Lookup NU author(s): Professor Alexander Thiele
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Population codes assume that neural systems represent sensory inputs through the firing rates of populations of differently tuned neurons. However, trial-by-trial variability and noise correlations are known to affect the information capacity of neural codes. Although recent studies have shown that stimulus presentation reduces both variability and rate correlations with respect to their spontaneous level, possibly improving the encoding accuracy, whether these second order statistics are tuned is unknown. If so, second-order statistics could themselves carry information, rather than being invariably detrimental. Here we show that rate variability and noise correlation vary systematically with stimulus direction in directionally selective middle temporal (MT) neurons, leading to characteristic tuning curves. We show that such tuning emerges in a stochastic recurrent network, for a set of connectivity parameters that overlaps with a single-state scenario and multi-stability. Information theoretic analysis shows that second-order statistics carry information that can improve the accuracy of the population code.
Author(s): Ponce-Alvarez A, Thiele A, Albright TD, Stoner GR, Deco G
Publication type: Article
Publication status: Published
Journal: Proceedings of the National Academy of Sciences
Print publication date: 01/08/2013
ISSN (print): 0027-8424
ISSN (electronic): 1091-6490
Publisher: National Academy of Sciences
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