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The Redemption of Noise: Inference with Neural Populations
Rodrigo Echeveste1, Máté Lengyel2
1Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, UK.
Abstract:
In 2006, Ma et al. (Nat. Neurosci. 1006;9:1432-1438) presented an elegant theory for how populations of neurons might represent uncertainty to perform Bayesian inference. Critically, according to this theory, neural variability is no longer a nuisance, but rather a vital part of how the brain encodes probability distributions and performs computations with them.
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