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Competitive interactions shape mammalian brain network dynamics and computation.

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2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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  1. [1] § Methods › Computational memory capacity from reservoir computing ↔ conn2res/tasks.py, lines 331–469 · score 0.64 · memory capacity task, uniformly distributed, absolute, trained, reproduce, delayed
  2. [2] § Methods › Updating the generative connectivity ↔ matlab/analysis_utils/hopf_config_utils.m, lines 7–118 · score 0.60 · optimization iteration, L1 regularization, GEC, optional

Paper

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The authors' code

Python · 492 lines · 14 KB · BSD-3-Clause · 1 match

  1. # -*- coding: utf-8 -*-
  2. """
  3. Functionality for fetching task datasets
  4. """
  5. from abc import ABCMeta, abstractmethod
  6. import numpy as np
  7. import neurogym as ngym
  8. from reservoirpy import datasets
  11. NEUROGYM_TASKS = [
  12. 'AntiReach',
  13. # 'Bandit', # *
  14. 'ContextDecisionMaking',
  15. # 'DawTwoStep', # *
  16. 'DelayComparison',
  17. 'DelayMatchCategory',
  18. 'DelayMatchSample',
  19. 'DelayMatchSampleDistractor1D',
  20. 'DelayPairedAssociation',
  21. # 'Detection', # *
  22. 'DualDelayMatchSample',
  23. # 'EconomicDecisionMaking', # *
  24. 'GoNogo',
  25. 'HierarchicalReasoning',
  26. 'IntervalDiscrimination',
  27. 'MotorTiming',
  28. 'MultiSensoryIntegration',
  29. # 'Null',
  30. 'OneTwoThreeGo',
  31. 'PerceptualDecisionMaking',
  32. 'PerceptualDecisionMakingDelayResponse',
  33. 'PostDecisionWager',
  34. 'ProbabilisticReasoning',
  35. 'PulseDecisionMaking',
  36. 'Reaching1D',
  37. 'Reaching1DWithSelfDistraction',
  38. 'ReachingDelayResponse',
  39. 'ReadySetGo',
  40. 'SingleContextDecisionMaking',
  41. 'SpatialSuppressMotion',
  42. # 'ToneDetection' # *
  43. ]
  45. RESERVOIRPY_TASKS = [
  46. 'henon_map',
  47. 'logistic_map',
  48. 'lorenz',
  49. 'mackey_glass',
  50. 'multiscroll',
  51. 'doublescroll',
  52. 'rabinovich_fabrikant',
  53. 'narma',
  54. 'lorenz96',
  55. 'rossler'
  56. ]
  58. CONN2RES_TASKS = [
  59. 'MemoryCapacity'
  60. ]
  63. class Task(metaclass=ABCMeta):
  64. """
  65. Class for generating task datasets
  67. Parameters
  68. ----------
  69. name : str
  70. name of the task
  71. n_trials : int, optional
  72. number of trials if task indicated by 'name' is a
  73. a trial-based task, by default 10
  74. """
  76. def __init__(self, name, n_trials=10):
  77. """
  78. Constructor method for class Task
  79. """
  80. self.name = name
  81. self.n_trials = n_trials
  82. self.n_targets = None
  83. self.n_features = None
  85. @property
  86. @abstractmethod
  87. def name(self):
  88. pass
  90. @name.setter
  91. @abstractmethod
  92. def name(self, name):
  93. pass
  95. @abstractmethod
  96. def fetch_data(self, n_trials=None, **kwargs):
  97. pass
  100. class NeuroGymTask(Task):
  101. """
  102. Class for generating task datasets from the
  103. Neurogym repository
  105. Parameters
  106. ----------
  107. name : str
  108. name of the task
  109. n_trials : int, optional
  110. number of trials if task indicated by 'name' is a
  111. a trial-based task, by default 10
  112. """
  114. def __init__(self, *args, **kwargs):
  115. """
  116. Constructor method for class NeuroGymTask
  117. """
  118. super().__init__(*args, **kwargs)
  119. self.timing = None
  121. @property
  122. def name(self):
  123. return self._name
  125. @name.setter
  126. def name(self, name):
  127. if name not in NEUROGYM_TASKS:
  128. raise ValueError("Task not included in NeuroGym tasks")
  130. self._name = name
  132. def fetch_data(self, n_trials=None, input_gain=None, add_bias=False,
  133. **kwargs):
  134. """
  135. Fetch data for Neurogym tasks
  137. Parameters
  138. ----------
  139. n_trials : int, optional
  140. number of trials to be generated, by default None
  141. input_gain : float, optional
  142. gain on the input signal, i.e., scalar multiplier, by default None
  143. add_bias : bool, optional
  144. decides whether bias is added to the input signal or not,
  145. by default False
  147. Returns
  148. -------
  149. x, y : numpy.ndarray, list
  150. input (x) and output (y) training data
  151. """
  152. if n_trials is not None:
  153. self.n_trials = n_trials
  155. # create a Dataset object from NeuroGym
  156. dataset = ngym.Dataset(self._name + '-v0', kwargs)
  158. # get environment object
  159. env = dataset.env
  161. # generate per trial dataset
  162. _ = env.reset()
  164. x, y = [], []
  165. for _ in range(self.n_trials):
  166. env.new_trial()
  167. ob, gt = env.ob, env.gt
  169. # reshape data if needed
  170. if ob.ndim == 1:
  171. ob = ob[:, np.newaxis]
  172. if gt.ndim == 1:
  173. gt = gt[:, np.newaxis]
  175. # scale input data
  176. if input_gain is not None:
  177. ob *= input_gain
  179. # add bias to input data if needed
  180. if add_bias:
  181. ob = np.hstack((np.ones((n_trials, 1)), ob))
  183. # store input and output
  184. x.append(ob)
  185. y.append(gt)
  187. # set attributes
  188. if x[0].squeeze().ndim == 1:
  189. self.n_features = 1
  190. elif x[0].squeeze().ndim == 2:
  191. self.n_features = x[0].shape[1]
  193. if y[0].squeeze().ndim == 1:
  194. self.n_targets = 1
  195. elif y[0].squeeze().ndim == 2:
  196. self.n_targets = y[0].shape[1]
  198. self.timing = env.timing
  199. # self._data = {'x': x, 'y': y}
  201. return x, y
  204. class ReservoirPyTask(Task):
  205. """
  206. Class for generating task datasets from the
  207. ReservoirPy repository
  209. Parameters
  210. ----------
  211. name : str
  212. name of the task
  213. n_trials : int, optional
  214. number of trials if task indicated by 'name' is a
  215. a trial-based task, by default 10
  216. """
  218. def __init__(self, *args, **kwargs):
  219. """
  220. Constructor method for class NeuroGymTask
  221. """
  222. super().__init__(*args, **kwargs)
  223. self.horizon = None
  225. @property
  226. def name(self):
  227. return self._name
  229. @name.setter
  230. def name(self, name):
  231. if name not in RESERVOIRPY_TASKS:
  232. raise ValueError("Task not included in ReservoirPy tasks")
  234. self._name = name
  236. def fetch_data(self, n_trials=None, horizon=1, win=30,
  237. input_gain=None, add_bias=False, **kwargs):
  238. """
  239. Fetch data for ReservoirPy tasks, which are defined as single-
  240. or multi-output tasks using chaotic time series as input signal
  241. and preceded or delayed output signal(s) with respect to the input
  243. Parameters
  244. ----------
  245. n_trials : int, optional
  246. number of time steps in input and output, by default None
  247. horizon : int, numpy.ndarray or list, optional
  248. shift between input and output, i.e., positive number for
  249. prediction and negative number for memory task, by default 1
  250. note that array/list is used for multi-output task
  251. win : int, optional
  252. initial window of the input signal to be used for generating the
  253. delayed output signal in case of memory tasks, by default 30
  254. note that no values in horizon should exceed this window (in
  255. absolute value), otherwise ValueError is thrown
  256. input_gain : float, optional
  257. gain on the input signal, i.e., scalar multiplier, by default None
  258. add_bias : bool, optional
  259. decides whether bias is added to the input signal or not,
  260. by default False
  262. Returns
  263. -------
  264. x, y : numpy.ndarray, list
  265. input (x) and output (y) training data
  267. Raises
  268. ------
  269. ValueError
  270. if horizon has elements with different sign
  271. ValueError
  272. if any horizon exceeds win (in absolute value)
  273. """
  274. if n_trials is not None:
  275. self.n_trials = n_trials
  277. # make sure horizon is a list. Exclude 0.
  278. if isinstance(horizon, (int, np.integer)):
  279. horizon = [horizon]
  281. # check that horizon has elements with same sign
  282. if np.unique(np.sign(horizon)).size != 1:
  283. raise ValueError("Horizon sohuld have elements with same sign")
  285. # calculate absolute maximum horizon
  286. abs_horizon_max = np.max(np.abs(horizon))
  287. if win < abs_horizon_max:
  288. raise ValueError("Absolute maximum horizon should be within window")
  290. # generate input data
  291. env = getattr(datasets, self._name)
  292. x = env(n_timesteps=self.n_trials + win + abs_horizon_max + 1, **kwargs)
  294. # output data
  295. y = np.hstack([x[win + h : -abs_horizon_max + h - 1] for h in horizon])
  297. # update input data
  298. x = x[win : -abs_horizon_max - 1]
  300. # reshape data if needed
  301. if x.ndim == 1:
  302. x = x[:, np.newaxis]
  303. if y.ndim == 1:
  304. y = y[:, np.newaxis]
  306. # scale input data
  307. if input_gain is not None:
  308. x *= input_gain
  310. # add bias to input data if needed
  311. if add_bias:
  312. x = np.hstack((np.ones((n_trials, 1)), x))
  314. # set attributes
  315. if x.squeeze().ndim == 1:
  316. self.n_features = 1
  317. elif x.squeeze().ndim == 2:
  318. self.n_features = x.shape[1]
  320. if y.squeeze().ndim == 1:
  321. self.n_targets = 1
  322. elif y.squeeze().ndim == 2:
  323. self.n_targets = y.shape[1]
  325. self.horizon = horizon
  326. # self._data = {'x': x, 'y': y}
  328. return x, y
  331. class Conn2ResTask(Task):
  332. """
  333. Class for generating datasets for MemoryCapacity task
  335. Parameters
  336. ----------
  337. name : str
  338. name of the task
  339. n_trials : int, optional
  340. number of trials if task indicated by 'name' is a
  341. a trial-based task, by default 10
  342. """
  344. def __init__(self, *args, **kwargs):
  345. """
  346. Constructor method for class Conn2ResTask
  347. """
  348. super().__init__(*args, **kwargs)
  349. self.horizon_max = None
  351. @property
  352. def name(self):
  353. return self._name
  355. @name.setter
  356. def name(self, name):
  357. if name not in CONN2RES_TASKS:
  358. raise ValueError("Task not included in conn2res tasks")
  360. self._name = name
  362. def fetch_data(self, n_trials=None, horizon_max=-20, win=30,
  363. low=-1, high=1, input_gain=None, add_bias=False,
  364. seed=None):
  365. """
  366. Fetch data for MemoryCapacity, which is defined as a multi-output
  367. task using a uniformly distributed input signal and multiple
  368. delayed output signals
  370. Parameters
  371. ----------
  372. n_trials : int, optional
  373. number of time steps in input and output, by default None
  374. horizon_max : int, optional
  375. maximum shift between input and output, i.e., negative number
  376. for memory capacity task, by default -20
  377. note that an array of horizons are generated from -1 to
  378. inclusive of horizon_max using a step of -1, which
  379. defines memory capacity task as a multi-output task, i.e., one
  380. task per horizon
  381. win : int, optional
  382. initial window of the input signal to be used for generating the
  383. delayed output signal, by default 30
  384. note that horizon_max should exceed this window (in
  385. absolute value), otherwise ValueError is thrown
  386. low : float, optional
  387. lower boundary of the output interval of numpy.uniform(),
  388. by default -1
  389. high : float, optional
  390. upper boundary of the output interval of numpy.uniform(),
  391. by default 1
  392. input_gain : float, optional
  393. gain on the input signal, i.e., scalar multiplier, by default None
  394. add_bias : bool, optional
  395. decides whether bias is added to the input signal or not,
  396. by default False
  397. seed : int, array_like[ints], SeedSequence, BitGenerator, Generator, optional
  398. seed to initialize the random number generator, by default None
  399. for details, see numpy.random.default_rng()
  401. Returns
  402. -------
  403. x, y : numpy.ndarray, list
  404. input (x) and output (y) training data
  406. Raises
  407. ------
  408. ValueError
  409. if maximum horizon exceeds win (in absolute value)
  410. """
  411. if n_trials is not None:
  412. self.n_trials = n_trials
  414. # generate horizon as a list inclusive of horizon_max
  415. sign_ = np.sign(horizon_max)
  416. horizon = np.arange(
  417. sign_,
  418. sign_ + horizon_max,
  419. sign_,
  420. )
  422. # calculate absolute maximum horizon
  423. abs_horizon_max = np.abs(horizon_max)
  424. if win < abs_horizon_max:
  425. raise ValueError("Absolute maximum horizon should be within window")
  427. # use random number generator for reproducibility
  428. rng = np.random.default_rng(seed=seed)
  430. # generate input data
  431. x = rng.uniform(low=low, high=high, size=(self.n_trials + win + abs_horizon_max + 1))[
  432. :, np.newaxis
  433. ]
  435. # output data
  436. y = np.hstack([x[win + h : -abs_horizon_max + h - 1] for h in horizon])
  438. # update input data
  439. x = x[win : -abs_horizon_max - 1]
  441. # reshape data if needed
  442. if x.ndim == 1:
  443. x = x[:, np.newaxis]
  444. if y.ndim == 1:
  445. y = y[:, np.newaxis]
  447. # scale input data
  448. if input_gain is not None:
  449. x *= input_gain
  451. # add bias to input data if needed
  452. if add_bias:
  453. x = np.hstack((np.ones((n_trials, 1)), x))
  455. # set attributes
  456. if x.squeeze().ndim == 1:
  457. self.n_features = 1
  458. elif x.squeeze().ndim == 2:
  459. self.n_features = x.shape[1]
  461. if y.squeeze().ndim == 1:
  462. self.n_targets = 1
  463. elif y.squeeze().ndim == 2:
  464. self.n_targets = y.shape[1]
  466. self.horizon_max = horizon_max
  467. # self._data = {'x': x, 'y': y}
  469. return x, y
  472. def get_task_list(repository):
  473. """
  474. Returns list of tasks in repository
  476. Parameters
  477. ----------
  478. repository : str
  479. _description_
  481. Returns
  482. -------
  483. _type_
  484. _description_
  485. """
  486. repository = repository.lower()
  487. if repository == 'neurogym':
  488. return NEUROGYM_TASKS
  489. elif repository == 'reservoirpy':
  490. return RESERVOIRPY_TASKS
  491. elif repository == 'conn2res':
  492. return CONN2RES_TASKS

tasks.py at commit 3ccb707, under BSD-3-Clause · at the source

Overview

Authors: Andrea I. Luppi1,2,3,4,5,6,7, Yonatan Sanz Perl2,6,7,8, Jakub Vohryzek2,6,7,8, Hana Ali1,2,6,7, Pedro A. M. Mediano2,6,7,9, Fernando E. Rosas2,6,7,10,11, Filip Milisav5, Laura E. Suárez5, Silvia Gini12,13, Daniel Gutierrez-Barragan12, Yohan Yee14, Seán Froudist-Walsh15, Alessandro Gozzi12, Bratislav Misic5, Gustavo Deco2,6,7,8,16, Morten L. Kringelbach1,2,6,7,17
ORCID iDs: Andrea I. Luppi, Yonatan Sanz Perl, Jakub Vohryzek, Pedro A. M. Mediano, Fernando E. Rosas, Filip Milisav, Laura E. Suárez, Silvia Gini, Yohan Yee, Seán Froudist-Walsh, Alessandro Gozzi, Bratislav Misic, Gustavo Deco, Morten L. Kringelbach
17 affiliations
  1. Department of Psychiatry and Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford,Oxford, UK
  2. International Centre for Flourishing, Universities of Oxford,Oxford, UK
  3. St. John’s College, University of Cambridge,Cambridge, UK
  4. Division of Information Engineering, University of Cambridge,Cambridge, UK
  5. Montreal Neurological Institute, McGill University,Montreal, Quebec Canada
  6. International Centre for Flourishing, Universities of Aarhus,Aarhus, Denmark
  7. International Centre for Flourishing, Universities Pompeu Fabra,Barcelona, Spain
  8. Centre for Brain and Cognition, Pompeu Fabra University,Barcelona, Spain
  9. Department of Computing, Imperial College London,London, UK
  10. Department of Informatics, Sussex AI, and Sussex Centre for Consciousness Science, University of Sussex,Brighton, UK
  11. Principles of Intelligent Behavior in Biological and Social Systems, Prague, Czech Republic
  12. Centre for Neuroscience and Cognitive Systems, Italian Institute of Technology,Rovereto, Italy
  13. Centre for Mind/Brain Sciences, University of Trento,Trento, Italy
  14. Department of Radiology, University of Calgary,Calgary, Alberta Canada
  15. Bristol Computational Neuroscience Unit, Bristol University,Bristol, UK
  16. Catalan Institution for Research and Advanced Studies (ICREA),Barcelona, Spain
  17. Centre for Music in the Brain, Aarhus University,Aarhus, Denmark
Institutions: University of Oxford (United Kingdom); University of Cambridge (United Kingdom); McGill University (Canada); Aarhus University (Denmark); Universitat Pompeu Fabra (Spain); Imperial College London (United Kingdom); University of Sussex (United Kingdom); Italian Institute of Technology (Italy); University of Trento (Italy); University of Calgary (Canada); University of Bristol (United Kingdom); Institució Catalana de Recerca i Estudis Avançats (Spain)
Journal: Nature neuroscience, volume 29, issue 4, pages 915-933
Dates: received 1 November 2024; accepted 6 January 2026; published online 11 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02205-3 · PMID 41814091 · PMCID PMC13061630 · OpenAlex W7134889684
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism), mouse (organism), non-human primate (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Preprocessing, Graphs, Machine learning
Keywords: Biophysical models, Dynamical systems, Network models
MeSH: Brain*, Models, Neurological*, Nerve Net*, Animals, Computer Simulation, Connectome, Humans, Macaca, Mice, Neural Pathways (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Wellcome Trust (Wellcome) (226924/Z/23/Z); EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) (896354, 101017716, 802371, 101071900); Fonds de Recherche du Québec - Nature et Technologies (Quebec Fund for Research in Nature and Technology) (UNIQUE Neuro-AI Excellence Scholarship); Simons Foundation (400101); Fondazione Telethon (Telethon Foundation) (GGP19177); Canada Research Chairs (CRC); Carlsbergfondet (Carlsberg Foundation); Jane Bradley Pettit Foundation; Danmarks Grundforskningsfond (Danish National Research Foundation) (DNRF117); RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) (BB/X013243/1)
Citations: cited by 3 papers (Europe PMC); 155 references in the paper

Abstract

How does brain network architecture balance cooperation and competition between distributed circuits? Here we use computational whole-brain modeling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque and mouse, we show that to faithfully reproduce brain activity, model architecture consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. Across species, competitive interactions preferentially link regions characterized by opposite profiles of cytoarchitecture, gene expression and receptor expression. The model with competitive interactions provides superior subject specificity, consistently outperforming the cooperative-only model and exhibiting excellent fit to the spatiotemporal properties of the living brain. These properties were not explicitly optimized, instead emerging spontaneously. Competitive interactions in the generative connectivity produce more synergistic and hierarchical dynamics, leading to enhanced performance for neuromorphic computing. Altogether, this work provides a generative link among network architecture, dynamical properties and computational performance in the mammalian brain.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Imperial-MIND-lab/integrated-info-decomp

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netneurolab/conn2res

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Hana-Ali/competitive-cooperative-hopf

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sites.google.com/site/bctnet

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neurospin/pypreclin

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jlizier/jidt

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Evidence: files inventoried
Commit: 1ca832235e64ae5b3a482dc0ead6f9b2ff2a0456, 3 August 2026
Languages: Java (318), MATLAB (147), Jupyter (41), Python (25), Shell (18), R (6), C/C++ (5), C (5), CUDA (4), Julia (4), C++ (2)
Size: 799 files, 575 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file, tests, 60 notebooks
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: NumPy (48 files), Matplotlib (44 files), Statistics and Machine Learning Toolbox (5 files), SciPy (5 files), pandas (4 files), PyTorch (3 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
576 files
At the source: github.com/jlizier/jidt

Code availability

MATLAB and C++ code for the Hopf model with cooperative and competitive interactions is provided at https://github.com/Hana-Ali/competitive-cooperative-hopf.git.

MATLAB/Octave and Python code (version 1.0) to compute measures of integrated information decomposition of time series with the Gaussian MMI solver is available at https://github.com/Imperial-MIND-lab/integrated-info-decomp. The conn2res Python toolbox for reservoir computing (version 1.0.0) is available at https://github.com/netneurolab/conn2res. The Brain Connectivity Toolbox code (version 2019-03-03) used for graph-theoretical analyses and network randomization is freely available online (https://sites.google.com/site/bctnet/). The Python processing for PreClinical data pipeline, Pypreclin version 1.0.1, is freely available at https://github.com/neurospin/pypreclin. The FMRIB Software Library (FSL) is freely available online (http://www.fmrib.ox.ac.uk/fsl/; version accessed on 4 February 2018). CONN toolbox version 17f is freely available at http://www.nitrc.org/projects/conn/. DSI Studio is freely available at https://dsi-studio.labsolver.org/. Java Information Dynamics Toolbox version 1.5 is freely available online (https://github.com/jlizier/jidt). The latest version of the BigBrainWarp toolbox is freely available online (https://bigbrainwarp.readthedocs.io). The abagen toolbox (version 0.1.4) for processing of the AHBA human transcriptomic dataset is available at https://abagen.readthedocs.io/. The neuromaps toolbox (version 0.0.5) is available at https://netneurolab.github.io/neuromaps/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 6 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 760 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The Human Connectome Project functional and structural datasets are freely available from http://www.humanconnectome.org/. Macaque fMRI data are available from the PRIMatE Data Exchange (PRIME-DE) through the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC; http://fcon_1000.projects.nitrc.org/indi/indiPRIME.html). The macaque connectome is available on Zenodo at 10.5281/zenodo.1471588. The CoCoMac database on which it is based is also available online at http://cocomac.g-node.org/main/index.php?. Mouse functional and structural connectome data are available from author A.G. NeuroSynth is available at https://neurosynth.org/. The original macaque cortical gene expression and cell type density data from ref. 151 are available at https://macaque.digital-brain.cn/spatial-omics. The dataset is provided by the Brain Science Data Center, Chinese Academy of Sciences (https://braindatacenter.cn/). The original macaque receptor density data from autoradiography are available from https://balsa.wustl.edu/study/P2Nql and https://search.kg.ebrains.eu/instances/de62abc1-7252-4774-9965-5040f5e8fb6b97. The original map of macaque intracortical myelination from T1w/T2w ratio from ref. 97 is available at https://balsa.wustl.edu/study/P2Nql. Mouse cell type data are available as described in ref. 152. Human gene expression data153 are available from the Allen Human Brain Atlas at http://human.brain-map.org/static/download. Mouse gene expression data154 are available at https://mouse.brain-map.org/. Human cell type data are available as described in ref. 155. Human receptor density data are available online from the neuromaps toolbox. Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 3 keywords, 10 MeSH terms, 10 funders, 150 references.

Cite

This paper

Luppi, A. I., Sanz Perl, Y., Vohryzek, J., Ali, H., Mediano, P. A. M., Rosas, F. E., Milisav, F., Suárez, L. E., Gini, S., Gutierrez-Barragan, D., Yee, Y., Froudist-Walsh, S., Gozzi, A., Misic, B., Deco, G., & Kringelbach, M. L. (2026). Competitive interactions shape mammalian brain network dynamics and computation. Nature neuroscience, 29(4), 915-933. https://doi.org/10.1038/s41593-026-02205-3

BibTeX

@article{luppi2026competitive,
author = {Luppi, Andrea I. and Sanz Perl, Yonatan and Vohryzek, Jakub and Ali, Hana and Mediano, Pedro A. M. and Rosas, Fernando E. and Milisav, Filip and Suárez, Laura E. and Gini, Silvia and Gutierrez-Barragan, Daniel and Yee, Yohan and Froudist-Walsh, Seán and Gozzi, Alessandro and Misic, Bratislav and Deco, Gustavo and Kringelbach, Morten L.},
title = {{Competitive interactions shape mammalian brain network dynamics and computation}},
journal = {Nature neuroscience},
year = {2026},
month = mar,
volume = {29},
number = {4},
pages = {915--933},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02205-3},
url = {https://doi.org/10.1038/s41593-026-02205-3},
pmid = {41814091},
pmcid = {PMC13061630}
}

RIS

TY - JOUR
AU - Luppi, Andrea I.
AU - Sanz Perl, Yonatan
AU - Vohryzek, Jakub
AU - Ali, Hana
AU - Mediano, Pedro A. M.
AU - Rosas, Fernando E.
AU - Milisav, Filip
AU - Suárez, Laura E.
AU - Gini, Silvia
AU - Gutierrez-Barragan, Daniel
AU - Yee, Yohan
AU - Froudist-Walsh, Seán
AU - Gozzi, Alessandro
AU - Misic, Bratislav
AU - Deco, Gustavo
AU - Kringelbach, Morten L.
TI - Competitive interactions shape mammalian brain network dynamics and computation
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/03/11
VL - 29
IS - 4
SP - 915
EP - 933
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02205-3
UR - https://doi.org/10.1038/s41593-026-02205-3
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41593-026-02205-3",
"type": "article-journal",
"title": "Competitive interactions shape mammalian brain network dynamics and computation",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Luppi",
"given": "Andrea I."
},
{
"family": "Sanz Perl",
"given": "Yonatan"
},
{
"family": "Vohryzek",
"given": "Jakub"
},
{
"family": "Ali",
"given": "Hana"
},
{
"family": "Mediano",
"given": "Pedro A. M."
},
{
"family": "Rosas",
"given": "Fernando E."
},
{
"family": "Milisav",
"given": "Filip"
},
{
"family": "Suárez",
"given": "Laura E."
},
{
"family": "Gini",
"given": "Silvia"
},
{
"family": "Gutierrez-Barragan",
"given": "Daniel"
},
{
"family": "Yee",
"given": "Yohan"
},
{
"family": "Froudist-Walsh",
"given": "Seán"
},
{
"family": "Gozzi",
"given": "Alessandro"
},
{
"family": "Misic",
"given": "Bratislav"
},
{
"family": "Deco",
"given": "Gustavo"
},
{
"family": "Kringelbach",
"given": "Morten L."
}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "4",
"page": "915-933",
"DOI": "10.1038/s41593-026-02205-3",
"PMID": "41814091",
"PMCID": "PMC13061630",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02205-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
11
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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