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SUMMARY:LCN Seminar: Learning precisely timed spikes
DTSTART:20140918T133000
DTSTAMP:20260508T132349Z
UID:6f1dfc6af93a14735965821432822b73f7854a53b74ffb3e730d72d8
CATEGORIES:Conferences - Seminars
DESCRIPTION:Raoul-Martin MEMMESHEIMER\; Faculty of  Science\, Neuroinform
 atics\, Radboud University Nijmegen\, Netherlands\nExperiments have reveal
 ed precisely timed patterns of spikes in several neuronal systems\, raisin
 g the possibility that these temporal signals are used by the brain to enc
 ode and transmit sensory information. It is thus important to understand t
 he capability of neural circuits to learn to produce stimulus specific tem
 porally precise spikes. Learning to spike at given times is challenging si
 nce the spike threshold and the ensuing reset induce a strongly nonlinear 
 dependence of the voltage on the value of the synaptic weights. I will pre
 sent two learning algorithms\, High Threshold Projection and Finite Precis
 ion Learning that accomplish the task. High Threshold Projection Learning 
 converges in finite time to exactly fit the desired spike pattern if it is
  realizable. First Error Learning is a more biologically plausible rule\, 
 which converges to solutions with finite precision. The algorithms are emp
 loyed to establish the capacity of a leaky integrate-and-fire neuron using
  a temporal code. We use theoretical considerations to derive the scaling 
 of the capacity and to predict its numerical value in the low output rate 
 regime. To show that our algorithms are able to learn behaviorally meaning
 ful tasks from real neuronal data\, we apply them to neuronal recordings o
 f song birds. In addition\, this proposes a novel way to estimate the info
 rmation content carried by spike patterns which is accessible to neuronal 
 architectures. Further\, we show that our approach may provide a simple me
 thod to reconstruct anatomical connectivity from spike trains. Finally\, w
 e generalize our learning algorithms to perform learning of precise spike 
 timing patterns in arbitrary neuronal architecture that includes feedback 
 and recurrent connections.\nReference: Memmesheimer*\, R-M\, Rubin* R\, Ö
 lveczky BP\, Sompolinsky H\, "Learning Precisely Timed Spikes"\,Neuron 82:
 925-938 (2014).
LOCATION:AAC132 http://plan.epfl.ch/?room=AAC132
STATUS:CONFIRMED
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