Neuromimetic ICs with analog cores: an alternative for simulating spiking neural networks

This paper aims at discussing the implementation of simulation systems for SNN based on analog computation cores (neuromimetic ICs). Such systems are an alternative to completely digital solutions for the simulation of spiking neurons or neural networks. Design principles for the neuromimetic ICs an...

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Bibliographic Details
Main Authors: Renaud, Sylvie, Tomas, Jean, Bornat, Yannick, Daouzli, Adel, Saighi, Sylvain
Format: Conference Proceeding
Language:English
Subjects:
Analog computers
Artificial neural networks
Biological neural networks
Biological system modeling
Biology computing
Computational modeling
Computer networks
Hardware
Neural networks
Neurons
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Summary:This paper aims at discussing the implementation of simulation systems for SNN based on analog computation cores (neuromimetic ICs). Such systems are an alternative to completely digital solutions for the simulation of spiking neurons or neural networks. Design principles for the neuromimetic ICs and the hosting systems are presented together with their features and performances. We summarize the existing architectures and neuron models used in such systems, when configured as stand-alone tools for simulating ANN or together with a neurophysiology set-up to study hybrid living artificial neural networks. As a primary illustration, we present results from one of the platforms: hardware simulations of single neurons and adaptive neural networks modeled using the Hodgkin-Huxley formalism for point neurons and spike-timing dependent plasticity algorithms for the network adaptation. Additional examples are detailed in the other papers of the session.
ISSN:0271-4302
2158-1525
DOI:10.1109/ISCAS.2007.378286