Izhikevich E M
Center for Systems Science and Engineering, Arizona State University, Tempe, AZ 85287-7606, USA.
IEEE Trans Neural Netw. 1999;10(3):508-26. doi: 10.1109/72.761708.
We study pulse-coupled neural networks that satisfy only two assumptions: each isolated neuron fires periodically, and the neurons are weakly connected. Each such network can be transformed by a piece-wise continuous change of variables into a phase model, whose synchronization behavior and oscillatory associative properties are easier to analyze and understand. Using the phase model, we can predict whether a given pulse-coupled network has oscillatory associative memory, or what minimal adjustments should be made so that it can acquire memory. In the search for such minimal adjustments we obtain a large class of simple pulse-coupled neural networks that can memorize and reproduce synchronized temporal patterns the same way a Hopfield network does with static patterns. The learning occurs via modification of synaptic weights and/or synaptic transmission delays.
每个孤立神经元周期性地发放脉冲，且神经元之间连接较弱。每个这样的网络都可以通过变量的分段连续变换转化为一个相位模型，其同步行为和振荡关联特性更易于分析和理解。利用相位模型，我们可以预测给定的脉冲耦合网络是否具有振荡关联记忆，或者应进行哪些最小调整使其能够获得记忆。在寻找此类最小调整的过程中，我们得到了一大类简单的脉冲耦合神经网络，它们能够像霍普菲尔德网络处理静态模式一样记忆和再现同步的时间模式。学习通过修改突触权重和/或突触传递延迟来实现。
