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SUMMARY:Computational Model of Surprise in Neuroscience
DTSTART:20190625T140000
DTEND:20190625T160000
DTSTAMP:20260404T062806Z
UID:c009fc48098d722f3ab4d03a4a37f3d392a6f3dd788e4ec0528513b1
CATEGORIES:Conferences - Seminars
DESCRIPTION:Alireza Modirshanechi \nEDIC candidacy exam\nExam president: 
 Prof. Michael Gastpar\nThesis advisor: Prof. Wulfram Gerstner\nCo-examiner
 : Prof. Martin Jaggi\n\nAbstract\nShaping perception and making decisions 
 in the absence of complete knowledge requires estimation of unknown variab
 les. To survive despite its natural lack of knowledge\, our brain is evolv
 ed in a way to continuously estimate the environmental unobserved variable
 s and predict the consequences of its actions. A common hypothesis is that
  the brain makes a probabilistic model of the world\, and learns its param
 eters during time. Then it uses this probabilistic model for estimation\, 
 inference\, and prediction. In this proposal\, we first introduce a unifyi
 ng notation for such probabilistic models\, and then review three impactfu
 l articles which proposed such models for human perception and behavior.\n
 \nBackground papers\nMathys\, Christoph\, et al. A Bayesian foundation for
  individual learning under uncertainty. Frontiers in human neuroscience5 (
 2011): 39.\nFriston\, Karl\, et al.Active inference: a process theory. Neu
 ral computation 29.1 (2017): 1-49. (only first 19 pages\, until the end
  of section 2) \nMaheu\, Maxime\, Stanislas Dehaene\, and Florent Meyniel
 .Brain signatures of a multiscale process of sequence learning in humans. 
 Elife.
LOCATION:ELD 120 https://plan.epfl.ch/?room=ELD120
STATUS:CONFIRMED
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