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| 001 | 1015246 | ||
| 005 | 20240313103127.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2023-03601 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2023-03601 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Dick, Michael |0 P:(DE-Juel1)176960 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Bernstein Conference 2023 |c Berlin |d 2023-09-26 - 2023-09-30 |w Germany |
| 245 | _ | _ | |a Linking Network and Neuron Level Correlations via Renormalized Field Theory |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
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| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1695641536_30160 |2 PUB:(DE-HGF) |x After Call |
| 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 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. |
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| 536 | _ | _ | |a RenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A) |0 G:(DE-Juel-1)BMBF-01IS19077A |c BMBF-01IS19077A |x 2 |
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| 536 | _ | _ | |a MSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018) |0 G:(DE-Juel1)HGF-SMHB-2014-2018 |c HGF-SMHB-2014-2018 |f MSNN |x 4 |
| 700 | 1 | _ | |a Alexander, van Meegen |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Helias, Moritz |0 P:(DE-Juel1)144806 |b 2 |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1015246/files/poster.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2023 |
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| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 2 |
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