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Spatio-temporal Representations of Uncertainty in Spiking Neural Networks
Cristina Savin · Sophie Denève

Tue Dec 09 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

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)

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