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为了模拟人与动物感知信息的真实环境,以脉动神经元节点组成神经元网络,研究在随机刺激和混沌刺激等极端条件下的记忆模式存储与时间分割问题.研究表明:网络对于若干种模式的叠加输入,能够以一部分神经元同步发放的形式在时间域上分割出每一模式.如果输入模式是缺损的,系统能够把它们恢复到原型,即具有联想记忆功能.通过调节耦合强度和噪声强度等参数使得网络在中等强度噪声达到最优的时间分割,与广泛讨论的随机共振现象一致.
In order to simulate the real environment of human and animal perception information, the neuron network is composed of pulsatile neuron nodes, and the memory mode storage and time segmentation under extreme conditions such as stochastic stimulation and chaotic stimulation are studied. The results show that: Overlay input, can be part of the neurons issued in the form of synchronization in the time domain segmentation of each mode.If the input mode is missing, the system can restore them to the prototype, that is, with associative memory function.By adjusting the coupling strength and noise intensity Equal parameters allow the network to be optimally time-sliced at moderate intensity noise, consistent with the widely discussed stochastic resonance phenomenon.