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能够形成条件反射的可塑性神经元网络(学习的“膜”模型)

[Network of plastic neurons capable of forming conditioned reflexes ("membrane" model of learning)].

作者信息

Litvinov E G, Frolov A A

出版信息

Biofizika. 1978 Nov-Dec;23(6):1069-75.

PMID:719022
Abstract

Simple net neuronal model was suggested which was able to form the conditioning due to changes of the neuron excitability. The model was based on the following main concepts: (a) the conditioning formation should result in reduction of the firing threshold in the same neurons where the conditioning and reinforcement stimuli were converged, (b) neuron threshold may have only two possible states: initial and final ones, these were identical for all cells, the threshold may be changed only once from the initial value to the final one, (c) isomorphous relation may be introduced between some pair of arbitrary stimuli and some subset of the net neurons; any two pairs differing at least in one stimulus have unlike subsets of the convergent neurons. Stochastically organized neuronal net was used for analysis of the model. Considerable information capacity of the net gives the opportunity to consider that the conditioning formation is possible on the basis of the nervous cells. The efficienty of the model turn out to be comparable with the well known models where the conditioning formation was due to the modification of the synapses.

摘要

提出了一种简单的神经网络模型,该模型能够因神经元兴奋性的变化而形成条件反射。该模型基于以下主要概念:(a) 条件反射的形成应导致在条件刺激和强化刺激汇聚的相同神经元中,放电阈值降低;(b) 神经元阈值可能只有两种可能状态:初始状态和最终状态,所有细胞的这两种状态相同,阈值只能从初始值改变一次到最终值;(c) 可以在任意一对刺激与神经网络的某个子集之间引入同构关系;至少在一个刺激上不同的任意两对刺激,其汇聚神经元子集不同。使用随机组织的神经网络来分析该模型。网络相当大的信息容量使得有机会认为基于神经细胞形成条件反射是可能的。该模型的效率与已知的因突触修饰而形成条件反射的模型相当。

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