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000911044 1001_ $$0P:(DE-Juel1)177789$$aTiberi, Lorenzo$$b0$$eCorresponding author$$ufzj
000911044 245__ $$aGell-Mann–Low Criticality in Neural Networks
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000911044 520__ $$aCriticality is deeply related to optimal computational capacity. The lack of a renormalized theory of critical brain dynamics, however, so far limits insights into this form of biological information processing to mean-field results. These methods neglect a key feature of critical systems: the interaction between degrees of freedom across all length scales, required for complex nonlinear computation. We present a renormalized theory of a prototypical neural field theory, the stochastic Wilson-Cowan equation. We compute the flow of couplings, which parametrize interactions on increasing length scales. Despite similarities with the Kardar-Parisi-Zhang model, the theory is of a Gell-Mann–Low type, the archetypal form of a renormalizable quantum field theory. Here, nonlinear couplings vanish, flowing towards the Gaussian fixed point, but logarithmically slowly, thus remaining effective on most scales. We show this critical structure of interactions to implement a desirable trade-off between linearity, optimal for information storage, and nonlinearity, required for computation.
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000911044 7001_ $$0P:(DE-Juel1)171475$$aStapmanns, Jonas$$b1
000911044 7001_ $$0P:(DE-Juel1)164473$$aKühn, Tobias$$b2
000911044 7001_ $$0P:(DE-Juel1)159481$$aLuu, Thomas$$b3
000911044 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b4
000911044 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b5
000911044 773__ $$0PERI:(DE-600)1472655-5$$a10.1103/PhysRevLett.128.168301$$gVol. 128, no. 16, p. 168301$$n16$$p168301$$tPhysical review letters$$v128$$x0031-9007$$y2022
000911044 8564_ $$uhttps://juser.fz-juelich.de/record/911044/files/Invoice_INV_22_MAR_008032.pdf
000911044 8564_ $$uhttps://juser.fz-juelich.de/record/911044/files/PhysRevLett.128.168301.pdf$$yOpenAccess
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