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Real-time decoding of brain activity by embedded Spiking Neural Networks using OxRAM synapses

Abstract : An innovative approach for decoding of brain signals based on Spiking Neural Networks is presented in this paper. Synapses are implemented by BEOL compatible oxide resistive RAM (OxRAM) devices providing low programming voltages (<2.5V) and currents (∼30μA). Spike-timing-dependent plasticity enables the network for autonomous online spike sorting of measured biological signals. Ultra-low synaptic power consumption in the range of 10nW, recognition rates around 90% and real-time functionality bear high potential for future healthcare applications.
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https://hal-cea.archives-ouvertes.fr/cea-01817861
Contributor : Léna Le Roy <>
Submitted on : Monday, June 18, 2018 - 2:28:53 PM
Last modification on : Friday, July 10, 2020 - 7:58:55 AM

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T. Werner, D. Garbin, E. Vianello, O. Bichler, D. Cattaert, et al.. Real-time decoding of brain activity by embedded Spiking Neural Networks using OxRAM synapses. 2016 IEEE International Symposium on Circuits and Systems (ISCAS), May 2016, Montreal, Canada. pp.2318-2321, ⟨10.1109/ISCAS.2016.7539048⟩. ⟨cea-01817861⟩

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