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一种用于多个记忆领域的神经网络模型。

A neural net model for multiple memory domains.

作者信息

Anninos P, Kokkinidis M

出版信息

J Theor Biol. 1984 Jul 7;109(1):95-110. doi: 10.1016/s0022-5193(84)80113-7.

Abstract

Previous studies with neural nets constructed of discrete populations of formal neurons have assumed that all neurons have the same probability of connection with any other neuron in the net. However, in this new study we incorporate the behavior of the neural systems in which the neural connections can be set up by means of chemical markers carried by the individual cells. With this new approach we studied the dynamics of isolated neural nets again as well as the dynamics of neural nets with sustained inputs. Results obtained with this approach show simple and multiple hysteresis phenomena. Such hysteresis loops may be considered to represent the basis for short-term memory.

摘要

先前对由离散形式神经元群体构建的神经网络的研究假定,网络中所有神经元与其他任何神经元建立连接的概率相同。然而,在这项新研究中,我们纳入了神经系统的行为,其中神经连接可以通过单个细胞携带的化学标记来建立。通过这种新方法,我们再次研究了孤立神经网络的动力学以及具有持续输入的神经网络的动力学。用这种方法获得的结果显示出简单和多重滞后现象。这种滞后环可被视为短期记忆的基础。

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