Computing with spikes, architecture, properties and implementation of emerging paradigms

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Summary

This thesis proposes an efficient method to properly estimate the parameters (delayed synaptic weights) of a neural network from the observa- tion of its spiking dynamics, and proposes a C++ library, called EnaS, which is distributed under the CeCILL-C free license.

Type
preprint
Published
2011-01-24
Cited by
2
References
120
Access
Open access

Keywords

Computer science, Artificial neural network, Robustness (evolution), Implementation, Spiking neural network

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