TY  - JOUR
AU  - Tiberi, Lorenzo
AU  - Stapmanns, Jonas
AU  - Kühn, Tobias
AU  - Luu, Thomas
AU  - Dahmen, David
AU  - Helias, Moritz
TI  - Gell-Mann–Low Criticality in Neural Networks
JO  - Physical review letters
VL  - 128
IS  - 16
SN  - 0031-9007
CY  - College Park, Md.
PB  - APS
M1  - FZJ-2022-04370
SP  - 168301
PY  - 2022
AB  - Criticality is deeply related to optimal computational capacity. The lack of a renormalized theory of critical brain dynamics, however, so far limits insights into this form of biological information processing to mean-field results. These methods neglect a key feature of critical systems: the interaction between degrees of freedom across all length scales, required for complex nonlinear computation. We present a renormalized theory of a prototypical neural field theory, the stochastic Wilson-Cowan equation. We compute the flow of couplings, which parametrize interactions on increasing length scales. Despite similarities with the Kardar-Parisi-Zhang model, the theory is of a Gell-Mann–Low type, the archetypal form of a renormalizable quantum field theory. Here, nonlinear couplings vanish, flowing towards the Gaussian fixed point, but logarithmically slowly, thus remaining effective on most scales. We show this critical structure of interactions to implement a desirable trade-off between linearity, optimal for information storage, and nonlinearity, required for computation.
LB  - PUB:(DE-HGF)16
C6  - 35522522
UR  - <Go to ISI:>//WOS:000804565600003
DO  - DOI:10.1103/PhysRevLett.128.168301
UR  - https://juser.fz-juelich.de/record/911044
ER  -