001015246 001__ 1015246
001015246 005__ 20240313103127.0
001015246 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-03601
001015246 037__ $$aFZJ-2023-03601
001015246 041__ $$aEnglish
001015246 1001_ $$0P:(DE-Juel1)176960$$aDick, Michael$$b0$$eCorresponding author$$ufzj
001015246 1112_ $$aBernstein Conference 2023$$cBerlin$$d2023-09-26 - 2023-09-30$$wGermany
001015246 245__ $$aLinking Network and Neuron Level Correlations via Renormalized Field Theory
001015246 260__ $$c2023
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001015246 520__ $$aIt is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well-suited for computation and critical slowing down, which may offer a mechanism for dynamic memory. However, mean-field approximations, while versatile and popular, inherently neglect the fluctuations responsible for such critical dynamics. Thus, a renormalized theory is necessary. We consider the Sompolinsky-Crisanti-Sommers model which displays a well studied chaotic as well as a magnetic transition. Based on the analogue of a quantum effective action, we derive self-consistency equations for the first two renormalized Greens functions. Their self-consistent solution reveals a coupling between the population level activity and single neuron heterogeneity. The quantitative theory explains the population autocorrelation function, the single-unit autocorrelation function with its multiple temporal scales, and cross correlations.
001015246 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001015246 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
001015246 536__ $$0G:(DE-Juel-1)BMBF-01IS19077A$$aRenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A)$$cBMBF-01IS19077A$$x2
001015246 536__ $$0G:(GEPRIS)491111487$$aDFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x3
001015246 536__ $$0G:(DE-Juel1)HGF-SMHB-2014-2018$$aMSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)$$cHGF-SMHB-2014-2018$$fMSNN$$x4
001015246 7001_ $$0P:(DE-HGF)0$$aAlexander, van Meegen$$b1
001015246 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b2$$ufzj
001015246 8564_ $$uhttps://juser.fz-juelich.de/record/1015246/files/poster.pdf$$yOpenAccess
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001015246 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b2$$kFZJ
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001015246 9141_ $$y2023
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001015246 9201_ $$0I:(DE-Juel1)PGI-1-20110106$$kPGI-1$$lQuanten-Theorie der Materialien$$x0
001015246 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x1
001015246 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
001015246 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x3
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