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Modelling Memory and Learning Consistently from Psychology to Physiology

  • L. Andrew Coward
Chapter
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS)

Abstract

Natural selection pressures have resulted in the physical resources of the brain being organized into modules that perform different general types of information processes. Each module is made up of submodules performing different information processes of the general type, and each submodule is made up of yet more detailed modules. At the highest level, modules correspond with major anatomical structures like the cortex, hippocampus, basal ganglia, cerebellum etc. In the cortex, for example, the more detailed modules include areas, columns, neurons, and a series of neuron substructures down to molecules. Any one memory or learning phenomenon requires many information processes performed by many different anatomical structures. However, the modular structure makes it possible to describe a memory phenomenon at a high (psychological) level in terms of the information processes performed by the major anatomical structures. The same phenomenon can be described at each level in a hierarchy of more detailed descriptions, in terms of the information processes performed by anatomical substructures. At higher levels, descriptions are approximate but can be mapped to more detailed, more precise descriptions as required down to neuron levels and below. The total information content of a high level description is small enough that it can be fully understood. Small parts of a high level phenomenon, when described at a more detailed level, also have a small enough information content to be understood. The information processes and resultant hierarchy of descriptions therefore make it possible to understand cognitive phenomena like episodic, semantic or working memory in terms of neuron processes via a series of intermediate levels of description.

Keywords

Receptive Field Episodic Memory Information Model Declarative Memory Indirect Activation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  1. 1.College of Engineering and Computer ScienceAustralian National UniversityCanberraAustralia

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