Journal article
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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- Publisher copy:
- 10.1371/journal.pcbi.1012532
Authors
Funding
Bibliographic Details
- 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:
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1553-7358
- ISSN:
-
1553-734X
Item Description
- Language:
-
English
- Source identifiers:
-
2381837
- Deposit date:
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2024-10-30
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