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Alpha phase coding supports feature binding during working memory maintenance.

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Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 35 lines · 793 B · no license

  1. from scipy.stats import circmean,circvar
  2. from cmath import phase
  3. from numpy import array
  4. from scipy.stats import circmean,circvar,circstd
  5. from numpy import *
  6. from cmath import phase
  7. from matplotlib.pylab import *
  10. def len2(x):
  11. if type(x) is not type([]):
  12. if type(x) is not type(array([])):
  13. return -1
  14. return len(x)
  16. def phase2(x):
  17. if not isnan(x):
  18. return phase(x)
  19. return nan
  21. def circdist(angles1,angles2):
  22. if len2(angles2) < 0:
  23. if len2(angles1) > 0:
  24. angles2 = [angles2]*len(angles1)
  25. else:
  26. angles2 = [angles2]
  27. angles1 = [angles1]
  28. if len2(angles1) < 0:
  29. angles1 = [angles1]*len(angles2)
  30. return amap(lambda a1,a2: phase2(exp(1j*a1)/exp(1j*a2)), angles1,angles2)
  32. def circdist_2pi(angles1,angles2):
  33. dist = circdist(angles1,angles2)
  34. dist[dist<0]+=2*pi
  35. return dist

circ_stats.py at commit bb9227a, no license · at the source

Overview

Authors: Mattia F Pagnotta1, Aniol Santo-Angles2, Ainsley Temudo2,3, Joao Barbosa4,5, Albert Compte4, Mark D’Esposito1,6, Kartik K Sreenivasan2,7
ORCID iDs: Mattia F Pagnotta, Joao Barbosa, Albert Compte, Mark D’Esposito, Kartik K Sreenivasan
  1. Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA USA
  2. Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
  3. University of Utah, Salt Lake City, UT USA
  4. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
  5. Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, École Normale Supérieure - PSL Research University, Paris, France
  6. Department of Psychology, University of California Berkeley, Berkeley, CA USA
  7. Center for Brain and Health, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
Institutions: University of California, Berkeley (United States); New York University Abu Dhabi (United Arab Emirates); University of Utah (United States); Inserm (France); Université Paris Sciences et Lettres (France); École Normale Supérieure - PSL (France); Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (Spain); Laboratoire de Neurosciences Cognitives et Computationnelles
Journal: Communications biology, volume 9, issue 1, article 922
Dates: received 30 March 2025; accepted 7 April 2026; published online 4 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42003-026-10071-9 · PMID 42082670 · PMCID PMC13346530 · OpenAlex W4391116397
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.01.21.576561
Status: code verified
Categories: MEG (modality), human (organism), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Spectral & time-frequency, Source localization, Physiology & signal measures, Connectivity
Keywords: Cognitive neuroscience, Human behaviour
MeSH: Alpha Rhythm*, Memory, Short-Term*, Humans, Magnetoencephalography, Male (* major topic)
Topic: Advanced Memory and Neural Computing (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Funding: Fundação Bial (ref: 356/19); Swiss National Science Foundation (195083, 214404, P500PB_214404, P2FRP3_195083); Foundation for the National Institutes of Health (R01MH063901)
Citations: not cited yet (Europe PMC); 117 references in the paper

Abstract

The ability to successfully retain and manipulate information in working memory (WM) requires that objects’ individual features are bound into cohesive representations; yet, the mechanisms supporting feature binding remain unclear. Binding (or swap) errors, where memorized features are erroneously associated with the wrong object, can provide a window into the intrinsic limits in capacity of WM that represent a key bottleneck in our cognitive ability. We tested the hypothesis that binding in WM is accomplished via neural phase synchrony and that swap errors result from perturbations in this synchrony. Using magnetoencephalography data collected from human subjects in a task designed to induce swap errors, we showed that swaps are characterized by reduced phase-locked oscillatory activity during memory retention, as predicted by an attractor model of spiking neural networks. Further, we found that this reduction arises from increased phase coding variability in the alpha-band over a distributed network of sensorimotor areas. Our findings demonstrate that feature binding in WM is accomplished through phase coding dynamics that emerge from the competition between different memories.

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

Repository

Its files are read in the Code ↔ Paper reader above.

comptelab/binding

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: bb9227aec9c080da9063ce632965a46043cd0807, 10 June 2021
Languages: Python (14)
Size: 15 files, 14 scripts
Software Heritage: archived
Found in: the text, “Computational model”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (10 files), NumPy (10 files), SciPy (10 files), scikit-learn (6 files), Brian 2 (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
15 files
At the source: github.com/comptelab/binding

Code availability

Analysis scripts are available on the Open Science Framework116. The computational model can be obtained from https://github.com/comptelab/binding. Supplemental data can be requested via email to the lead author.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 14 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

Behavioral data and preprocessed MEG data are available on the Open Science Framework (https://osf.io/s5mcy/; https://osf.io/37hb6/)116,117.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 5 MeSH terms, 3 funders, 108 references.

Cite

This paper

Pagnotta, M. F., Santo-Angles, A., Temudo, A., Barbosa, J., Compte, A., D’Esposito, M., & Sreenivasan, K. K. (2026). Alpha phase coding supports feature binding during working memory maintenance. Communications biology, 9(1), 922. https://doi.org/10.1038/s42003-026-10071-9

BibTeX

@article{pagnotta2026alpha,
author = {Pagnotta, Mattia F and Santo-Angles, Aniol and Temudo, Ainsley and Barbosa, Joao and Compte, Albert and D’Esposito, Mark and Sreenivasan, Kartik K},
title = {{Alpha phase coding supports feature binding during working memory maintenance}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {922},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10071-9},
url = {https://doi.org/10.1038/s42003-026-10071-9},
pmid = {42082670},
pmcid = {PMC13346530}
}

RIS

TY - JOUR
AU - Pagnotta, Mattia F
AU - Santo-Angles, Aniol
AU - Temudo, Ainsley
AU - Barbosa, Joao
AU - Compte, Albert
AU - D’Esposito, Mark
AU - Sreenivasan, Kartik K
TI - Alpha phase coding supports feature binding during working memory maintenance
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/04
VL - 9
IS - 1
SP - 922
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10071-9
UR - https://doi.org/10.1038/s42003-026-10071-9
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Alpha phase coding supports feature binding during working memory maintenance",
"container-title": "Communications biology",
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"given": "Mattia F"
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"container-title-short": "Commun Biol",
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"issue": "1",
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"PMCID": "PMC13346530",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
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"language": "en",
"issued": {
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}
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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