Recurrent dynamics in the cerebral cortex: Integration of sensory evidence with stored knowledge.
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| Title: | Recurrent dynamics in the cerebral cortex: Integration of sensory evidence with stored knowledge. |
|---|---|
| Authors: | Singer, Wolf1,2,3 wolf.singer@brain.mpg.de |
| Source: | Proceedings of the National Academy of Sciences of the United States of America. 8/17/2021, Vol. 118 Issue 33, p1-12. 12p. |
| Subjects: | Cerebral cortex, Sensorimotor integration, Deep learning, Feature extraction, Evidence, Predictive tests, Patient discharge instructions |
| Abstract: | Current concepts of sensory processing in the cerebral cortex emphasize serial extraction and recombination of features in hierarchically structured feed-forward networks in order to capture the relations among the components of perceptual objects. These concepts are implemented in convolutional deep learning networks and have been validated by the astounding similarities between the functional properties of artificial systems and their natural counterparts. However, cortical architectures also display an abundance of recurrent coupling within and between the layers of the processing hierarchy. This massive recurrence gives rise to highly complex dynamics whose putative function is poorly understood. Here a concept is proposed that assigns specific functions to the dynamics of cortical networks and combines, in a unifying approach, the respective advantages of recurrent and feed-forward processing. It is proposed that the priors about regularities of the world are stored in the weight distributions of feed-forward and recurrent connections and that the high-dimensional, dynamic space provided by recurrent interactions is exploited for computations. These comprise the ultrafast matching of sensory evidence with the priors covertly represented in the correlation structure of spontaneous activity and the context-dependent grouping of feature constellations characterizing natural objects. The concept posits that information is encoded not only in the discharge frequency of neurons but also in the precise timing relations among the discharges. Results of experiments designed to test the predictions derived from this concept support the hypothesis that cerebral cortex exploits the high-dimensional recurrent dynamics for computations serving predictive coding. [ABSTRACT FROM AUTHOR] |
| Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 151993449 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1073/pnas.2101043118 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Cerebral cortex Type: general – SubjectFull: Sensorimotor integration Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Evidence Type: general – SubjectFull: Predictive tests Type: general – SubjectFull: Patient discharge instructions Type: general Titles: – TitleFull: Recurrent dynamics in the cerebral cortex: Integration of sensory evidence with stored knowledge. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singer, Wolf IsPartOfRelationships: – BibEntity: Dates: – D: 17 M: 08 Text: 8/17/2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 00278424 Numbering: – Type: volume Value: 118 – Type: issue Value: 33 Titles: – TitleFull: Proceedings of the National Academy of Sciences of the United States of America Type: main |
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