Convergence in an Adaptive Neural Network: The Influence of Noise Inputs Correlation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009

AUTHORS

Adel Daouzli , Sylvain Saïghi , Michelle Rudolph , Alain Destexhe , Sylvie Renaud

ABSTRACT

This paper presents a study of convergence modalities in a small adaptive network of conductance-based neurons, receiving input patterns with different degrees correlation . The models for the neurons, synapses and plasticity rules (STDP) have a common biophysics basis. The neural network is simulated using a mixed analog-digital platform, which performs real-time simulations. We describe the study context, and the models for the neurons and for the adaptation functions. Then we present the simulation platform, including analog integrated circuits to simulate the neurons and a real-time software to simulate the plasticity. We also detail the analysis tools used to evaluate the final state of the network by the way of its post-adaptation synaptic weights. Finally, we present experimental results, with a systematic exploration of the network convergence when varying the input correlation, the initial weights and the distribution of hardware neurons to simulate the biological variability. More... »

PAGES

140-148

References to SciGraph publications

Book

TITLE

Bio-Inspired Systems: Computational and Ambient Intelligence

ISBN

978-3-642-02477-1
978-3-642-02478-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-02478-8_18

DOI

http://dx.doi.org/10.1007/978-3-642-02478-8_18

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003881184


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