Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-30T20:35:41.677Z Has data issue: false hasContentIssue false

Neuronal codes for predictive processing in cortical layers

Published online by Cambridge University Press:  19 June 2020

Lucy S. Petro
Affiliation:
Institute of Neuroscience and Psychology, University of Glasgow, GlasgowG12 8QB, UK. [email protected]
Lars Muckli
Affiliation:
Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, GlasgowG12 8QB, UK. [email protected] https://muckli.psy.gla.ac.uk/

Abstract

Predictive processing as a computational motif of the neocortex needs to be elaborated into theories of higher cognitive functions that include simulating future behavioural outcomes. We contribute to the neuroscientific perspective of predictive processing as a foundation for the proposed representational architectures of the mind.

Type
Open Peer Commentary
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bar, M. (2007) The proactive brain: using analogies and associations to generate predictions. Trends in Cognitive Sciences 11(7):280–89.CrossRefGoogle ScholarPubMed
Bergmann, J., Morgan, A. T. & Muckli, L. (2019) Two distinct feedback codes in V1 for ‘real’ and ‘imaginary’ internal experiences. BioRxiv 664870.Google Scholar
Dijkstra, N., Zeidman, P., Ondobaka, S., van Gerven, M. A. & Friston, K. (2017) Distinct top-down and bottom-up brain connectivity during visual perception and imagery. Scientific Reports 7, Article number: 5677.CrossRefGoogle ScholarPubMed
Dohmatob, E., Dumas, G. & Bzdok, D. (2018) Dark control: Towards a unified account of default mode function by Markov decision processes. bioRxiv 148890.Google Scholar
Edwards, G., Vetter, P., McGruer, F., Petro, LS & Muckli, L. (2017) Predictive feedback to V1 dynamically updates with sensory input. Scientific Reports 7(1):16538.CrossRefGoogle ScholarPubMed
Feldman, H. & Friston, K. (2010) Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience 4:215.CrossRefGoogle ScholarPubMed
Friston, K. (2005) A theory of cortical responses. Philosophical transactions of the Royal Society B: Biological Sciences 360(1456):815–36.CrossRefGoogle ScholarPubMed
George, D. & Hawkins, J. (2009) Towards a mathematical theory of cortical micro-circuits. PLOS Computational Biology 5(10):e1000532.CrossRefGoogle ScholarPubMed
Gordon, N., Tsuchiya, N., Koenig-Robert, R. & Hohwy, J. (2019) Expectation and attention increase the integration of top-down and bottom-up signals in perception through different pathways. PLOS Biology 17(4):e3000233.CrossRefGoogle ScholarPubMed
Spratling, M. W. (2017) A review of predictive coding algorithms. Brain and Cognition 112:9297.CrossRefGoogle ScholarPubMed