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  Increased region of surround stimulation enhances contextual feedback and feedforward processing in human V1

Revina, Y., Petro, L. S., Denk-Florea, C. B., Rao, I. S., & Muckli, L. (2021). Increased region of surround stimulation enhances contextual feedback and feedforward processing in human V1. bioRxiv. doi:10.1101/2021.02.27.433018.

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Revina, Yulia1, Author                 
Petro, Lucy S.1, Author
Denk-Florea, Cristina B.1, Author
Rao, Isa S.1, Author
Muckli, Lars1, Author
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1Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, ou_persistent22              

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 Abstract: The majority of synaptic inputs to the primary visual cortex (V1) are non-feedforward, instead originating from local and anatomical feedback connections. Animal electrophysiology experiments show that feedback signals originating from higher visual areas with larger receptive fields modulate the surround receptive fields of V1 neurons. Theories of cortical processing propose various roles for feedback and feedforward processing, but systematically investigating their independent contributions to cortical processing is challenging because feedback and feedforward processes coexist even in single neurons. Capitalising on the larger receptive fields of higher visual areas compared to primary visual cortex (V1), we used an occlusion paradigm that isolates top-down influences from feedforward processing. We utilised functional magnetic resonance imaging (fMRI) and multi-voxel pattern analysis methods in humans viewing natural scene images. We parametrically measured how the availability of contextual information determines the presence of detectable feedback information in non-stimulated V1, and how feedback information interacts with feedforward processing. We show that increasing the visibility of the contextual surround increases scene-specific feedback information, and that this contextual feedback enhances feedforward information. Our findings are in line with theories that cortical feedback signals transmit internal models of predicted inputs.

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 Dates: 2021-02-28
 Publication Status: Published online
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 Identifiers: DOI: 10.1101/2021.02.27.433018
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Title: bioRxiv
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