Spatio-temporal Representations of Uncertainty in Spiking Neural Networks

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 encodingof such distributions remains however highly controversial. Here we present a novel circuit model for representing multidimensional real-valued distributions usinga spike based spatiotemporal code. Our model combines the computational advantagesof the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures. Furthermore, themodel highlights the challenges associated with interpreting neural activity in relation to behavioral uncertainty and points to alternative populationlevel approachesfor the experimental validation of distributed representations. Core brain computations, such as sensory perception, have been successfully characterized as probabilistic inference,whereby sensory stimuli are interpreted in terms of the objects or features that gave rise to them [1, 2].