Predictive Coding with Neural Transmission Delays: A Real-Time Temporal Alignment Hypothesis
Hierarchical predictive coding is an influential model of cortical organization, in which sequential hierarchical levels are connected by backward connections carrying predictions, as well as forward connections carrying prediction errors. To date, however, predictive coding models have largely negl...
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| Vydáno v: | eNeuro Ročník 6; číslo 2; s. ENEURO.0412-18.2019 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Society for Neuroscience
01.03.2019
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| ISSN: | 2373-2822, 2373-2822 |
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| Abstract | Hierarchical predictive coding is an influential model of cortical organization, in which sequential hierarchical levels are connected by backward connections carrying predictions, as well as forward connections carrying prediction errors. To date, however, predictive coding models have largely neglected to take into account that neural transmission itself takes time. For a time-varying stimulus, such as a moving object, this means that backward predictions become misaligned with new sensory input. We present an extended model implementing both forward and backward extrapolation mechanisms that realigns backward predictions to minimize prediction error. This realignment has the consequence that neural representations across all hierarchical levels become aligned in real time. Using visual motion as an example, we show that the model is neurally plausible, that it is consistent with evidence of extrapolation mechanisms throughout the visual hierarchy, that it predicts several known motion–position illusions in human observers, and that it provides a solution to the temporal binding problem. |
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| AbstractList | Hierarchical predictive coding is an influential model of cortical organization, in which sequential hierarchical levels are connected by backward connections carrying predictions, as well as forward connections carrying prediction errors. To date, however, predictive coding models have largely neglected to take into account that neural transmission itself takes time. For a time-varying stimulus, such as a moving object, this means that backward predictions become misaligned with new sensory input. We present an extended model implementing both forward and backward extrapolation mechanisms that realigns backward predictions to minimize prediction error. This realignment has the consequence that neural representations across all hierarchical levels become aligned in real time. Using visual motion as an example, we show that the model is neurally plausible, that it is consistent with evidence of extrapolation mechanisms throughout the visual hierarchy, that it predicts several known motion–position illusions in human observers, and that it provides a solution to the temporal binding problem. Hierarchical predictive coding is an influential model of cortical organization, in which sequential hierarchical levels are connected by backward connections carrying predictions, as well as forward connections carrying prediction errors. To date, however, predictive coding models have largely neglected to take into account that neural transmission itself takes time. For a time-varying stimulus, such as a moving object, this means that backward predictions become misaligned with new sensory input. We present an extended model implementing both forward and backward extrapolation mechanisms that realigns backward predictions to minimize prediction error. This realignment has the consequence that neural representations across all hierarchical levels become aligned in real time. Using visual motion as an example, we show that the model is neurally plausible, that it is consistent with evidence of extrapolation mechanisms throughout the visual hierarchy, that it predicts several known motion-position illusions in human observers, and that it provides a solution to the temporal binding problem.Hierarchical predictive coding is an influential model of cortical organization, in which sequential hierarchical levels are connected by backward connections carrying predictions, as well as forward connections carrying prediction errors. To date, however, predictive coding models have largely neglected to take into account that neural transmission itself takes time. For a time-varying stimulus, such as a moving object, this means that backward predictions become misaligned with new sensory input. We present an extended model implementing both forward and backward extrapolation mechanisms that realigns backward predictions to minimize prediction error. This realignment has the consequence that neural representations across all hierarchical levels become aligned in real time. Using visual motion as an example, we show that the model is neurally plausible, that it is consistent with evidence of extrapolation mechanisms throughout the visual hierarchy, that it predicts several known motion-position illusions in human observers, and that it provides a solution to the temporal binding problem. |
| Author | Hogendoorn, Hinze Burkitt, Anthony N. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31064839$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Copyright © 2019 Hogendoorn and Burkitt. Copyright © 2019 Hogendoorn and Burkitt 2019 Hogendoorn and Burkitt |
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| Issue | 2 |
| Keywords | prediction extrapolation alignment predictive coding neural delays temporal |
| Language | English |
| License | https://creativecommons.org/licenses/by-nc-sa/4.0 Copyright © 2019 Hogendoorn and Burkitt. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 H.H. and A.N.B. designed research; H.H. and A.N.B. performed research; H.H. and A.N.B. wrote the paper. H.H. was supported by the Australian Government through the Australian Research Council's Discovery Projects funding scheme (project DP180102268). The authors declare no competing financial interests. |
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| SubjectTerms | Brain - physiology Humans Illusions - physiology Models, Neurological Motion Perception - physiology Synaptic Transmission - physiology Theory/New Concepts |
| Title | Predictive Coding with Neural Transmission Delays: A Real-Time Temporal Alignment Hypothesis |
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