Hauptseite > Publikationsdatenbank > Linking network- and neuron-level correlations by renormalized field theory > print

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100 1 _ |a Dick, Michael
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245 _ _ |a Linking network- and neuron-level correlations by renormalized field theory
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520 _ _ |a 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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700 1 _ |a Meegen, Alexander van
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700 1 _ |a Helias, Moritz
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773 _ _ |a 10.1103/PhysRevResearch.6.033264
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