Nakada Kazuki, Asai Tetsuya, Hayashi Hatsuo
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Kitakyushu, Fukuoka 808-0196, Japan.
Int J Neural Syst. 2006 Dec;16(6):445-56. doi: 10.1142/S0129065706000846.
We propose an analog integrated circuit that implements a resonate-and-fire neuron (RFN) model based on the Lotka-Volterra (LV) system. The RFN model is a spiking neuron model that has second-order membrane dynamics, and thus exhibits fast damped subthreshold oscillation, resulting in the coincidence detection, frequency preference, and post-inhibitory rebound. The RFN circuit has been derived from the LV system to mimic such dynamical behavior of the RFN model. Through circuit simulations, we demonstrate that the RFN circuit can act as a coincidence detector and a band-pass filter at circuit level even in the presence of additive white noise and background random activity. These results show that our circuit is expected to be useful for very large-scale integration (VLSI) implementation of functional spiking neural networks.
我们提出了一种模拟集成电路，该电路基于洛特卡-沃尔泰拉（LV）系统实现了一个谐振并发放神经元（RFN）模型。RFN模型是一种具有二阶膜动力学的脉冲发放神经元模型，因此呈现出快速阻尼的阈下振荡，从而实现了重合检测、频率偏好和抑制后反弹。RFN电路是从LV系统推导而来，以模拟RFN模型的这种动力学行为。通过电路仿真，我们证明即使存在加性白噪声和背景随机活动，RFN电路在电路层面也能充当重合检测器和带通滤波器。这些结果表明，我们的电路有望用于功能性脉冲发放神经网络的超大规模集成（VLSI）实现。
