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首页> 外文期刊>IEEE Transactions on Neural Networks >A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity
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A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity

机译:低功率尖峰神经元和双稳态突触的VLSI阵列,具有依赖于尖峰时序的可塑性

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摘要

We present a mixed-mode analog/digital VLSI device comprising an array of leaky integrate-and-fire (I&F) neurons, adaptive synapses with spike-timing dependent plasticity, and an asynchronous event based communication infrastructure that allows the user to (re)configure networks of spiking neurons with arbitrary topologies. The asynchronous communication protocol used by the silicon neurons to transmit spikes (events) off-chip and the silicon synapses to receive spikes from the outside is based on the "address-event representation" (AER). We describe the analog circuits designed to implement the silicon neurons and synapses and present experimental data showing the neuron's response properties and the synapses characteristics, in response to AER input spike trains. Our results indicate that these circuits can be used in massively parallel VLSI networks of I&F neurons to simulate real-time complex spike-based learning algorithms.
机译:我们提出了一种混合模式的模拟/数字VLSI设备,该设备包括一组泄漏的集成发射(I&F)神经元,具有依赖于尖峰时序的可塑性的自适应突触以及一个基于异步事件的通信基础结构,该结构允许用户(重新)用任意拓扑配置尖峰神经元网络。硅神经元用来在芯片外发送尖峰(事件)而硅突触从外部接收尖峰的异步通信协议是基于“地址事件表示”(AER)的。我们描述了设计用于实现硅神经元和突触的模拟电路,并给出了实验数据,这些数据显示了神经元的响应特性和突触特性,以响应AER输入尖峰序列。我们的结果表明,这些电路可用于I&F神经元的大规模并行VLSI网络中,以模拟基于实时复杂峰值的学习算法。

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