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SUMMARY:Adaptation\, coding and Bayesian computations in single neurons an
 d neural populations - Alessandro Ticchi\, Imperial College London
DTSTART:20140522T093000Z
DTEND:20140522T103000Z
UID:TALK52731@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:Experimental evidence at the behavioral level shows that the b
 rain is able to make Bayes-optimal decisions\, yet at the circuit level li
 ttle is known about how brains may implement Bayesian learning and inferen
 ce. In this talk\, we will investigate how spiking neurons can implement B
 ayesian computations both on the level of the single neuron and of a neura
 l population. On the level of single neuron\, we will use a Bayesian appro
 ach\nto derive a normative model of neural adaptation to the instantaneous
  input statistics. On the population level\, we will investigate how neura
 l variability can be exploited to implement inference by sampling and\, in
  particular\, Markov Chain Monte Carlo algorithms.
LOCATION:Cambridge University Engineering Department\, CBL Rm #438 (http:/
 /learning.eng.cam.ac.uk/Public/Directions)
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