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Neural Mechanisms for Information Compression by Multiple Alignment, Unification and Search

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Document pages: 43 pages

Abstract: This article describes how an abstract framework for perception and cognition may be realised in terms of neural mechanisms and neural processing.This framework — called information compression by multiple alignment, unification and search (ICMAUS) — has been developed in previous research as a generalized model of any system for processing information, either natural orartificial. It has a range of applications including the analysis and production of natural language, unsupervised inductive learning, recognition of objects and patterns, probabilistic reasoning, and others. The proposals in this article may be seen as an extension and development ofHebb’s (1949) concept of a ‘cell assembly’.The article describes how the concept of ‘pattern’ in the ICMAUS framework may be mapped onto a version of the cellassembly concept and the way in which neural mechanisms may achieve the effect of ‘multiple alignment’ in the ICMAUS framework.By contrast with the Hebbian concept of a cell assembly, it is proposed here that any one neuron can belong in one assembly and only one assembly. A key feature of present proposals, which is not part of the Hebbian concept, is that any cell assembly may contain ‘references’ or ‘codes’ that serve to identify one or more other cell assemblies. This mechanism allows information to be stored in a compressed form, it provides a robust mechanism by which assemblies may be connected to form hierarchies and other kinds of structure, it means that assemblies can expressabstract concepts, and it provides solutions to some of the other problems associated with cell assemblies.Drawing on insights derived from the ICMAUS framework, the article also describes how learning may be achieved with neural mechanisms. This concept of learning is significantly different from the Hebbian concept and appears to provide a better account of what we know about human learning.

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