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

机译:使用OxRAM突触的嵌入式Spiking神经网络实时解码大脑活动

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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.
机译:本文提出了一种基于尖峰神经网络的脑信号解码创新方法。突触由BEOL兼容的氧化物电阻RAM(OxRAM)器件实现,可提供低编程电压(<; 2.5V)和电流(〜30μA)。依赖于尖峰时间的可塑性使网络能够对测量的生物信号进行自主的在线尖峰分类。突触功耗极低,仅为10nW,识别率约为90%,实时功能在未来的医疗保健应用中具有很高的潜力。

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