Morita M
Institute of Information Sciences and Electronics, University of Tsukuba, Ibaraki, Japan.
Brain Res Cogn Brain Res. 1996 Dec;5(1-2):137-46. doi: 10.1016/s0926-6410(96)00050-x.
The brain stores various kinds of temporal sequences as long-term memories, such as motor sequences, episodes, and melodies. The present study aims at clarifying the general principle underlying such memories. For this purpose, the memory mechanism of sequential patterns is examined from the viewpoint of computational theory and neural network modeling, and a neural network model of sequential pattern memory based on a simple and reasonable principle is presented. Specifically, spatio-temporal patterns varying gradually with time are stably stored in a network consisting of pairs of excitatory and inhibitory cells with recurrent connections; such a pair can achieve non-monotonic input-output characteristics which are essential for smooth sequential recall. Storage is performed using a simple learning algorithm which is based on the covariance rule and requires only that the sequence be input several times and retrieval is highly tolerant to noise. It is thought that a similar principle is used in cerebral memory systems, and the relevance of this model to the brain is discussed. Also, possible roles of hippocampus and basal ganglia in memorizing sequences are suggested.
大脑将各种时间序列作为长期记忆存储起来，比如运动序列、事件和旋律。本研究旨在阐明此类记忆背后的一般原理。为此，从计算理论和神经网络建模的角度研究序列模式的记忆机制，并提出一种基于简单合理原理的序列模式记忆神经网络模型。具体而言，随时间逐渐变化的时空模式稳定地存储在一个由具有循环连接的兴奋性和抑制性细胞对组成的网络中；这样的细胞对可以实现非单调的输入输出特性，这对于流畅的序列回忆至关重要。存储使用一种基于协方差规则的简单学习算法来执行，该算法只要求序列被输入几次，并且检索对噪声具有高度耐受性。人们认为大脑记忆系统中使用了类似的原理，并讨论了该模型与大脑的相关性。此外，还提出了海马体和基底神经节在记忆序列中的可能作用。
