Watanabe M, Aihara K, Kondo S
Department of Quantum Engineering and Systems Science, School of Engineering, University of Tokyo, Japan.
Biol Cybern. 1998 Feb;78(2):87-93. doi: 10.1007/s004220050416.
A neural network model capable of altering its pattern classifying properties by program input is proposed. Here the "program input" is another source of input besides the pattern input. Unlike most neural network models, this model runs as a deterministic point process of spikes in continuous time; connections among neurons have finite delays, which are set randomly according to a normal distribution. Furthermore, this model utilizes functional connectivity which is dynamic connectivity among neurons peculiar to temporal-coding neural networks with short neuronal decay time constants. Computer simulation of the proposed network has been performed, and the results are considered in light of experimental results shown recently for correlated firings of neurons.
提出了一种能够通过程序输入改变其模式分类属性的神经网络模型。这里的“程序输入”是除模式输入之外的另一种输入源。与大多数神经网络模型不同，该模型作为连续时间内尖峰的确定性点过程运行；神经元之间的连接具有有限延迟，这些延迟根据正态分布随机设置。此外，该模型利用功能连接性，这是具有短神经元衰减时间常数的时间编码神经网络特有的神经元之间的动态连接性。已对所提出的网络进行了计算机模拟，并根据最近显示的神经元相关放电的实验结果对结果进行了考量。
