000916173 001__ 916173
000916173 005__ 20240313095006.0
000916173 037__ $$aFZJ-2022-05993
000916173 1001_ $$0P:(DE-Juel1)176593$$aMorales-Gregorio, Aitor$$b0$$eCorresponding author$$ufzj
000916173 1112_ $$aRedwood seminar hosted by Prof. Fritz Sommer$$cUC Berkeley$$wUSA
000916173 245__ $$aFeedback modulation of neural manifolds in macaque primary visual cortex$$f2022-11-21 - 
000916173 260__ $$c2022
000916173 3367_ $$033$$2EndNote$$aConference Paper
000916173 3367_ $$2DataCite$$aOther
000916173 3367_ $$2BibTeX$$aINPROCEEDINGS
000916173 3367_ $$2ORCID$$aLECTURE_SPEECH
000916173 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1673328000_24179$$xInvited
000916173 3367_ $$2DINI$$aOther
000916173 520__ $$aHigh-dimensional brain activity is in many cases organized into lower-dimensional neural manifolds [1,2]. Feedback from V4 to V1 is known to mediate visual attention [3] and computational work has shown that it can also rotate neural manifolds in a context-dependent manner [4]. However, whether feedback signals can modulate neural manifolds in vivo remains to be ascertained. Here, we studied the neural manifolds in macaque (Macaca mulatta) visual cortex during resting state [5] and found two distinct high-dimensional clusters in the activity. The clusters were primarily correlated with behavioral state (eye closure) and had distinct dimensionality. Granger causality analysis revealed that feedback from V4 to V1 was significantly stronger during the eyes-open periods. Finally, spiking neuron model simulations confirmed that signals mimicking V4-to-V1 feedback can modulate neural manifolds. Taken together, the data analysis and simulations suggest that feedback signals actively modulate neural manifolds in the visual cortex of the macaque.References:[1] Stringer et al. (2020). Nature 571, 361-365. https://doi.org/10.1038/s41586-019-1346-5[2] Singh et al. (2008). Journal of Vision 8(8), 11. https://doi.org/10.1167/8.8.11[3] Poort et al. (2012). Neuron 75 (1), 143-156. https://doi.org/10.1016/j.neuron.2012.04.032[4] Naumann et al. (2022). eLife 11, 76096. https://doi.org/10.7554/eLife.76096[5] Chen*, Morales-Gregorio* et al. (2022). Scientific Data 9 (1), 77. https://doi.org/10.1038/s41597-022-01180-1
000916173 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000916173 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
000916173 536__ $$0G:(GEPRIS)347572269$$aSPP 2041 347572269 - Integration von Multiskalen-Konnektivität und Gehirnarchitektur in einem supercomputergestützten Modell der menschlichen Großhirnrinde (347572269)$$c347572269$$x2
000916173 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x3
000916173 909CO $$ooai:juser.fz-juelich.de:916173$$pec_fundedresources$$pVDB$$popenaire
000916173 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176593$$aForschungszentrum Jülich$$b0$$kFZJ
000916173 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000916173 9141_ $$y2022
000916173 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000916173 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000916173 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000916173 980__ $$atalk
000916173 980__ $$aVDB
000916173 980__ $$aI:(DE-Juel1)INM-6-20090406
000916173 980__ $$aI:(DE-Juel1)IAS-6-20130828
000916173 980__ $$aI:(DE-Juel1)INM-10-20170113
000916173 980__ $$aUNRESTRICTED
000916173 981__ $$aI:(DE-Juel1)IAS-6-20130828