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Learning probability distributions of sensory inputs with Monte Carlo predictive coding

Abstract:

It has been suggested that the brain employs probabilistic generative models to optimally interpret sensory information. This hypothesis has been formalised in distinct frameworks, focusing on explaining separate phenomena. On one hand, classic predictive coding theory proposed how the probabilistic models can be learned by networks of neurons employing local synaptic plasticity. On the other hand, neural sampling theories have demonstrated how stochastic dynamics enable neural circuits to re...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Role:
Author
ORCID:
0009-0004-6752-8296
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Institution:
University of Oxford
Role:
Author
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Funder identifier:
https://ror.org/03x94j517
Publisher:
Public Library of Science
Journal:
PLoS Computational Biology More from this journal
Volume:
20
Issue:
10
Article number:
e1012532
Publication date:
2024-10-30
Acceptance date:
2024-10-01
DOI:
EISSN:
1553-7358
ISSN:
1553-734X
Language:
English
Source identifiers:
2381837
Deposit date:
2024-10-30
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