BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Statistically optimal inference and learning: from behavior to neu
 ral representations - Professor József Fiser\, Brandeis University
DTSTART:20090317T130000Z
DTEND:20090317T140000Z
UID:TALK17217@talks.cam.ac.uk
CONTACT:John Mollon
DESCRIPTION:In recent years\, a growing number of human psychophysical stu
 dies and physiological experiments with behaving animals supported the not
 ion that the brain encodes both the value and the uncertainty of the stimu
 lus during perception.  As a result\, cortical sensory processing has been
  increasingly viewed in the framework of probabilistic inference.  However
 \, there have been much fewer attempts to extend this probabilistic notion
  to learning even though perception and learning are clearly two fundament
 al aspects of cortical processing.  In addition\, present theoretical prob
 abilistic models of cortical inference cannot handle easily important aspe
 cts of cortical activity such as trial-to-trial variability\, and structur
 ed spontaneous activity.  I will present a framework and some results that
  address these issues and potentially link learning behavior to probabilis
 tic neural activity in the cortex.  First\, I provide evidence that human 
 learning of chunks\, new visual feature combinations is close to statistic
 ally optimal.  Next\, I present a generative framework of cortical process
 ing and learning based on the assumption that neural activity represents s
 amples of the posterior probability distribution defined by the uncertaint
 y of the stimulus.  This framework can explain trial-to-trial variability\
 , gives a functional role to spontaneous activity in the cortex\, and prov
 ides some clearly testable physiological predictions.  In the final part o
 f my talk\, I will demonstrate how we confirmed one of these predictions b
 y showing that with accumulating visual experience\, the distribution of s
 pontaneous activity in the cortex approximates the distribution of evoked 
 activity averaged over natural stimuli.
LOCATION:Kenneth Craik Room\, Craik-Marshall Building\, Downing Site
END:VEVENT
END:VCALENDAR