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PRODID:UW-Madison-Physics-Events
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SEQUENCE:0
UID:UW-Physics-Event-1071
DTSTART:20080325T170500Z
DURATION:PT1H0M0S
DTSTAMP:20260423T040957Z
LAST-MODIFIED:19700101T060000Z
LOCATION:4274 Chamberlin Hall
SUMMARY:Computational constraints in Hebbian learning: relationship to
  epileptogenesis and other neurological disorders\, Chaos & Complex Sy
 stems Seminar\, David Hsu\, Neurology
DESCRIPTION:The ability of the brain to absorb and incorporate within 
 itself new ideas implies that it is a metastable system. It must conti
 nually change and yet not devolve into randomness. How does it do this
 \, and what are the consequences? Brain activity can be represented in
  terms of a large collection of excitable bodies that possess both spo
 ntaneous activity and that can stimulate other bodies to become excite
 d. I discuss a simple computer model of such a system and study (1) wh
 at is necessary for such a system to learn\, and (2) what is necessary
  for it to maintain itself in a state capable of further learning. It 
 turns out that the highest performing brain models that are able to ma
 intain stable learning also show self-organized criticality. Unfortuna
 tely\, the homeostatic constraints that maintain optimal brain perform
 ance also predispose the brain toward neurological disease. The relati
 onship to epilepsy is presented\, and an approach to its cure is propo
 sed.
URL:https://www.physics.wisc.edu/events/?id=1071
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