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Author Freudenburg, Z.V. ♦ Ghosh, B.K. ♦ Ulinski, P.S.
Sponsorship IEEE Engineering in Medicine and Biology Society
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1964
Language English
Subject Domain (in DDC) Technology ♦ Medicine & health ♦ Engineering & allied operations
Subject Keyword Brain modeling ♦ Neurons ♦ Large-scale systems ♦ In vivo ♦ Information analysis ♦ Anatomy ♦ Adaptation model ♦ Voltage ♦ Reflection ♦ Spatiotemporal phenomena ♦ turtle visual cortex ♦ Hebbian learning ♦ large-scale cortex model ♦ synaptic adaptation
Abstract Both single and repeated visual stimuli produce waves of activity in the visual cortex of freshwater turtles. Large-scale, biophysically realistic models of the visual cortex capture the basic features of the waves produced by single stimuli. However, these models do not respond to repetitive stimuli due to the presence of a long-lasting hyperpolarization that follows the initial wave. This paper modifies the large-scale model so that it responds to repetitive stimuli by incorporating Hebbian and anti-Hebbian learning rules in synapses in the model. The resulting adaptive model responds to repetitive stimuli with repetitive waves. However, repeated presentation of a stimulus to a restricted region of visual space produces a habituation in the model in the same way it does in the real cortex.
Description Author affiliation :: Dept. of Organismal Biol. & Anatomy, Univ. of Chicago, Chicago, IL
Author affiliation :: Dept. of Math. & Stat., Texas Tech Univ., Lubbock, TX
Author affiliation :: Dept. of Comput. Sci. & Eng., Washington Univ., St. Louis, MO
ISSN 00189294
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2009-05-01
Publisher Place U.S.A.
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Volume Number 56
Issue Number 5
Size (in Bytes) 740.15 kB
Page Count 10
Starting Page 1277
Ending Page 1286


Source: IEEE Xplore Digital Library