Chen Hsin, Saighi Sylvain, Buhry Laure, Renaud Sylvie
Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan.
IEEE Trans Neural Netw. 2010 Sep;21(9):1511-7. doi: 10.1109/TNN.2010.2049028. Epub 2010 Jun 21.
Neuronal variability has been thought to play an important role in the brain. As the variability mainly comes from the uncertainty in biophysical mechanisms, stochastic neuron models have been proposed for studying how neurons compute with noise. However, most papers are limited to simulating stochastic neurons in a digital computer. The speed and the efficiency are thus limited especially when a large neuronal network is of concern. This brief explores the feasibility of simulating the stochastic behavior of biological neurons in a very large scale integrated (VLSI) system, which implements a programmable and configurable Hodgkin-Huxley model. By simply injecting noise to the VLSI neuron, various stochastic behaviors observed in biological neurons are reproduced realistically in VLSI. The noise-induced variability is further shown to enhance the signal modulation of a neuron. These results point toward the development of analog VLSI systems for exploring the stochastic behaviors of biological neuronal networks in large scale.
神经元变异性被认为在大脑中起着重要作用。由于变异性主要源于生物物理机制的不确定性，因此人们提出了随机神经元模型来研究神经元如何利用噪声进行计算。然而，大多数论文仅限于在数字计算机上模拟随机神经元。因此，速度和效率受到限制，尤其是当涉及大型神经元网络时。本简报探讨了在超大规模集成电路（VLSI）系统中模拟生物神经元随机行为的可行性，该系统实现了可编程和可配置的霍奇金-赫胥黎模型。通过简单地向VLSI神经元注入噪声，生物神经元中观察到的各种随机行为在VLSI中得到了真实再现。噪声引起的变异性进一步表明可增强神经元的信号调制。这些结果为开发用于大规模探索生物神经元网络随机行为的模拟VLSI系统指明了方向。
