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SUMMARY:Computational Neuroscience Journal Club - Alberto Bernacchia (CBL)
DTSTART:20160712T150000Z
DTEND:20160712T160000Z
UID:TALK66820@talks.cam.ac.uk
CONTACT:Daniel McNamee
DESCRIPTION:Alberto Bernacchia will cover:\n\n* Biologically plausible lea
 rning in recurrent neural networks for flexible decision tasks\n* Thomas M
 iconi\n* bioRxiv (2016)\n* http://biorxiv.org/content/early/2016/06/07/057
 729\n\nRecurrent neural networks operating in the near-chaotic regime exhi
 bit complex dynamics\, reminiscent of neural activity in higher cortical a
 reas. As a result\, these networks have been proposed as models of cortica
 l computation during cognitive tasks. However\, existing methods for train
 ing the connectivity of these networks are either biologically implausible
 \, and/or require an instantaneous\, real-time continuous error signal to 
 guide the learning process. The lack of plausible learning method may rest
 rict the applicability of recurrent neural networks as models of cortical 
 computation. Here we introduce a biologically plausible learning rule that
  can train such recurrent networks\, guided solely by delayed\, phasic rew
 ards at the end of each trial. We use this method to learn various tasks f
 rom the experimental literature\, showing that this learning rule can succ
 essfully implement flexible associations\, memory maintenance\, nonlinear 
 mixed selectivities\, and coordination among multiple outputs. The trained
  networks exhibit complex dynamics previously observed in animal cortex\, 
 such as dynamic encoding and maintenance of task features\, switching from
  stimulus-specific to response-specific representations\, and selective in
 tegration of relevant input streams. We conclude that recurrent neural net
 works can offer a plausible model of cortical dynamics during both learnin
 g and performance of flexible behavior.
LOCATION:Cambridge University Engineering Department\, CBL\, BE-438 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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