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Brain Computation as Hierarchical Abstraction$
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Dana H. Ballard

Print publication date: 2015

Print ISBN-13: 9780262028615

Published to MIT Press Scholarship Online: September 2015

DOI: 10.7551/mitpress/9780262028615.001.0001

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Cortical Memory

Cortical Memory

Chapter:
(p.127) 4 Cortical Memory
Source:
Brain Computation as Hierarchical Abstraction
Author(s):

Dana H. Ballard

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262028615.003.0004

The primary way the brain responds quickly is to store previous experiences in a vast tabular format that allows responses to be accessed quickly. The exact format of the memory is probably a composite of many different constraints, the forms of which are described. The primary anatomical organization of cortex is into hierarchies of predominantly two-dimensional `maps,’ of key features. Such features are computed at the upper layers of each map, the lower layers handing input and output signals. The separate feature characteristics of a map initially led researchers to think of its properties as more or less independent from other maps, but recent research is revealing that the maps’ feature sets are far more integrated and interdependent. Bayesian network models have provided an elegant computational framework that captures this interdependence.

Keywords:   Table-lookup, Maps, What-where Dichotomy, Deep Learning, Bayes Networks

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