Hardware System for Biologically Realistic, Plastic, and Real-time Spiking Neural Network Simulations

Abstract : In this paper, we present an hardware implementation of spiking neural networks based on analog integrated circuits. These ICs compute in real-time a biologically realistic neuron models. Each integrated circuit includes five neurons and analog memory cells to set and store the conductance model parameters, and eventually optimize it to compensate the analog circuit variability. The circuits are embedded in a multi-board system all connected to a backplane with daisy-chain facilities. Each action potential computed by analog neuromimetic chips is time-stamped when detected by digital device (FPGA). These FPGAs are also in charge of the real-time plasticity computation and of controlling inter-boards communication. The implemented neural plasticity is also biological relevant thanks to its time dependent computation. The whole system is designed to compute programmable models and connectivity schemes.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00505776
Contributor : Chrystel Plumejeau <>
Submitted on : Monday, July 26, 2010 - 10:27:49 AM
Last modification on : Thursday, January 11, 2018 - 6:21:06 AM

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  • HAL Id : hal-00505776, version 1

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S. Saïghi, Timothée Levi, B. Belhadj, O. Malot, J. Tomas. Hardware System for Biologically Realistic, Plastic, and Real-time Spiking Neural Network Simulations. IEEE Int. Joint Conférence on Neural Networks, Jul 2010, Barcelone, Spain. pp.2189-2194. ⟨hal-00505776⟩

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