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学习与记忆的互补分子模型。

Complementary molecular models of learning and memory.

作者信息

Conrad M

出版信息

Biosystems. 1976 Dec;8(3):119-38. doi: 10.1016/0303-2647(76)90015-0.

DOI:10.1016/0303-2647(76)90015-0
PMID:1016695
Abstract

The functional capabilities of the brain are formally characterizable interms of a finite system along with a memory space which it can manipulate. Two types of learning are possible: (1) modification-based learning, associated with alternate realizations of the finite system; (2) memory-based learning, associated with the assimilation, manipulation, and retrieval of memories. Constructive models which fulfill these conditions and which at the same time operate on the basis of molecular information processing principles have certain general features. We describe these features in terms of two interfaced submodels, the first for the finite system and the second for the memory space. The finite system may be realized by networks of neurons in which the specificity of enzyme molecules controls the nerve impulse. Such a realization is amenable to modification-based learning mediated by processes analogous to those of natural evolution and selective theories of antibody synthesis. The memory space is realizable by networks of neurons in which the conformation of dendritic receptor molecules controls the nerve impulse. In this case certain neurons firing in response to an external input undergo sensitization at the dendrites and in such a way that they are loadable and later callable by reference neurons, thereby allowing for reconstruction of manipulation of the firing pattern associated with this input. The overall construction makes a large number of biochemical, anatomical, physiological, and psychological predictions which are either testable or in good agreement with fact.

摘要

大脑的功能能力可以通过一个有限系统以及它能够操纵的记忆空间来进行形式化描述。存在两种可能的学习类型:(1)基于修改的学习,与有限系统的交替实现相关联;(2)基于记忆的学习,与记忆的同化、操纵和检索相关联。满足这些条件并且同时基于分子信息处理原理运行的建设性模型具有某些一般特征。我们根据两个相互关联的子模型来描述这些特征,第一个用于有限系统,第二个用于记忆空间。有限系统可以由神经元网络来实现,其中酶分子的特异性控制神经冲动。这样的实现方式适合通过类似于自然进化和抗体合成选择理论的过程介导的基于修改的学习。记忆空间可以由神经元网络来实现,其中树突状受体分子的构象控制神经冲动。在这种情况下,某些响应外部输入而放电的神经元在树突处经历敏化,并且以这样一种方式,它们可被参考神经元加载并随后被调用,从而允许重建与该输入相关的放电模式的操纵。整体结构做出了大量的生化、解剖学、生理学和心理学预测,这些预测要么是可测试的,要么与事实高度相符。

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引用本文的文献

1
Computational modeling of evolutionary learning processes in the brain.大脑进化学习过程的计算建模
Bull Math Biol. 1983;45(6):931-68. doi: 10.1007/BF02458823.
2
The enzymatic neuron as a reaction-diffusion network of cyclic nucleotides.作为环状核苷酸反应扩散网络的酶促神经元。
Bull Math Biol. 1984;46(5-6):765-83. doi: 10.1007/BF02462070.