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Distinct roles of neuronal phenotypes during neurofeedback adaptation.

Code ↔ Paper

3 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.

The 3 matches
  1. [1] § Methods › Tunning curves ↔ functions/neural_fxns/tuning_curve_fxns.py, lines 23–32 · score 0.58 · modulation depth, tunning curve, firing rate, fitted
  2. [2] § Methods › Tunning curves ↔ processing/tuning/main_tuning_curve.py, lines 304–363 · score 0.55 · modulation depth, tunning curve, fitted
  3. [3] § Methods › Tunning curves ↔ functions/neural_fxns/tuning_curve_fxns.py, lines 23–32 · score 0.54 · cosine function, tuning curve, fit, neural

Paper

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The authors' code

Python · 96 lines · 1.9 KB · BSD-3-Clause · 2 matches

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Thu Jan 23 16:30:42 2025
  5. @author: hanna
  6. """
  8. import numpy as np
  9. from scipy.optimize import curve_fit
  11. def degChange(PD1, PD2):
  12. radA = PD1 * (np.pi/180)
  13. radB = PD2 * (np.pi/180)
  15. a = np.array( (np.cos(radA),np.sin(radA)) )
  16. b = np.array( (np.cos(radB),np.sin(radB)) )
  18. delta = np.arccos(np.matmul(a,b)) * (180/np.pi)
  20. return(delta)
  23. def cosine_model(theta, b1, PD, b0):
  24. """
  25. Cosine function for firing rate fitting.
  26. theta: angles (in radians)
  27. b1: modulation (M)
  28. Modulation Depth (MD): b1/b0
  29. PD: phase shift (preferred direction)
  30. b0: baseline firing rate
  31. """
  32. return b0 + b1 * np.cos(theta - PD)
  35. def fit_cosine_model(theta_rad, m):
  37. """
  38. constraints:
  39. b1 (modulation): 0 to +inf
  40. PD (preferred direction): -2pi to +2pi
  41. b0 (mean firing rate): 0 to +inf
  42. """
  44. 'initial guesses'
  45. #modulation
  46. p0_b1 = (np.max(m) - np.min(m))/2
  47. #preferred direction
  48. p0_PD = theta_rad[np.where(m == np.max(m))[0][0]]
  49. #mean firing rate
  50. p0_b0 = np.mean(m)
  51. p0 = [p0_b1, p0_PD, p0_b0]
  53. 'fit model'
  54. params, pcov = curve_fit(cosine_model, theta_rad, m, p0, bounds=([0,-2*np.pi,0],[np.inf,2*np.pi,np.inf]))
  55. M, PD, meanFR = params
  56. y_est = cosine_model(theta_rad, *params)
  58. 'return parameters'
  59. return(M, M/meanFR, PD, meanFR, y_est)
  66. def compute_signed_degChange(PD1, PD2):
  68. #TODO: need to update for signed changes....?
  70. # if PD1 < 0:
  71. # PD1+=360
  73. # if PD2 < 0:
  74. # PD2+=360
  76. delta1 = PD1 - PD2
  78. if delta1 < 0:
  79. delta2 = delta1+360
  80. else:
  81. delta2 = delta1
  83. if delta2 == 0:
  84. sign_ = 0 #'NO DIFF'
  85. elif delta2 == 180.0:
  86. sign_ = 'DIIF EXACTLY 180'
  87. elif delta2 < 180:
  88. sign_ = -1 #cw
  89. elif delta2 > 180:
  90. sign_ = 1 #'ccw'
  92. return(sign_, degChange(PD1, PD2))

tuning_curve_fxns.py at commit 76d745b, under BSD-3-Clause · at the source

Overview

Authors: Yi Zhao1, Hannah M. Stealey1, Hung-Yun Lu1, Enrique Contreras-Hernandez1, Yin-Jui Chang1, Philippe N. Tobler2,3, Samantha R. Santacruz1,4,5
ORCID iDs: Yi Zhao, Hannah M. Stealey, Hung-Yun Lu, Samantha R. Santacruz
  1. Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, United States of America
  2. Department of Economics, University of Zurich, Zurich, Switzerland
  3. Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
  4. Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, Texas, United States of America
  5. Interdisciplinary Neuroscience Program, University of Texas at Austin, Austin, Texas, United States of America
Institutions: The University of Texas at Austin (United States); University of Zurich (Switzerland); ETH Zurich (Switzerland)
Journal: PloS one, volume 21, issue 7, article e0351053
Dates: received 2 June 2025; accepted 21 May 2026; published online 10 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0351053 · PMID 42430410 · PMCID PMC13354095 · OpenAlex W4410116391
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.05.02.651867
Status: code verified
Categories: non-human primate (organism)
Methods: Machine learning, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions, Spectral & time-frequency
MeSH: Adaptation, Physiological*, Neurofeedback*, Neurons*, Animals, Brain-Computer Interfaces, Learning, Macaca mulatta, Male, Phenotype (* major topic)
Journal subjects: Biology and Life Sciences, Cell Biology, Cellular Types, Animal Cells, Neurons, Neuroscience, Cellular Neuroscience, Computational Biology, Computational Neuroscience, Single Neuron Function, Motor Neurons, Organisms, Eukaryota, Animals, Vertebrates, Amniotes, Mammals, Primates, Monkeys, Zoology, Neuronal Tuning, Anatomy, Nervous System, Motor System, Medicine and Health Sciences, Cognitive Science, Cognitive Psychology, Learning, Psychology, Social Sciences, Learning and Memory, Physiology, Electrophysiology, Membrane Potential, Action Potentials, Neurophysiology
Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Whitehall Foundation (Whitehall Foundation, Inc.) (2022-12-071); NSF (2145412)
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Learning adaptation allows the brain to refine motor patterns in response to changing environments rapidly. While population-level neural dynamics and single-neuron activity in motor learning have been widely studied, the contributions of individual neuron types remain poorly understood. Here, we employed a brain-machine interface (BMI) task with perturbations of varying difficulty to investigate single-neuron dynamics underlying neurofeedback adaptation in two rhesus macaques. Cortical neurons were classified based on waveform shape into narrow waveform (NW) and broad waveform (BW) categories, representing putative inhibitory interneurons and excitatory pyramidal neurons, respectively. Compared to BW neurons, NW neurons were more active and more strongly involved in the learning process. Moreover, task difficulty modulated neural responsiveness and coordination within both neuron groups, highlighting differential neuron engagement during neurofeedback adaptation. Our findings provide novel insights into single-neuron mechanisms underlying neurofeedback adaptation and emphasize the distinct functional roles of neuronal phenotypes in rapid learning processes.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

hstealey/bci_analysis

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 76d745b7344818592b5e9d70096545b231099d79, 8 May 2025
Languages: Python (29)
Size: 72 files, 29 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (28 files), pandas (23 files), SciPy (19 files), Matplotlib (17 files), seaborn (17 files), statsmodels (7 files), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
31 files
At the source: github.com/hstealey/bci_analysis/DEMO_data

The paper's code and data availability statement is in the Data section.

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;
  • 29 scripts, each with its path and the digest of its content;
  • 3 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

No dataset and no data link were found in the paper.

Data Availability

Raw data is available by request from the corresponding author, and processed data from representative sessions is available on GitHub (https://github.com/hstealey/bci_analysis/DEMO_data). The complete dataset will be published in a figshare repository after the review process.

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 2, 28 September 2026

  • Authors: added Hung-Yun Lu (0000-0002-8951-773X); removed Hung-Yun Lu

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 9 MeSH terms, 2 funders, 71 references.

Cite

This paper

Zhao, Y., Stealey, H. M., Lu, H.-Y., Contreras-Hernandez, E., Chang, Y.-J., Tobler, P. N., & Santacruz, S. R. (2026). Distinct roles of neuronal phenotypes during neurofeedback adaptation. PloS one, 21(7), e0351053. https://doi.org/10.1371/journal.pone.0351053

BibTeX

@article{zhao2026distinct,
author = {Zhao, Yi and Stealey, Hannah M. and Lu, Hung-Yun and Contreras-Hernandez, Enrique and Chang, Yin-Jui and Tobler, Philippe N. and Santacruz, Samantha R.},
title = {{Distinct roles of neuronal phenotypes during neurofeedback adaptation}},
journal = {PloS one},
year = {2026},
month = jul,
volume = {21},
number = {7},
pages = {e0351053},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0351053},
url = {https://doi.org/10.1371/journal.pone.0351053},
pmid = {42430410},
pmcid = {PMC13354095}
}

RIS

TY - JOUR
AU - Zhao, Yi
AU - Stealey, Hannah M.
AU - Lu, Hung-Yun
AU - Contreras-Hernandez, Enrique
AU - Chang, Yin-Jui
AU - Tobler, Philippe N.
AU - Santacruz, Samantha R.
TI - Distinct roles of neuronal phenotypes during neurofeedback adaptation
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/07/10
VL - 21
IS - 7
SP - e0351053
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0351053
UR - https://doi.org/10.1371/journal.pone.0351053
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Distinct roles of neuronal phenotypes during neurofeedback adaptation",
"container-title": "PloS one",
"author": [
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"family": "Zhao",
"given": "Yi"
},
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"family": "Stealey",
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{
"family": "Lu",
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{
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],
"container-title-short": "PLoS One",
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"language": "en",
"issued": {
"date-parts": [
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10
]
]
}
}

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