MCPC learns efficient generative models of sensory inputs.

<p><b>a</b>, Distributions learned by MCPC and PC in the linear model given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012532#pcbi.1012532.g001" target="_blank">Fig 1a</a> after 375 parameter updates. <b>b<...

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Bibliographic Details
Main Author:	Gaspard Oliviers (19976466) (author)
Other Authors:	Rafal Bogacz (307953) (author), Alexander Meulemans (19976469) (author)
Published:	2024
Subjects:	Neuroscience Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified explaining separate phenomena learning probability distributions generate likely inputs neural sampling theories neural sampling results introduced neural sampling combining predictive coding neural sampling predictive coding posterior distributions neural network neural dynamics neural data neural activity static inputs sensory inputs xlink "> variational filtering probabilistic models perceptual tasks latent states experimental observations brought together
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MCPC learns efficient generative models of sensory inputs.

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