Science and Technology Research Institute, University of Hertfordshire, College Lane, Hatfield, Hertfordshire, United Kingdom.
Neural Netw. 2009 Oct;22(8):1105-12. doi: 10.1016/j.neunet.2009.07.022. Epub 2009 Jul 21.
Many network models in computational neuroscience rise to the challenge of explaining behavioural phenomena ranging from microseconds to tens of seconds using components operating mostly on a time-scale of milliseconds. These models have in common that the underlying system has a memory, which implies that its output depends on its past input history. In this review we compare how such memory traces or delayed responses may be implemented in different brain areas supporting a diversity of functions.
计算神经科学中的许多网络模型都能应对挑战，它们使用主要在毫秒时间尺度上运行的组件，解释从微秒到数十秒的行为现象。这些模型的一个共同点是，它们的底层系统具有记忆，这意味着其输出取决于其过去的输入历史。在这篇综述中，我们比较了不同脑区中如何实现这种记忆痕迹或延迟响应，以支持多样化的功能。
