What is it about?
A prevailing idea in neuroscience suggests that information is primarily encoded in the mean firing rate of neural activity. However, the brain is abundant with noise, which has long been theorized to be a useful computational resource . We demonstrate that a neural network can be trained to extract perceptual information encoded entirely in the correlation structure of input noise and transfer it to the mean firing rate of downstream neurons through its intrinsic signal-noise coupling.
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Why is it important?
These results suggest that correlated noise may play a more central role in the brain than previously thought.
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This page is a summary of: Learning to integrate parts for whole through correlated neural variability, PLoS Computational Biology, September 2024, PLOS,
DOI: 10.1371/journal.pcbi.1012401.
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