spikes Correlation in an Adaptive Neural Network Implemented on VLSI

Abstract : This paper presents simulations of a conductance-based neural network implemented on a mixed hardwaresoftware simulation system. Synaptic connections follow a bio-realistic STDP rule. Neurons receive correlated input noise patterns, resulting in a weights convergence in a confined range of conductance values. The correlation of the output spike trains depends on the correlation degree of the input patterns
Type de document :
Communication dans un congrès
Bio-inspired Systems and Signal Processing (BIOSIGNALS), Jan 2008, France. pp.286-291, 2008
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https://hal.archives-ouvertes.fr/hal-00288431
Contributeur : Sylvain Saighi <>
Soumis le : lundi 16 juin 2008 - 19:32:45
Dernière modification le : jeudi 11 janvier 2018 - 06:21:07

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  • HAL Id : hal-00288431, version 1

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Adel Daouzli, Sylvain Saïghi, Laure Buhry, Yannick Bornat, Sylvie Renaud. Weights Convergence and Spikes Correlation in an Adaptive Neural Network Implemented on VLSI. Bio-inspired Systems and Signal Processing (BIOSIGNALS), Jan 2008, France. pp.286-291, 2008. 〈hal-00288431〉

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