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arxiv: 2604.21071 · v2 · submitted 2026-04-22 · ⚛️ physics.bio-ph

Recognition: no theorem link

A Critical Assessment of the Brain Criticality Hypothesis

Chesson Sipling , Yuan-Hang Zhang , Massimiliano Di Ventra

Authors on Pith no claims yet

Pith reviewed 2026-05-11 00:42 UTC · model grok-4.3

classification ⚛️ physics.bio-ph
keywords criticality hypothesisscale-invariant correlationsneural activitylong-range ordermemory resourcesbrain dynamicsneuroscience models
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The pith

Coupling neurons to slowly varying resources generates scale-invariant activity without criticality.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that scale-invariant correlations in neural activity, commonly linked to the brain operating at a critical point, can arise instead from interactions between neurons and slowly changing resources that serve as memory. This memory-induced long-range order produces the observed patterns in a robust phase of activity. A sympathetic reader would care because the alternative avoids the need for precise tuning to a special state and offers a simpler account grounded in resource dynamics rather than criticality. The claim challenges a leading hypothesis by showing how memory effects alone can sustain the correlations seen in experiments.

Core claim

Coupling between neurons and slowly varying resources acting as memory is sufficient by itself to produce a robust phase of neural activity with scale-invariant correlations, offering a more natural explanation for existing experimental data than the criticality hypothesis.

What carries the argument

The memory-induced long-range order phase that emerges from neuron coupling to slow resources.

If this is right

  • Scale-invariant neural correlations can appear in non-critical regimes driven by resource memory.
  • Models of brain activity need to treat slowly varying resources as an essential component rather than an afterthought.
  • Experimental signatures previously interpreted as evidence for criticality may instead reflect memory effects from resource coupling.
  • Optimal information processing in neural systems does not require operation near a critical point.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Computational simulations that include explicit slow resource variables could reproduce experimental correlation patterns without parameter tuning to criticality.
  • The mechanism may generalize to other biological or physical systems where fast variables couple to slow memory-like degrees of freedom.
  • Targeted experiments that modulate resource recovery times in cultured neural networks would provide direct tests of the proposed phase.

Load-bearing premise

That the memory-induced long-range order phase explains the observed scale-invariant correlations without implicitly depending on critical dynamics or specific unstated assumptions about resource timescales.

What would settle it

Measuring neural activity in a preparation where resource variation is either eliminated or made as fast as neural firing and finding that scale-invariant correlations disappear would test the claim.

Figures

Figures reproduced from arXiv: 2604.21071 by Chesson Sipling, Massimiliano Di Ventra, Yuan-Hang Zhang.

Figure 1
Figure 1. Figure 1: FIG. 1. A sketch of the human brain. Here, we consider [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Avalanche size, [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. A plot of [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
read the original abstract

A major unresolved question in Neuroscience is: What is the origin of the observed scale-invariant correlations in neural activity? Many researchers support the ``criticality hypothesis,'' which proposes that the brain operates near criticality, optimizing various information processing functions. However, the nature and behavior of criticality in cortical systems are still unclear. Alternatively, this opinion paper highlights that the coupling between neurons and slowly varying resources (acting as ``memory'') alone may be sufficient to generate a robust phase of neural activity with scale-invariant correlations. This memory-induced long-range order phase could provide a more natural explanation of the existing experimental data than the criticality hypothesis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript is an opinion paper arguing that coupling between neurons and slowly varying resources (acting as memory) may be sufficient by itself to produce a robust phase of neural activity with scale-invariant correlations, offering a more natural explanation for experimental data than the brain criticality hypothesis.

Significance. If a concrete model were to demonstrate generic emergence of long-range order from slow resource dynamics without fine-tuning, the proposal could meaningfully challenge the criticality framework and reframe interpretations of scale-free neural statistics. As presented, however, the conceptual argument lacks the quantitative grounding needed to evaluate whether the mechanism is distinct from or independent of critical dynamics.

major comments (2)
  1. Abstract: the central claim that neuron-resource coupling 'alone may be sufficient' to generate scale-invariant correlations rests entirely on qualitative reasoning with no dynamical equations, stability analysis, or simulation results shown. This is load-bearing for the sufficiency and 'more natural' assertions, as the skeptic note correctly identifies that without an explicit derivation one cannot verify independence from parameter tuning or implicit critical behavior.
  2. Abstract: the manuscript does not specify the functional form of the resource dynamics, the coupling term, or the relaxation timescale, leaving open whether the proposed memory-induced long-range order phase requires the resource timescale to be tuned to the observation window (which would reduce the mechanism to a form of effective criticality rather than a distinct robust phase).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments on our opinion manuscript. We clarify below that the paper is intended as a conceptual discussion proposing an alternative mechanism, but we agree that additional specificity would strengthen the presentation and address the concerns about quantitative grounding.

read point-by-point responses
  1. Referee: Abstract: the central claim that neuron-resource coupling 'alone may be sufficient' to generate scale-invariant correlations rests entirely on qualitative reasoning with no dynamical equations, stability analysis, or simulation results shown. This is load-bearing for the sufficiency and 'more natural' assertions, as the skeptic note correctly identifies that without an explicit derivation one cannot verify independence from parameter tuning or implicit critical behavior.

    Authors: We acknowledge that the central claim relies on qualitative reasoning, as is appropriate for an opinion paper whose goal is to highlight a conceptual alternative rather than deliver a complete modeling study. The argument is that slow resource dynamics acting as memory can produce long-range correlations generically through timescale separation, without the fine-tuning required at a critical point. We disagree that this necessarily reduces to implicit criticality, but we accept that an explicit minimal model would help readers evaluate the distinction. In revision we will add a short section outlining a possible dynamical model (resource equation with linear relaxation coupled to neural activity) to illustrate the mechanism, while keeping the paper's opinion character. revision: partial

  2. Referee: Abstract: the manuscript does not specify the functional form of the resource dynamics, the coupling term, or the relaxation timescale, leaving open whether the proposed memory-induced long-range order phase requires the resource timescale to be tuned to the observation window (which would reduce the mechanism to a form of effective criticality rather than a distinct robust phase).

    Authors: We agree that the current text leaves the functional forms unspecified. The intended distinction is that any sufficiently slow resource dynamics (relative to neural timescales) will generate scale-free statistics across a wide range of observation windows due to the memory effect, without requiring the resource timescale to match the observation window exactly. This differs from criticality, which demands parameter tuning to a specific point. We will revise the manuscript to include example functional forms (e.g., resource relaxation dR/dt = -R/τ + coupling to activity, with τ much larger than neural timescales) and a brief discussion of why the separation of timescales renders the phase robust rather than tuned. revision: yes

Circularity Check

0 steps flagged

No circularity: conceptual opinion paper with no derivations or self-referential reductions

full rationale

The manuscript is framed as an opinion paper whose central claim—that neuron-resource coupling alone can produce scale-invariant correlations—is presented conceptually without equations, parameter fitting, or explicit derivations. No load-bearing steps reduce by construction to inputs, self-citations, or ansatzes; the argument does not invoke uniqueness theorems or rename known results. The provided abstract and structure contain no mathematical content that could exhibit the enumerated circularity patterns, making the derivation chain self-contained by absence of formal claims.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that slowly varying resources can be treated as memory without additional critical dynamics; no free parameters or invented entities with independent evidence are specified in the abstract.

axioms (1)
  • domain assumption Neurons couple to slowly varying resources that act as memory sufficient to produce scale-invariant correlations
    This is the core premise invoked to generate the alternative phase of activity.
invented entities (1)
  • memory-induced long-range order phase no independent evidence
    purpose: To explain scale-invariant neural correlations as an alternative to criticality
    Postulated mechanism without a falsifiable handle provided in the abstract

pith-pipeline@v0.9.0 · 5396 in / 1234 out tokens · 28963 ms · 2026-05-11T00:42:11.469349+00:00 · methodology

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Reference graph

Works this paper leans on

52 extracted references · 1 canonical work pages

  1. [1]

    Emerge due to collective behavior,

  2. [2]

    Possess long-range temporal correlations, and

  3. [3]

    supporting information processing

    Support information processing. Unlike Destexhe and Touboul’s counterexamples, memory-induced long-range orderdoessatisfy each of these physically motivated requirements. First, the long- range order in these systems emergespreciselydue to collective behavior in neural activity, which is induced by the presence of memory as provided by the resources. Addi...

  4. [4]

    J. M. Beggs and D. Plenz, Journal of Neuroscience23, 11167 (2003)

  5. [5]

    Benayoun, J

    M. Benayoun, J. D. Cowan, W. van Drongelen, and E. Wallace, PLOS Computational Biology6, 1 (2010)

  6. [6]

    J. M. Beggs, Philosophical Transactions of the Royal So- ciety A: Mathematical, Physical and Engineering Sci- ences366, 329 (2007)

  7. [7]

    Friedman, S

    N. Friedman, S. Ito, B. A. W. Brinkman, M. Shimono, R. E. L. DeVille, K. A. Dahmen, J. M. Beggs, and T. C. Butler, Phys. Rev. Lett.108, 208102 (2012)

  8. [8]

    D. R. Chialvo, Nature physics6, 744 (2010)

  9. [9]

    J. M. Beggs and N. Timme, Frontiers in PhysiologyVol- ume 3 - 2012(2012)

  10. [10]

    J. M. Beggs, Frontiers in Computational Neuroscience16 (2022)

  11. [11]

    Destexhe and J

    A. Destexhe and J. D. Touboul, eNeuro8(2021)

  12. [12]

    U. C. T¨ auber,Critical dynamics: a field theory ap- proach to equilibrium and non-equilibrium scaling behav- ior(Cambridge University Press, 2014)

  13. [13]

    W. J. Reed and B. D. Hughes, Phys. Rev. E66, 067103 (2002)

  14. [14]

    Mitzenmacher, Internet Mathematics1, 226 (2004)

    M. Mitzenmacher, Internet Mathematics1, 226 (2004)

  15. [15]

    J. P. Sethna, K. A. Dahmen, and C. R. Myers, Nature 410, 242 (2001)

  16. [16]

    J. P. Sethna, K. A. Dahmen, and O. Perkovic, arXiv preprint cond-mat/0406320 (2004)

  17. [17]

    A. J. Fontenele, N. A. De Vasconcelos, T. Feliciano, L. A. Aguiar, C. Soares-Cunha, B. Coimbra, L. Dalla Porta, S. Ribeiro, A. J. Rodrigues, N. Sousa,et al., Physical review letters122, 208101 (2019)

  18. [18]

    Chan, T.-F

    L.-C. Chan, T.-F. Kok, and E. S. C. Ching, PRX Life3, 013013 (2025)

  19. [19]

    J. K.-C. Sun, C. Sipling, Y.-H. Zhang, and M. Di Ventra, Phys. Rev. E112, 064401 (2025)

  20. [20]

    M. E. Fisher and M. N. Barber, Physical Review Letters 28, 1516 (1972). 7

  21. [21]

    Priesemann, M

    V. Priesemann, M. H. Munk, and M. Wibral, BMC neu- roscience10, 40 (2009)

  22. [22]

    G. Hahn, T. Petermann, M. N. Havenith, S. Yu, W. Singer, D. Plenz, and D. Nikoli´ c, Journal of neuro- physiology104, 3312 (2010)

  23. [23]

    Yaghoubi, T

    M. Yaghoubi, T. De Graaf, J. G. Orlandi, F. Girotto, M. A. Colicos, and J. Davidsen, Scientific reports8, 3417 (2018)

  24. [24]

    Z. Ma, G. G. Turrigiano, R. Wessel, and K. B. Hengen, Neuron104, 655 (2019)

  25. [25]

    J. G. Orlandi, J. Soriano, E. Alvarez-Lacalle, S. Teller, and J. Casademunt, Nature Physics9, 582 (2013)

  26. [26]

    Di Santo, P

    S. Di Santo, P. Villegas, R. Burioni, and M. A. Mu˜ noz, Proceedings of the National Academy of Sciences115, E1356 (2018)

  27. [27]

    Dalla Porta and M

    L. Dalla Porta and M. Copelli, PLoS computational bi- ology15, e1006924 (2019)

  28. [28]

    Haldeman and J

    C. Haldeman and J. M. Beggs, Physical review letters 94, 058101 (2005)

  29. [29]

    H. R. Wilson and J. D. Cowan, Biophysical journal12, 1 (1972)

  30. [30]

    R. Kubo, J. Phys. Soc. Jap.12, 570 (1957)

  31. [31]

    Sipling, Y.-H

    C. Sipling, Y.-H. Zhang, and M. Di Ventra, Physical Re- view E112, 014124 (2025)

  32. [32]

    M. C. Morrell, I. Nemenman, and A. Sederberg, eLife12, RP89337 (2024)

  33. [33]

    J. S. Marvin, A. C. Kokotos, M. Kumar, C. Pulido, A. N. Tkachuk, J. S. Yao, T. A. Brown, and T. A. Ryan, Proceedings of the National Academy of Sciences121, e2314604121 (2024)

  34. [34]

    H. Li, S. M. Foss, Y. Dobryy, C. K. Park, S. A. Hires, N. C. Shaner, R. Y. Tsien, L. C. Osborne, and S. M. Voglmaier, Frontiers in molecular neuroscience4, 10875 (2011)

  35. [35]

    A. J. Bower, C. Renteria, J. Li, M. Marjanovic, R. Barkalifa, and S. A. Boppart, Applied Physics Let- ters118(2021)

  36. [36]

    A. Bose, S. M. Epp, R. Belenya, K. Kurcyus, E. C. Dominguez, A. Ranft, E. S. Villa, M. Bursche, C. Preibisch, G. Castrill´ on,et al., bioRxiv , 2024 (2024)

  37. [37]

    Zhang, C

    Y.-H. Zhang, C. Sipling, and M. Di Ventra, New Journal of Physics (2026)

  38. [38]

    Rabut, M

    C. Rabut, M. Correia, V. Finel, S. Pezet, M. Pernot, T. Deffieux, and M. Tanter, Nature methods16, 994 (2019)

  39. [39]

    Demas, J

    J. Demas, J. Manley, F. Tejera, K. Barber, H. Kim, F. M. Traub, B. Chen, and A. Vaziri, Nature Methods18, 1103 (2021)

  40. [40]

    Gutierrez-Barragan, N

    D. Gutierrez-Barragan, N. A. Singh, F. G. Alvino, L. Coletta, F. Rocchi, E. De Guzman, A. Galbusera, M. Uboldi, S. Panzeri, and A. Gozzi, Current biology 32, 631 (2022)

  41. [41]

    D. J. Felleman and D. C. Van Essen, Cerebral cortex (New York, NY: 1991)1, 1 (1991)

  42. [42]

    R. J. Douglas and K. A. Martin, Annu. Rev. Neurosci. 27, 419 (2004)

  43. [43]

    K. D. Harris and G. M. Shepherd, Nature neuroscience 18, 170 (2015)

  44. [44]

    J. F. Mejias, J. D. Murray, H. Kennedy, and X.-J. Wang, Science advances2, e1601335 (2016)

  45. [45]

    C. G. Langton, Physica D: nonlinear phenomena42, 12 (1990)

  46. [46]

    R¨ am¨ o, S

    P. R¨ am¨ o, S. Kauffman, J. Kesseli, and O. Yli-Harja, Physica D: Nonlinear Phenomena227, 100 (2007)

  47. [47]

    W. L. Shew, H. Yang, S. Yu, R. Roy, and D. Plenz, Jour- nal of neuroscience31, 55 (2011)

  48. [48]

    Bertschinger, T

    N. Bertschinger, T. Natschl¨ ager, and R. Legenstein, Advances in neural information processing systems17 (2004)

  49. [49]

    Wilting and V

    J. Wilting and V. Priesemann, Current Opinion in Neu- robiology58, 105 (2019), computational Neuroscience

  50. [50]

    Di Ventra and F

    M. Di Ventra and F. L. Traversa, Journal of Applied Physics123(2018)

  51. [51]

    Di Ventra,MemComputing: Fundamentals and Ap- plications(Oxford University Press, 2022)

    M. Di Ventra,MemComputing: Fundamentals and Ap- plications(Oxford University Press, 2022)

  52. [52]

    In this work, we will exclusively use the word “memory” in this context, rather than in the context of the storage or retrieval of information