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A Computational Model of the Human Visual Cortex

Published

Author(s)

James S. Albus

Abstract

The brain is first and foremost a control system that is capable of building an internal representation of the external world, and using this representation to make decisions, set goals and priorities, formulate plans, and control behavior with intent to achieve its goals.   The computational model proposed here assumes that this internal representation resides in arrays of cortical columns.  More specifically, it models each cortical hypercolumn together with its underlying thalamic nuclei as a Fundamental Computational Unit (FCU) consisting of a frame-like data structure (containing attributes and pointers) plus the computational processes and mechanisms required to maintain it. In sensory-processing areas of the brain, FCUs enable segmentation, grouping, and classification.  Pointers stored in FCU frames link pixels and signals to objects and events in situations and episodes that are overlaid with meaning and emotional values.  In behavior-generating areas of the brain, FCUs make decisions, set goals and priorities, generate plans, and control behavior.  Pointers are used to define rules, grammars, procedures, plans, and behaviors.   It is suggested that it may be possible to reverse engineer the human brain at the FCU level of fidelity using next-generation massively parallel computer hardware and software.
Proceedings Title
Proceedings of the SPIE Defense and Security 2008
Conference Dates
March 18-20, 2008
Conference Location
Orlando, FL
Conference Title
SPIE Defense and Security 2008

Keywords

computational modeling, human cortex, brain modeling, reverse engineering the brain, image processing, perception, segmentation, knowledge representation

Citation

Albus, J. (2008), A Computational Model of the Human Visual Cortex, Proceedings of the SPIE Defense and Security 2008, Orlando, FL, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824644 (Accessed December 2, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created March 20, 2008, Updated February 17, 2017