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A Neuromimetic Spiking Neural Network for Simulating Cortical Circuits

Abstract : In this paper, we present an hardware implementation of spiking neural networks based on analog integrated circuits. These ICs compute in real-time biologically realistic cortical neuron models. Each integrated circuit includes five neurons and analog memory cells to set and store the conductance model parameters. The system allows switching online the model of cortical neuron. 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 in biological real-time. It will allow extending the hybrid technique (connection between biological and artificial neurons) to Micro Electrode Array.
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Contributor : Chrystel Plumejeau <>
Submitted on : Wednesday, June 1, 2011 - 3:06:20 PM
Last modification on : Thursday, January 11, 2018 - 6:21:09 AM


  • HAL Id : hal-00597648, version 1


Filippo Grassia, Timothée Levi, Jean Tomas, Sylvie Renaud, Sylvain Saïghi. A Neuromimetic Spiking Neural Network for Simulating Cortical Circuits. 45th Annual Conference on Information Sciences ans Systems, Mar 2011, Baltimore, United States. pp.1. ⟨hal-00597648⟩



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