Journal Article

FZJ-2024-05234

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Linking network- and neuron-level correlations by renormalized field theory

Dick, M. (Corresponding author)FZJ* ; Meegen, A. v.FZJ* ; Helias, M.FZJ*

2024
APS College Park, MD

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Please use a persistent id in citations: doi:10.1103/PhysRevResearch.6.033264  doi:10.34734/FZJ-2024-05234

Abstract: It 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 analog 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.

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Contributing Institute(s):

1. Computational and Systems Neuroscience (IAS-6)
2. Quanten-Theorie der Materialien (PGI-1)
3. Computational and Systems Neuroscience (INM-6)

Research Program(s):

1. 5232 - Computational Principles (POF4-523) (POF4-523)
2. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
3. RenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A) (BMBF-01IS19077A)
4. DFG project G:(GEPRIS)491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487) (491111487)

Appears in the scientific report 2024

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