Pith. sign in

REVIEW 3 major objections 9 minor 55 references

Optimal signal transmission and timescale diversity in a model of human brain operating near criticality

T0 review · 3 major / 9 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper shows that near a connectome-driven phase transition, a whole-brain mean field model simultaneously minimizes signal attenuation and maximizes diversity of intrinsic timescales, proposing criticality as a unifying mechanism for…

desk verdict A serious whole-brain mean-field study with a real criticality story, but the headline brain claim outruns the evidence because the spiking model is never tested on the main metrics. read the letter →

arxiv 2412.17043 v1 pith:IGXEHPBE submitted 2024-12-22 q-bio.NC physics.bio-ph

Yang Qi , Jiexiang Wang , Weiyang Ding , Gustavo Deco , Viktor Jirsa , Wenlian Lu , Jianfeng Feng This is my paper · ORCID
classification q-bio.NCphysics.bio-ph
keywords criticalitymeanfieldmodelintrinsictimescalessignaltransmissionwholebrainautocorrelationwindowphasetransitionstructuralconnectome
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that criticality—the state a whole-brain model reaches when cross-regional coupling crosses a phase transition—simultaneously solves two problems the brain faces: transmitting sensory signals across the cortex without fading, and producing a wide range of intrinsic timescales across regions. The authors build a second-order mean field model from a large-scale spiking 'digital twin brain' model on the human structural connectome, and show that as the global coupling strength approaches the critical point, the attenuation length of a visual stimulus diverges while the entropy and range ratio of autocorrelation windows peak. If true, this gives a single dynamical mechanism by which the brain can be both sensitive to input and temporally diverse.

What carries the argument

The central object is a second-order mean field model, called a moment neural network, obtained by moment closure (Fokker-Planck diffusion approximation) and coarse-graining of a conductance-based spiking Digital Twin Brain model with 378 HCPex regions and DWI-derived structural connectivity. The state is the per-region mean and variance of excitatory and inhibitory firing rates, evolved by tau dm/dt = -m + Phi(m), with Phi decomposed into synaptic summation, an effective-current approximation for conductance-based synapses, and a moment activation mapping. Linear stability analysis of the Jacobian reveals the phase transition and its eigenmodes; signal transmission is quantified by response energy and attenuation length, and timescale diversity by the entropy and range ratio of autocorrelation-window (ACW) estimates of envelope responses.

What would settle it

Simulate the full Digital Twin Brain spiking model at its own critical point gamma = 31.6 and measure the fitted attenuation length of a V1 pulse response and the entropy/range ratio of ACWs across regions; if these quantities do not peak near gamma = 31.6, or peak only in the mean field reduction at gamma = 41.8, the claim that criticality simultaneously optimizes both properties in the brain model fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that near the critical point of a cross-regional coupling-induced phase transition, the model attains optimal signal transmission and maximal timescale diversity simultaneously. Concretely, a brief step stimulus to V1 loses less response energy across the cortex near gamma_c = 41.8 (mean field) than below or above it, with fitted attenuation length maximal at the critical point; under white-noise stimulation, the distribution of autocorrelation windows across regions broadens from narrow and unimodal to broad and multimodal, and both entropy and range ratio diverge near criticality. The authors argue criticality is a unifying mechanism: structural connectome provides the scaffold, nonlinearity allows the phase transition, and operating near the transition amplifies the hierarchy of timescales while minimizing attenuation.

Load-bearing premise

The load-bearing premise is that the second-order moment closure with the effective-current approximation captures the spiking dynamics faithfully enough near the phase transition, so that the divergent attenuation length and timescale diversity found in the mean field model are properties of the spiking brain model and not artifacts of the reduction.

Editorial extensions

If this is right

  • Near criticality, a focal visual stimulus propagates across the whole brain with minimal attenuation, so higher-order regions receive usable signal despite being far from the input site.
  • The same critical state produces a broad, multimodal distribution of intrinsic timescales, with short timescales in early visual areas and long timescales in transmodal regions such as area 46, OFC, and ACC.
  • The structural connectome supplies the scaffold for the timescale hierarchy, while biophysical nonlinearity lets criticality amplify it without requiring extreme coupling strengths.
  • The mean field model predicts the DTB spiking model's phase transition at a shifted critical point and reproduces BOLD correlations peaking near the critical regime, so the mechanism is testable at the macroscale.
  • Beyond the critical point, signal energy attenuates sharply and timescales become homogeneous, implying the brain must regulate cross-regional coupling near the transition to keep both properties.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct empirical prediction is that in vivo measures of evoked-response attenuation and ACW diversity should covary across individuals or states: whichever condition moves the brain closer to criticality should show both longer attenuation lengths and broader timescale distributions.
  • The same mechanism may generalize beyond vision: any focal input, such as auditory or somatosensory stimulation, should show distance-dependent attenuation that is minimal near criticality, a testable extension of the V1-centred analysis.
  • The phase-shift response used for the supercritical regime suggests a second signature of criticality: near the transition, perturbation response should become slow and scale-invariant, which could be probed with time-resolved perturbation experiments.
  • Because the critical point differs between the spiking model (gamma = 31.6) and the mean field approximations (gamma = 41.8), quantitative comparisons to empirical data should rely on the spiking critical point; the mean field point is a qualitative proxy.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 9 minor

Summary. This paper develops a second-order moment-closure mean-field model (MNN) of the human brain by coarse-graining a 378-region spiking Digital Twin Brain (DTB) model with a DWI-derived connectome. The authors identify a phase transition driven by the cross-regional coupling strength γ, with the DTB critical point at γc=31.6 and the MNN critical point at γc=41.8. Using the MNN at subcritical (γ=36.0), critical (γ=41.8), and supercritical (γ=42.8) values, they report that a pulse stimulus delivered to V1 propagates with maximal attenuation length near criticality, and that Gaussian white-noise stimulation produces autocorrelation windows whose entropy and range ratio peak near criticality. They conclude that criticality jointly optimizes signal transmission and timescale diversity, and that the structural connectome scaffolds the resulting timescale hierarchy.

Significance. If the central claim holds, the paper provides a substantive bridge from a biophysically detailed spiking model to a tractable whole-brain mean-field model, and it offers a concrete mechanism—operating near a connectome-driven phase transition—for two often-separated phenomena: reliable signal propagation and hierarchical timescale diversity. The quantitative metrics (attenuation length, ACW entropy, range ratio) are measured from simulations rather than imposed, and the SI control experiments (a linear model on the same connectome and a nonlinear model on a rewired Erdős–Rényi network) correctly isolate the contributions of nonlinearity and structure. The analytic gradient of the moment activation and the use of automatic differentiation for the Jacobian are useful technical contributions. The main caveat is that the load-bearing evidence is generated entirely in the reduced mean-field model, whose fidelity to the spiking dynamics near the transition is asserted rather than demonstrated, so the significance for the real brain is conditional on that fidelity being established.

major comments (3)
  1. [§2.4–2.5, Figs. 3–4; Methods 4.3–4.4] The central claim that the brain operates near criticality to achieve optimal signal transmission and timescale diversity is supported only by simulations of the MNN at γ=36.0, 41.8, and 42.8. The spiking DTB is used to locate the phase transition and to validate BOLD correlations, but its attenuation length and ACW diversity are never measured near its own critical point γc=31.6. Given that the MNN and DTB critical points differ by about 32%, and given the paper's own caveats (Methods 4.3: the effective-current approximation inherits P(Vth)=0, "which is not true when synaptic decay is present"; Methods 4.4: τ is fitted by matching oscillation frequency and each region is treated as homogeneous), the peaked metrics could in principle be an artifact of the second-order closure rather than a property of the spiking dynamics the model coarse-grains. Please either compute the attenuation length and ACW metrics in the DTB at γ≈31.6, or provide a direct validation that the MNN response statistics (not just the phase boundary) match the DTB in the critical region.
  2. [§4.7, Fig. 4E] The ACW estimation is asymmetric across regimes: for the subcritical and critical regimes the envelope is computed from the raw response, whereas for the supercritical regime the response is first band-pass filtered with a rectangular window of width 10 Hz centered on an oscillation frequency estimated from one region (8Ad, right hemisphere). This makes the supercritical side of the ACW peak in Fig. 4(E) dependent on a hand-chosen filter. Since the claim is that entropy and range ratio "increase sharply as the system approaches criticality" and then drop in the supercritical regime, the comparison should be insensitive to this choice. Please report a sensitivity analysis over filter width and filter type, or apply the same filtering protocol to all regimes.
  3. [§2.4, Fig. 3(F)] The attenuation length Δ is extracted from an exponential fit E(d)=A e^{−d/Δ}, but the paper reports no goodness-of-fit or confidence intervals for Δ. In the critical regime the binned energy appears nearly flat over distance (Fig. 3F, left), in which case the exponential fit is poorly constrained and the peak in Δ near γc could reflect a fitting artifact. Please report fit residuals, confidence bounds, and a comparison with alternative decay forms (e.g., power law or constant) for the same data.
minor comments (9)
  1. [Abstract] The phrase "is found to induced" should read "is found to induce".
  2. [§2.2] Calling the shift from γc=31.6 to γc=41.8 "slightly shifted" understates a roughly 32% change; please rephrase to be more precise.
  3. [§4.6] The stimulus is described as a "20 mV current stimulus"; current is not measured in mV. Please clarify the units and whether this is an injected current or a voltage offset.
  4. [Eq. (10)] The notation h2β(Ēαi) is ambiguous; it should be written as (hβ(Ēαi))² to be consistent with Eq. (11).
  5. [§4.4 and SI S1.1] The statement that in-degrees are the same for all neurons in a given region is in tension with SI S1.1, which describes cropping cross-regional in-degrees to a range [71, 3500]; please clarify how the coarse-grained K values are obtained.
  6. [Fig. 4] Panels (B)-(C) are single trials while (D)-(G) are averaged over 100 trials; please state whether the single-trial examples are representative and report trial-to-trial variability for the ACW metrics.
  7. [SI S3] There is a typo: "possbile" should be "possible".
  8. [References] Reference 24 lists "Xin Du" and "Jianfeng Feng" twice in the author list; please correct the reference.
  9. [Code availability] The GitHub link is appreciated; please also provide a versioned archive (e.g., Zenodo DOI) or a commit hash for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: criticality effects are measured rather than fitted, and self-citations are not load-bearing.

full rationale

The paper's derivation chain is self-contained and does not reduce any prediction to its inputs. The critical point γc=41.8 is fixed by linear stability analysis of the mean-field Jacobian (Results 2.3; Eqs. 24-25), independently of the later response metrics. The attenuation length Δ is obtained by fitting E(d)=Ae^{−d/Δ} to simulated response energies, and the ACW entropy/range ratio are computed from simulated autocorrelation windows; neither metric is defined in terms of γc, nor are any parameters fitted to make them peak at γc. The peaks at criticality therefore emerge from the simulations rather than being imposed. The model does use same-author prior work ([43] for the efficient moment-activation algorithm and [23,24] for the DTB spiking model), but these are computational/architectural building blocks, and the central criticality result is not imported from those citations; the code is publicly available and the BOLD validation against external fMRI data provides an independent check. The paper's own disclosure that the effective-current approximation assumes P(Vth)=0 'which is not true when synaptic decay is present' (Methods 4.3) and the absence of attenuation/ACW measurements in the spiking DTB are validity limitations, not examples of a fitted input being renamed as a prediction.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

No genuinely new physical entities are introduced. The free parameters are calibration or analysis choices: the effective time constant, the data-assimilated external currents, and the supercritical band-pass settings. The axioms are the standard mean field decoupling, the effective current approximation, the homogeneity of regions, linear response assumptions, and the fidelity of the connectome and parcellation data.

free parameters (3)
  • Effective time constant tau = ~11 ms
    Calibrated in Section 4.4 by matching the oscillation frequency of the mean field model to the DTB spiking model just above their critical points. It scales the Jacobian (Eq. 25) and hence all stability and timescale results.
  • Per-region external current rates lambda_u,ext(t) = time series from dHMDA
    Estimated by fitting simulated BOLD to experimental BOLD (Methods 4.1, SI S1.3). Sets the operating point of each region and thus the fixed points around which criticality is assessed.
  • Supercritical envelope band-pass frequency and width = frequency from 8Ad, width 10 Hz
    Chosen by hand in Section 4.7 to compute ACWs in the oscillatory regime; this choice affects the reported supercritical timescales.
assumptions (6)
  • domain assumption Each brain region can be treated as a homogeneous population of neurons with identical firing statistics in the coarse graining.
    Invoked in Section 4.4 to reduce the 378-region spiking DTB to a 4n-dimensional moment system; the paper notes the moment activation is valid when the circuit within each region is homogeneous.
  • domain assumption The effective current approximation maps conductance-based LIF neurons with synaptic decay to an equivalent current-based LIF neuron for moment closure.
    Used in Methods 4.3 to build phi_eff; the authors note the caveat that the boundary condition P(Vth)=0 is not strictly true and is repaired by double integration, following [44].
  • domain assumption Pre-synaptic spike trains of different neurons are approximately uncorrelated when populations are large and sparsely connected.
    Needed for the linear summation of moments in Eqs. 6-7 and 16-17, i.e., the phi_sum mapping.
  • domain assumption Linear response around the fixed point, plus a phase shift for limit cycles, captures stimulus-evoked propagation and intrinsic timescales in all regimes.
    Section 4.5 linearizes around fixed points; Section 4.6 uses a phase-shift T_lag for the supercritical limit cycle; SI S3 frames intrinsic timescales through linear response theory.
  • domain assumption The DWI-derived structural connectome, HCPex parcellation, and dHMDA-assimilated external currents are adequate representations of human brain structure and operating points.
    These data sources define the model in Section 4.1 and SI S1; if the connectome or assimilated inputs are unrepresentative, the spatial patterns of propagation and timescales are affected.
  • standard math Fokker-Planck diffusion approximation and first-passage time formulas for the current-based LIF neuron describe the input-output moment mapping.
    Foundational to the moment activation phi_MA (Eqs. 12-13), following [42,40,45]; this is a standard, published mathematical approximation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Optimal signal transmission and timescale diversity in a model of human brain operating near criticality." pith.science (2026). https://pith.science/paper/IGXEHPBE

@misc{pith2026241217043,
  author       = {Pith},
  title        = {Pith review of: Optimal signal transmission and timescale diversity in a model of human brain operating near criticality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IGXEHPBE}},
  note         = {Machine review of arXiv:2412.17043}
}
read the original abstract

Cortical neurons exhibit a hierarchy of timescales across brain regions in response to input stimuli, which is thought to be crucial for information processing of different temporal scales. Modeling studies suggest that both intra-regional circuit dynamics as well as cross-regional connectome may contribute to this timescale diversity. Equally important to diverse timescales is the ability to transmit sensory signals reliably across the whole brain. Therefore, the brain must be able to generate diverse timescales while simultaneously minimizing signal attenuation. To understand the dynamical mechanism behind these phenomena, we develop a second-order mean field model of the human brain by applying moment closure and coarse-graining to a digital twin brain model endowed with whole brain structural connectome. Cross-regional coupling strength is found to induced a phase transition from asynchronous activity to synchronous oscillation. By analyzing the input-response properties of the model, we reveal criticality as a unifying mechanism for enabling simultaneously optimal signal transmission and timescales diversity. We show how structural connectome and criticality jointly shape intrinsic timescale hierarchy across the brain.

Figures

Figures reproduced from arXiv: 2412.17043 by the authors.

Figure 1
Figure 1. Phase transition induced by cross-regional coupling [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Linear stability analysis and whole brain eigenmodes [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Enhancement of visual signal propagation near criticality. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Optimal diversity of intrinsic timescales near criticality. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

55 extracted references · 54 canonical work pages

  1. [1]

    Intrinsic neural timescales: temporal integration and segregation

    Annemarie Wolff, Nareg Berberian, Mehrshad Golesorkhi, Javier Gomez-Pilar, Federico Zilio, and Georg Northoff. Intrinsic neural timescales: temporal integration and segregation. Trends in Cognitive Sciences, 26(2):159–173, 2022

    work page 2022

  2. [2]

    Mehrshad Golesorkhi, Javier Gomez-Pilar, Federico Zilio, Nareg Berberian, Annemarie Wolff, Mustapha C. E. Yagoub, and Georg Northoff. The brain and its time: intrinsic neural timescales are key for input processing. Communications Biology, 4(1):970, 2021

    work page 2021

  3. [3]

    Daniel Senkowski and Andreas K. Engel. Multi-timescale neural dynamics for multisensory integration. Nature Reviews Neuroscience, pages 1–18, 2024

    work page 2024

  4. [4]

    Cavanagh, Laurence T

    Sean E. Cavanagh, Laurence T. Hunt, and Steven W. Kennerley. A diversity of intrinsic timescales underlie neural computations. Frontiers in Neural Circuits, 14:615626, 2020

    work page 2020

  5. [5]

    Honey, Thomas Thesen, Tobias H

    Christopher J. Honey, Thomas Thesen, Tobias H. Donner, Lauren J. Silbert, Chad E. Carlson, Orrin Devinsky, Werner K. Doyle, Nava Rubin, David J. Heeger, and Uri Hasson. Slow cortical dynamics and the accumulation of information over long timescales. Neuron, 76(2):423–434, 2012

    work page 2012

  6. [6]

    Claire H. C. Chang, Samuel A. Nastase, and Uri Hasson. Information flow across the cortical timescale hierarchy during narrative construction. Proceedings of the National Academy of Sciences , 119(51):e2209307119, 2022

    work page 2022

  7. [7]

    Himberger, Hsiang-Yun Chien, and Christopher J

    Kevin D. Himberger, Hsiang-Yun Chien, and Christopher J. Honey. Principles of temporal processing across the cortical hierarchy. Neuroscience, 389:161–174, 2018

    work page 2018

  8. [8]

    Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

    Sean E Cavanagh, Joni D Wallis, Steven W Kennerley, and Laurence T Hunt. Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice. eLife, 5:e18937, 2016

    work page 2016

Show all 55 references
  1. [9]

    Steinmetz, Marc A

    Roxana Zeraati, Yan-Liang Shi, Nicholas A. Steinmetz, Marc A. Gieselmann, Alexander Thiele, Tirin Moore, Anna Levina, and Tatiana A. Engel. Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity. Nature Communications, 14(1):...

    2023

  2. [10]

    A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies

    Merav Stern, Nicolae Istrate, and Luca Mazzucato. A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies. eLife, 12:e86552, 2023

    2023

  3. [11]

    A diversity of localized timescales in network activity

    Rishidev Chaudhuri, Alberto Bernacchia, and Xiao-Jing Wang. A diversity of localized timescales in network activity. eLife, 3:e01239, 2014

    2014

  4. [12]

    Once upon a (slow) time in the land of recurrent neuronal networks

    Chengcheng Huang and Brent Doiron. Once upon a (slow) time in the land of recurrent neuronal networks. . . . Current Opinion in Neurobiology , 46:31–38, 2017

    2017

  5. [13]

    Hierarchical timescales in the neocortex: Mathematical mechanism and biological insights

    Songting Li and Xiao-Jing Wang. Hierarchical timescales in the neocortex: Mathematical mechanism and biological insights. Proceedings of the National Academy of Sciences, 119(6):e2110274119, 2022

    2022

  6. [14]

    A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex

    Rishidev Chaudhuri, Kenneth Knoblauch, Marie-Alice Gariel, Henry Kennedy, and Xiao-Jing Wang. A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex. Neuron, 88(2):419–431, 2015. 18

    2015

  7. [15]

    Synergetics: An Introduction to Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology

    Hermann Haken. Synergetics: An Introduction to Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology . Springer, 1977

    1977

  8. [16]

    Behavior of information flow near criticality

    Matthijs Meijers, Sosuke Ito, and Pieter Rein ten Wolde. Behavior of information flow near criticality. Physical Review E , 103(1):L010102, 2021

    2021

  9. [17]

    How critical is brain criticality? Trends in Neurosciences, 45(11):820–837, 2022

    Jordan O’Byrne and Karim Jerbi. How critical is brain criticality? Trends in Neurosciences, 45(11):820–837, 2022

    2022

  10. [18]

    Colloquium: Criticality and dynamical scaling in living systems

    Miguel A Munoz. Colloquium: Criticality and dynamical scaling in living systems. Reviews of Modern Physics , 90(3):031001, 2018

    2018

  11. [19]

    Gollo, Andrew Zalesky, and Michael Breakspear

    Luca Cocchi, Leonardo L. Gollo, Andrew Zalesky, and Michael Breakspear. Criticality in the brain: A synthesis of neurobiology, models and cognition. Progress in Neurobiology, 158:132–152, 2017

    2017

  12. [20]

    Andreas Spiegler, Enrique C. A. Hansen, Christophe Bernard, Anthony R. McIntosh, and Viktor K. Jirsa. Selective activation of resting-state networks following focal stimulation in a connectome-based network model of the human brain. eNeuro, 3(5):ENEURO.0068–16.2016, 2016

    2016

  13. [21]

    Breyton, J

    M. Breyton, J. Fousek, G. Rabuffo, P. Sorrentino, L. Kusch, M. Massimini, S. Petkoski, and V. Jirsa. Spatiotemporal brain complexity quantifies consciousness outside of perturbation paradigms. eLife, page 2023.04.18.537321, 2024

    2023

  14. [22]

    The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging

    Mario Lavanga, Johanna Stumme, Bahar Hazal Yalcinkaya, Jan Fousek, Christiane Jockwitz, Hiba Sheheitli, Nora Bittner, Meysam Hashemi, Spase Petkoski, Svenja Caspers, and Viktor Jirsa. The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in heal...

    2023

  15. [23]

    Imitating and exploring the human brain’s resting and task-performing states via brain computing: scaling and architecture

    Wenlian Lu, Longbin Zeng, Jiexiang Wang, Shitong Xiang, Yang Qi, Qibao Zheng, Ningsheng Xu, and Jianfeng Feng. Imitating and exploring the human brain’s resting and task-performing states via brain computing: scaling and architecture. National Science Review , 11(5):nwae080, 03 2024

    2024

  16. [24]

    Simulation and assimilation of the digital human brain

    Wenlian Lu, Xin Du, Jiexiang Wang, Longbin Zeng, Leijun Ye, Shitong Xiang, Qibao Zheng, Jie Zhang, Ningsheng Xu, Jianfeng Feng, Yubin Bao, Boyu Chen, Siming Chen, Zhongyu Chen, Fei Dai, Weiyang Ding, Xin Du, Jianfeng Feng, and the DTB Consortium. Simulation and assimilation of...

    2024

  17. [25]

    An extended Human Connectome Project multimodal parcellation atlas of the human cortex and subcortical areas

    Chu-Chung Huang, Edmund T Rolls, Jianfeng Feng, and Ching-Po Lin. An extended Human Connectome Project multimodal parcellation atlas of the human cortex and subcortical areas. Brain Structure and Function , 227(3):763–778, 2022

    2022

  18. [26]

    Clustering in globally coupled inhibitory neurons

    David Golomb and John Rinzel. Clustering in globally coupled inhibitory neurons. Physica D, 72(3):259–282, 1994

    1994

  19. [27]

    Hierarchical connectome modes and critical state jointly maximize human brain functional diversity

    Rong Wang, Pan Lin, Mianxin Liu, Ying Wu, Tao Zhou, and Changsong Zhou. Hierarchical connectome modes and critical state jointly maximize human brain functional diversity. Physical Review Letters, 123(3):038301, 2019

    2019

  20. [28]

    Neuronal cascades shape whole-brain functional dynamics at rest

    Giovanni Rabuffo, Jan Fousek, Christophe Bernard, and Viktor Jirsa. Neuronal cascades shape whole-brain functional dynamics at rest. eNeuro, 8(5), 2021. 19

    2021

  21. [29]

    A hierarchy of intrinsic timescales across primate cortex

    John D Murray, Alberto Bernacchia, David J Freedman, Ranulfo Romo, Jonathan D Wallis, Xinying Cai, Camillo Padoa-Schioppa, Tatiana Pasternak, Hyojung Seo, Daeyeol Lee, and Xiao-Jing Wang. A hierarchy of intrinsic timescales across primate cortex. Nature Neuroscience, 17(12):16...

    2014

  22. [30]

    The ventral visual pathway: an expanded neural framework for the processing of object quality

    Dwight J Kravitz, Kadharbatcha S Saleem, Chris I Baker, Leslie G Ungerleider, and Mortimer Mishkin. The ventral visual pathway: an expanded neural framework for the processing of object quality. Trends in Cognitive Sciences, 17(1):26–49, 2013

    2013

  23. [31]

    A new neural framework for visuospatial processing

    Dwight J Kravitz, Kadharbatcha S Saleem, Chris I Baker, and Mortimer Mishkin. A new neural framework for visuospatial processing. Nature Reviews Neuroscience, 12(4):217–230, 2011

    2011

  24. [32]

    Pang, Kevin M

    James C. Pang, Kevin M. Aquino, Marianne Oldehinkel, Peter A. Robinson, Ben D. Fulcher, Michael Breakspear, and Alex Fornito. Geometric constraints on human brain function. Nature, 618(7965):566–574, 2023

    2023

  25. [33]

    N. T. Markov, M. M. Ercsey-Ravasz, A. R. Ribeiro Gomes, C. Lamy, L. Magrou, J. Vezoli, P. Misery, A. Falchier, R. Quilodran, M. A. Gariel, J. Sallet, R. Gamanut, C. Huissoud, S. Clavagnier, P. Giroud, D. Sappey-Marinier, P. Barone, C. Dehay, Z. Toroczkai, K. Knoblauch, D. C. V...

    2014

  26. [34]

    Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics

    Karl J Friston, Andrea Mechelli, Robert Turner, and Cathy J Price. Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. NeuroImage, 12(4):466–477, 2000

    2000

  27. [35]

    Dynamic models of large-scale brain activity

    Michael Breakspear. Dynamic models of large-scale brain activity. Nature Neuroscience, 20(3):340–352, 2017

    2017

  28. [36]

    Koller, Michael Schirner, and Petra Ritter

    Dominik P. Koller, Michael Schirner, and Petra Ritter. Human connectome topology directs cortical traveling waves and shapes frequency gradients. Nature Communications, 15(1):3570, 2024. mnn dtb

    2024

  29. [37]

    Hierarchical organization of the human ventral visual streams revealed with magnetoencephalography

    Edmund T Rolls, Gustavo Deco, Yi Zhang, and Jianfeng Feng. Hierarchical organization of the human ventral visual streams revealed with magnetoencephalography. Cerebral Cortex, 33(20):10686–10701, 2023

    2023

  30. [38]

    Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons

    Nicolas Brunel. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of Computational Neuroscience , 8(3):183–208, 2000

    2000

  31. [39]

    A master equation formalism for macroscopic modeling of asynchronous irregular activity states

    Sami El Boustani and Alain Destexhe. A master equation formalism for macroscopic modeling of asynchronous irregular activity states. Neural Computation, 21(1):46–100, 2009

    2009

  32. [40]

    Dynamics of moment neuronal networks

    Jianfeng Feng, Yingchun Deng, and Enrico Rossoni. Dynamics of moment neuronal networks. Physical Review E , 73(4):041906, 2006

    2006

  33. [41]

    Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons

    Yann Zerlaut, Sandrine Chemla, Frederic Chavane, and Alain Destexhe. Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons. Journal of Computational Neuroscience , 44(1):45–61, 2018

    2018

  34. [42]

    R. M. Capocelli and L. M. Ricciardi. Diffusion approximation and first passage time problem for a model neuron. Kybernetik, 8(6):214–223, 1971. 20

    1971

  35. [43]

    Moment neural network and an efficient numerical method for modeling irregular spiking activity

    Yang Qi. Moment neural network and an efficient numerical method for modeling irregular spiking activity. Physical Review E , 110:024310, 2024

    2024

  36. [44]

    Becker and Marco A

    Marcelo P. Becker and Marco A. P. Idiart. Mean-field method for generic conductance-based integrate-and-fire neurons with finite timescales. Physical Review E, 109(2):024406, 2024

    2024

  37. [45]

    On a Gaussian neuronal field model

    Wenlian Lu, Enrico Rossoni, and Jianfeng Feng. On a Gaussian neuronal field model. Neuroimage, 52(3):913–933, 2010

    2010

  38. [46]

    Intrinsic timescales as an organizational principle of neural processing across the whole rhesus macaque brain

    Ana MG Manea, Anna Zilverstand, Kamil Ugurbil, Sarah R Heilbronner, and Jan Zimmermann. Intrinsic timescales as an organizational principle of neural processing across the whole rhesus macaque brain. eLife, 11:e75540, 2022

    2022

  39. [47]

    Atypical intrinsic neural timescale in autism

    Takamitsu Watanabe, Geraint Rees, and Naoki Masuda. Atypical intrinsic neural timescale in autism. eLife, 8:e42256, 2019

    2019

  40. [48]

    Hearne, and Michael W

    Takuya Ito, Luke J. Hearne, and Michael W. Cole. A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales. NeuroImage, 221:117141, 2020

    2020

  41. [49]

    Invariant timescale hierarchy across the cortical somatosensory network

    Rom´ an Rossi-Pool, Antonio Zainos, Manuel Alvarez, Sergio Parra, Jer´ onimo Zizumbo, and Ranulfo Romo. Invariant timescale hierarchy across the cortical somatosensory network. Proceedings of the National Academy of Sciences , 118(3):e2021843118, 2021

    2021

  42. [50]

    Temporal hierarchy of intrinsic neural timescales converges with spatial core-periphery organization

    Mehrshad Golesorkhi, Javier Gomez-Pilar, Shankar Tumati, Maia Fraser, and Georg Northoff. Temporal hierarchy of intrinsic neural timescales converges with spatial core-periphery organization. Communications Biology, 4(1):277, 2021

    2021

  43. [51]

    Peterson, Paroma Varma, Priyadarshini Sebastian, Richard Gao, Torben Noto, Antonio H

    Thomas Donoghue, Matar Haller, Erik J. Peterson, Paroma Varma, Priyadarshini Sebastian, Richard Gao, Torben Noto, Antonio H. Lara, Joni D. Wallis, Robert T. Knight, Avgusta Shestyuk, and Bradley Voytek. Parameterizing neural power spectra into periodic and aperiodic components...

    2020

  44. [52]

    Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture

    Richard Gao, Ruud L van den Brink, Thomas Pfeffer, and Bradley Voytek. Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife, 9:e61277, 2020

    2020

  45. [53]

    Nikouline, J

    Klaus Linkenkaer-Hansen, Vadim V. Nikouline, J. Matias Palva, and Risto J. Ilmoniemi. Long-range temporal correlations and scaling behavior in human brain oscillations. The Journal of Neuroscience , 21(4):1370–1377, 2001

    2001

  46. [54]

    Matias Palva, Alexander Zhigalov, Jonni Hirvonen, Onerva Korhonen, Klaus Linkenkaer-Hansen, and Satu Palva

    J. Matias Palva, Alexander Zhigalov, Jonni Hirvonen, Onerva Korhonen, Klaus Linkenkaer-Hansen, and Satu Palva. Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws. Proceedings of the National Academy of Sciences , 110(9)...

    2013

  47. [55]

    Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

    Gorka Zamora-L´ opez, Yuhan Chen, Gustavo Deco, Morten L Kringelbach, and Changsong Zhou. Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs. Scientific Reports, 6(1):38424, 2016. 21 Supplementary Info...

    2016

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.