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Communication Dans Un Congrès Année : 2019

Real-time implementation of ReSuMe learning in Spiking Neural Network

Résumé

Neuromorphic systems are designed by mimicking or being inspired by the nervous system, which realizes robust, autonomous, and power-efficient information processing by highly parallel architecture. Supervised learning was proposed as a successful concept of information processing in neural network. Recently, there has been an increasing body of evidence that instruction-based learning is also exploited by the brain. ReSuMe is a proposed algorithm by Ponulak and Kasinski in 2010. It proposes a supervised learning for biologically plausible neurons that reproduce template signals (instructions) or patterns encoded in precisely timed sequences of spikes. Here, we present a real-time ReSuMe learning implementation on FPGA using Leaky Integrate-and-fire (LIF) Spiking Neural Network (SNN). FPGA allows real-time implementation and embedded system. We show that this implementation can make successful the learning on a specific pattern.
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Dates et versions

hal-02484014 , version 1 (19-02-2020)

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  • HAL Id : hal-02484014 , version 1

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Yang Xia, Seiji Uenohara, Kazuyuki Aihara, Timothée Levi. Real-time implementation of ReSuMe learning in Spiking Neural Network. ICAROB, Jan 2019, Beppu, Japan. ⟨hal-02484014⟩
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