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      A direct interareal feedback-to-feedforward circuit in primate visual cortex

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          Abstract

          The mammalian sensory neocortex consists of hierarchically organized areas reciprocally connected via feedforward (FF) and feedback (FB) circuits. Several theories of hierarchical computation ascribe the bulk of the computational work of the cortex to looped FF-FB circuits between pairs of cortical areas. However, whether such corticocortical loops exist remains unclear. In higher mammals, individual FF-projection neurons send afferents almost exclusively to a single higher-level area. However, it is unclear whether FB-projection neurons show similar area-specificity, and whether they influence FF-projection neurons directly or indirectly. Using viral-mediated monosynaptic circuit tracing in macaque primary visual cortex (V1), we show that V1 neurons sending FF projections to area V2 receive monosynaptic FB inputs from V2, but not other V1-projecting areas. We also find monosynaptic FB-to-FB neuron contacts as a second motif of FB connectivity. Our results support the existence of FF-FB loops in primate cortex, and suggest that FB can rapidly and selectively influence the activity of incoming FF signals.

          Abstract

          In the cerebral cortex, information is processed by multiple hierarchically organized areas, reciprocally connected via feedforward and feedback circuits. Here the authors show that in primate visual cortex, feedforward projection neurons receive monosynaptic feedback contacts selectively from the area to which they project.

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          Most cited references76

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          The free-energy principle: a unified brain theory?

          A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories - optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.
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            Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.

            We describe a model of visual processing in which feedback connections from a higher- to a lower-order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward connections carry the residual errors between the predictions and the actual lower-level activities. When exposed to natural images, a hierarchical network of model neurons implementing such a model developed simple-cell-like receptive fields. A subset of neurons responsible for carrying the residual errors showed endstopping and other extra-classical receptive-field effects. These results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.
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              How does the brain solve visual object recognition?

              Mounting evidence suggests that 'core object recognition,' the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains poorly understood. Here we review evidence ranging from individual neurons and neuronal populations to behavior and computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical subnetworks with a common functional goal. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                alessandra.angelucci@hsc.utah.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 August 2021
                13 August 2021
                2021
                : 12
                : 4911
                Affiliations
                [1 ]GRID grid.223827.e, ISNI 0000 0001 2193 0096, Department of Ophthalmology and Visual Science, Moran Eye Institute, , University of Utah, ; Salt Lake City, UT USA
                [2 ]GRID grid.1029.a, ISNI 0000 0000 9939 5719, Present Address: Medical Science, School of Science, , Western Sydney University, ; Campbelltown, NSW Australia
                Author information
                http://orcid.org/0000-0003-2103-4974
                http://orcid.org/0000-0002-1957-2231
                Article
                24928
                10.1038/s41467-021-24928-6
                8363744
                34389710
                e7a77378-e597-42d6-9f5e-875aa3d527f6
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 July 2020
                : 8 July 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000053, U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI);
                Award ID: R01 EY026812
                Award ID: R01 EY019743
                Award ID: Core EY014800
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI)
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: BRAIN U01 NS099702
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100001818, Research to Prevent Blindness (RPB);
                Funded by: U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI)
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                neural circuits,sensory processing,striate cortex
                Uncategorized
                neural circuits, sensory processing, striate cortex

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