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

Abstract : 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.
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Communication dans un congrès
ISCAS'07, May 2007, New-Orleans, United States. IEEE, pp.3355-3358, 2007
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  • HAL Id : hal-00161313, version 1

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Sylvie Renaud, Jean Tomas, Yannick Bornat, Adel Daouzli, Sylvain Saïghi. Neuromimetic ICs with analog cores: an alternative for simulating spiking neural networks. ISCAS'07, May 2007, New-Orleans, United States. IEEE, pp.3355-3358, 2007. 〈hal-00161313〉

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