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SUMMARY:Computational Neuroscience Journal Club - Sina Tootoonian ( CBL\, 
 Engineering\, U. Cambridge)
DTSTART:20140408T150000Z
DTEND:20140408T160000Z
UID:TALK51879@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:In this session\, Sina Tootoonian will present:\nSTDP Installs
  in Winner-Take-All Circuits an Online Approximation to Hidden Markov Mode
 l Learning\;\nby David Kappel\, Bernhard Nessler and Wolfgang Maass\;\nPLo
 S Comp. Biol. (2014)\n\nhttp://www.ploscompbiol.org/article/info%3Adoi%2F1
 0.1371%2Fjournal.pcbi.1003511\n\nABSTRACT: In order to cross a street with
 out being run over\, we need to be able to extract very fast hidden causes
  of dynamically changing multi-modal sensory stimuli\, and to predict thei
 r future evolution. We show here that a generic cortical microcircuit moti
 f\, pyramidal cells with lateral excitation and inhibition\, provides the 
 basis for this difficult but all-important information processing capabili
 ty. This capability emerges in the presence of noise automatically through
  effects of STDP on connections between pyramidal cells in Winner-Take-All
  circuits with lateral excitation. In fact\, one can show that these motif
 s endow cortical microcircuits with functional properties of a hidden Mark
 ov model\, a generic model for solving such tasks through probabilistic in
 ference. Whereas in engineering applications this model is adapted to spec
 ific tasks through offline learning\, we show here that a major portion of
  the functionality of hidden Markov models arises already from online appl
 ications of STDP\, without any supervision or rewards. We demonstrate the 
 emergent computing capabilities of the model through several computer simu
 lations. The full power of hidden Markov model learning can be attained th
 rough reward-gated STDP. This is due to the fact that these mechanisms ena
 ble a rejection sampling approximation to theoretically optimal learning. 
 We investigate the possible performance gain that can be achieved with thi
 s more accurate learning method for an artificial grammar task.
LOCATION:Cambridge University Engineering Department\, CBL Rm #438 (http:/
 /learning.eng.cam.ac.uk/Public/Directions)
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