延时FitzHugh-Nagumo神经网络的时空编码

为更接近真实神经系统，考虑具有延时的FitzHugh—Nagumo神经元组成的神经元网络，发现其并不是单个发放而是簇状发放，有利于模式的时间分割．给定一个输入模式，它是几种模式的叠加，网络能够以一部分神经元同步簇放电的形式一个接一个地分割出每一种模式．如果输入的模式是缺损的，系统能够按记忆的模式恢复，即网络具有联想记忆功能．

Spatiotemporal Coding in FitzHugh-Nagumo Neural Network with Time Delay

For crossing to the real condition of a neural system, considering the network composed of Fitzhugh-Nagumo neurons with time delay, it was found that not a single firing but a burst of firing fall into synchronization, and they pop out alternatively, this benefits the segmentation of patterns in time. For an input pattern which is an overlapped superposition of several stored patterns, it is shown that the proposed neuronal network model is capable of segmenting out each pattern one after another in the time domain as synchronous bursting of a subgroup of neurons, and if a corrupted input pattern is presented, the network is shown to be able to retrieve one learnt before, that is it has the function of associative memory.