Journal Article FZJ-2022-04370

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Gell-Mann–Low Criticality in Neural Networks

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2022
APS College Park, Md.

Physical review letters 128(16), 168301 () [10.1103/PhysRevLett.128.168301]

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Abstract: 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.

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Contributing Institute(s):
  1. Computational and Systems Neuroscience (INM-6)
  2. Jara-Institut Brain structure-function relationships (INM-10)
  3. Theoretical Neuroscience (IAS-6)
  4. Theorie der Starken Wechselwirkung (IAS-4)
  5. Theorie der starken Wechselwirkung (IKP-3)
Research Program(s):
  1. 5231 - Neuroscientific Foundations (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)

Appears in the scientific report 2022
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Institute Collections > IKP > IKP-3
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Institute Collections > INM > INM-6
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 Record created 2022-11-07, last modified 2024-06-10