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Encoding and Decoding Spikes for Dynamic Stimuli

rama{at}cs.toronto.edu Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4

Quentin J. M. Huys

qhuys{at}gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, U.K.

Peter Dayan

dayan{at}gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, U.K.

Richard S. Zemel

zemel{at}cs.toronto.edu Department of Computer Science, University of Toronto, Ontario, Canada M5S 3G4

Naturally occurring sensory stimuli are dynamic. In this letter, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner by a simple local learning rule.