---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Cristina
      foaf_name: Savin, Cristina
      foaf_surname: Savin
      foaf_workInfoHomepage: http://www.librecat.org/personId=3933349E-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Sophie
      foaf_name: Denève, Sophie
      foaf_surname: Denève
  bibo_issue: January
  bibo_volume: 27
  dct_date: 2014^xs_gYear
  dct_language: eng
  dct_publisher: Neural Information Processing Systems Foundation@
  dct_title: Spatio-temporal representations of uncertainty in spiking neural networks@
...