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It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions. The neural encoding of such distributions remains however highly controversial. Here we present a novel circuit model for representing multidimensional real-valued distributions using a spike based spatio-temporal code. Our model combines the computational advantages of the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures. Furthermore, the model highlights the challenges associated with interpreting neural activity in relation to behavioral uncertainty and points to alternative population-level approaches for the experimental validation of distributed representations.
Author Information
Cristina Savin (University of Cambridge)
Sophie Denève (GNT, Ecole Normale Superieure)
Related Events (a corresponding poster, oral, or spotlight)
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2014 Spotlight: Spatio-temporal Representations of Uncertainty in Spiking Neural Networks »
Tue. Dec 9th 08:30 -- 08:50 PM Room Level 2, room 210
More from the Same Authors
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2015 Poster: Enforcing balance allows local supervised learning in spiking recurrent networks »
Ralph Bourdoukan · Sophie Denève -
2013 Poster: Correlations strike back (again): the case of associative memory retrieval »
Cristina Savin · Peter Dayan · Mate Lengyel -
2013 Poster: Firing rate predictions in optimal balanced networks »
David G Barrett · Sophie Denève · Christian Machens -
2013 Oral: Correlations strike back (again): the case of associative memory retrieval »
Cristina Savin · Peter Dayan · Mate Lengyel -
2012 Poster: Learning optimal spike-based representations »
Ralph Bourdoukan · David Barrett · Christian Machens · Sophie Denève -
2011 Poster: Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories »
Cristina Savin · Peter Dayan · Mate Lengyel