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Design of Artificial Spiking Neuron with SiO2 Memristive Synapse to Demonstrate Neuron-Level Spike Timing Dependent Plasticity

机译:用SiO 2 膜突触的人工尖峰神经元设计展示神经元级尖峰定时依赖性可塑性

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In this paper, we report on the Design-Technology Co-optimization (DTCO) of an artificial spiking neuron utilizing a leaky-integrated-and-fire (LIF) circuit with a SiO2 based sub-1.2V analog resistive random-access memory (ReRAM). The memristor integrated artificial neuron is capable of spike-timing-dependent plasticity (STDP), necessary for the implementation of Spiking Neural Networks (SNNs). In addition, for the first time, we designed and demonstrated a STDP circuit in conjunction with the SiO2-based ReRAM that is capable of generating forward- and back-propagation signals. This allows for self-modulation of the synaptic weights through the Long-term potentiation (LTP) and Long-term depression (LTD) effect.
机译:在本文中,我们报告了利用具有SIO的漏液和火(LIF)电路的人工尖峰神经元的设计 - 技术协同优化(DTCO) 2 基于Sub-1.2V模拟电阻随机存取存储器(RERAM)。忆耳集成的人工神经元能够实现尖刺神经网络(SNNS)所必需的峰值定时依赖性塑性(STDP)。此外,我们首次设计并与SIO一起设计和演示了STDP电路 2 基于RERAM,其能够生成正向和背部传播信号。这允许通过长期增强(LTP)和长期凹陷(LTD)效应来自我调制突触权重。

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