Indiveri Giacomo, Chicca Elisabetta, Douglas Rodney
Institute of Neuroinformatics, Swiss Federal Institute of Technology, Zurich CH-8057, Switzerland.
IEEE Trans Neural Netw. 2006 Jan;17(1):211-21. doi: 10.1109/TNN.2005.860850.
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.
我们展示了一种混合模式的模拟/数字超大规模集成电路(VLSI)器件,它包括一个泄漏积分发放(I&F)神经元阵列、具有基于脉冲时间依赖可塑性的自适应突触,以及一个基于异步事件的通信基础设施,该基础设施允许用户(重新)配置具有任意拓扑结构的脉冲神经元网络。硅神经元用于将脉冲(事件)传输到芯片外以及硅突触用于从外部接收脉冲所使用的异步通信协议基于“地址事件表示”(AER)。我们描述了为实现硅神经元和突触而设计的模拟电路,并给出了实验数据,展示了神经元的响应特性和突触特征,以响应AER输入脉冲序列。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,以模拟基于脉冲的实时复杂学习算法。