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001     916173
005     20240313095006.0
037 _ _ |a FZJ-2022-05993
100 1 _ |a Morales-Gregorio, Aitor
|0 P:(DE-Juel1)176593
|b 0
|e Corresponding author
|u fzj
111 2 _ |a Redwood seminar hosted by Prof. Fritz Sommer
|c UC Berkeley
|w USA
245 _ _ |a Feedback modulation of neural manifolds in macaque primary visual cortex
|f 2022-11-21 -
260 _ _ |c 2022
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a Other
|2 DataCite
336 7 _ |a INPROCEEDINGS
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336 7 _ |a LECTURE_SPEECH
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336 7 _ |a Talk (non-conference)
|b talk
|m talk
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|s 1673328000_24179
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|x Invited
336 7 _ |a Other
|2 DINI
520 _ _ |a High-dimensional brain activity is in many cases organized into lower-dimensional neural manifolds [1,2]. Feedback from V4 to V1 is known to mediate visual attention [3] and computational work has shown that it can also rotate neural manifolds in a context-dependent manner [4]. However, whether feedback signals can modulate neural manifolds in vivo remains to be ascertained. Here, we studied the neural manifolds in macaque (Macaca mulatta) visual cortex during resting state [5] and found two distinct high-dimensional clusters in the activity. The clusters were primarily correlated with behavioral state (eye closure) and had distinct dimensionality. Granger causality analysis revealed that feedback from V4 to V1 was significantly stronger during the eyes-open periods. Finally, spiking neuron model simulations confirmed that signals mimicking V4-to-V1 feedback can modulate neural manifolds. Taken together, the data analysis and simulations suggest that feedback signals actively modulate neural manifolds in the visual cortex of the macaque.References:[1] Stringer et al. (2020). Nature 571, 361-365. https://doi.org/10.1038/s41586-019-1346-5[2] Singh et al. (2008). Journal of Vision 8(8), 11. https://doi.org/10.1167/8.8.11[3] Poort et al. (2012). Neuron 75 (1), 143-156. https://doi.org/10.1016/j.neuron.2012.04.032[4] Naumann et al. (2022). eLife 11, 76096. https://doi.org/10.7554/eLife.76096[5] Chen*, Morales-Gregorio* et al. (2022). Scientific Data 9 (1), 77. https://doi.org/10.1038/s41597-022-01180-1
536 _ _ |a 5231 - Neuroscientific Foundations (POF4-523)
|0 G:(DE-HGF)POF4-5231
|c POF4-523
|x 0
|f POF IV
536 _ _ |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
|0 G:(EU-Grant)945539
|c 945539
|x 1
|f H2020-SGA-FETFLAG-HBP-2019
536 _ _ |a SPP 2041 347572269 - Integration von Multiskalen-Konnektivität und Gehirnarchitektur in einem supercomputergestützten Modell der menschlichen Großhirnrinde (347572269)
|0 G:(GEPRIS)347572269
|c 347572269
|x 2
536 _ _ |a GRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)
|0 G:(GEPRIS)368482240
|c 368482240
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909 C O |o oai:juser.fz-juelich.de:916173
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910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
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|6 P:(DE-Juel1)176593
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
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|v Neuromorphic Computing and Network Dynamics
|9 G:(DE-HGF)POF4-5231
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914 1 _ |y 2022
920 1 _ |0 I:(DE-Juel1)INM-6-20090406
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920 1 _ |0 I:(DE-Juel1)IAS-6-20130828
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920 1 _ |0 I:(DE-Juel1)INM-10-20170113
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|l Jara-Institut Brain structure-function relationships
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980 _ _ |a talk
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980 _ _ |a I:(DE-Juel1)INM-6-20090406
980 _ _ |a I:(DE-Juel1)IAS-6-20130828
980 _ _ |a I:(DE-Juel1)INM-10-20170113
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981 _ _ |a I:(DE-Juel1)IAS-6-20130828

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