OSCR

Learning induces activation-mechanism-dependent neural plasticity in an intracortical microstimulation task.

Code ↔ Paper

28 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 28 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › Spike sorting during stimulation ↔ processing/preprocessing/icms_pipeline.py, lines 18–127 · score 0.87 · bandpass filter, Cubic interpolation, preprocessing pipeline, polynomial, subtract, Hz
  2. [2] § MATERIALS AND METHODS › Unit tracking via electrophysiological recording ↔ processing/batch_process/postprocessing/run_unitmatch.py, lines 175–281 · score 0.81 · naive Bayes, UnitMatch, connected component, concatenated, cross, position
  3. [3] § MATERIALS AND METHODS › Population coupling ↔ processing/pop_coupling/pop_coupling_control.py, lines 43–114 · score 0.77 · zero lag, Population coupling, population rate, Gaussian, shuffle, stPR
  4. [4] § RESULTS › Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations ↔ matlab/fig3/fig3_stats.m, lines 115–158 · score 0.76 · Kruskal Wallis, Tukey Kramer post, recruited neurons, spike onset, Fmax, hoc
  5. [5] § MATERIALS AND METHODS › Population coupling ↔ processing/pop_coupling/pop_coupling_v2.py, lines 73–149 · score 0.73 · zero lag, population rate, circular, Gaussian, shuffle, stPR
  6. [6] § MATERIALS AND METHODS › Unit tracking via electrophysiological recording ↔ python/fig_s8/generate_figure.py, lines 269–374 · score 0.71 · tracked pairs, matching unit, waveform distance, nearby, ratio, cross
  7. [7] § RESULTS › Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations ↔ matlab/fig3/fig3_stats.m, lines 115–158 · score 0.69 · Kruskal Wallis, Tukey Kramer post, recruited neurons, spike onset, Fmax, hoc
  8. [8] § RESULTS › Differential behavioral correlates of PL and NPL neurons ↔ python/fig6/hit_miss_histograms.py, lines 51–162 · score 0.69 · KL divergence, hit miss, FDR BH, pulse locking, NPL, correlates
  9. [9] § MATERIALS AND METHODS › Detection and quantification of ICMS-evoked Ca2+ activation ↔ processing/imaging/volumetric/NeuroAnalysis_BehavioralParametricSweepBlockExtractTest.m, lines 911–951 · score 0.66 · ImageJ, iterative thresholding, FIJI, variance, MATLAB, segmented
  10. [10] § RESULTS › Millisecond pulse-locking dynamics differentiate learning-induced plasticity ↔ processing/imaging/movement_control/export_recruited_traces.m, lines 1–127 · score 0.64 · Wheel movement, activation threshold, tracked neurons, recruitment, frames, PL
  11. [11] § RESULTS › Differential behavioral correlates of PL and NPL neurons ↔ python/fig6/hit_miss_histograms.py, lines 51–162 · score 0.62 · KL divergence, Hit Miss, FDR BH, IQR, NPL, correlates
  12. [12] § RESULTS › Differential behavioral correlates of PL and NPL neurons ↔ python/fig6/stats_table.py, lines 48–111 · score 0.62 · KL divergence, FDR BH, pulse locking, Mann Whitney, NPL, correlates
  13. [13] § RESULTS › Millisecond pulse-locking dynamics differentiate learning-induced plasticity ↔ python/fig5/generate_figure5.py, lines 97–153 · score 0.61 · Pyramidal cells, pulse locking, PL units, Fisher, interneurons, NPL
  14. [14] § MATERIALS AND METHODS › Quantifying neuromodulation metrics via electrophysiology ↔ processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, lines 138–193 · score 0.59 · pulse locked, spike probability, PLIs, Blanking, binned, metrics
  15. [15] § RESULTS › Differential behavioral correlates of PL and NPL neurons ↔ processing/pop_coupling/pop_coupling_control.py, lines 43–114 · score 0.58 · spike triggered population, population coupling, stPR, window, neuron
  16. [16] § RESULTS › Millisecond pulse-locking dynamics differentiate learning-induced plasticity ↔ python/fig5/generate_figure5.py, lines 97–153 · score 0.58 · pyramidal cell, pulse locked, PL unit, Fisher, NS, Filled
  17. [17] § RESULTS › Differential behavioral correlates of PL and NPL neurons ↔ processing/pop_coupling/pop_coupling_v2.py, lines 20–70 · score 0.57 · stPR, population coupling, population rate, window, neuron, spike
  18. [18] § MATERIALS AND METHODS › Analysis of behavioral data ↔ python/fig_s1/generate_figure.py, lines 202–249 · score 0.56 · psychometric curves, stimulation amplitude, block, python, thresholds, channel
  19. [19] § RESULTS › Millisecond pulse-locking dynamics differentiate learning-induced plasticity ↔ processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, lines 138–193 · score 0.56 · phase locking, pulse locked, spike probability, ms
  20. [20] § RESULTS › Millisecond pulse-locking dynamics differentiate learning-induced plasticity ↔ processing/imaging/movement_control/export_subset_traces.m, the whole file · a weak match · score 0.55 · Wheel movement, increasing subset, frames, recruitment, tracked, amplitude
  21. [21] § RESULTS › Millisecond pulse-locking dynamics differentiate learning-induced plasticity ↔ python/fig_s4/generate_figure.py, lines 45–155 · score 0.54 · Latency jitter, pulse locking, monotonically, fits, segment, distance
  22. [22] § MATERIALS AND METHODS › Spike sorting during stimulation ↔ processing/control/stage1_sort.py, lines 48–94 · score 0.53 · SpikeInterface, spike sorted, Mountainsort5, curation, preprocessed
  23. [23] § RESULTS › Differential behavioral correlates of PL and NPL neurons ↔ python/fig6/stats_table.py, lines 48–111 · score 0.52 · KL divergence, FDR BH, Mann Whitney, NPL, correlates, median
  24. [24] § MATERIALS AND METHODS › Spike sorting during stimulation ↔ processing/preprocessing/custom_preprocessors.py, lines 142–212 · score 0.52 · custom preprocessing, polynomial, subtract, artifacts, blanked, fits
  25. [25] § MATERIALS AND METHODS › Spike sorting during stimulation ↔ processing/control/stage2_curate.py, lines 355–404 · score 0.52 · waveform curation, spike sorted, accuracy, preprocessed
  26. [26] § RESULTS › Millisecond pulse-locking dynamics differentiate learning-induced plasticity ↔ python/fig_s8/generate_figure.py, lines 530–580 · score 0.51 · Waveform distances, tracked unit, S8, position, modulation, animals
  27. [27] § RESULTS › Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations ↔ matlab/fig3/fig3jlmn_subset_analysis.m, lines 179–239 · score 0.51 · Kruskal Wallis, spike onset, Fmax, 2–4, recruited, Figure 3
  28. [28] § RESULTS › Millisecond pulse-locking dynamics differentiate learning-induced plasticity ↔ python/fig4/probe_diagram.py, lines 25–102 · score 0.50 · probe diagram, contact sites, firing rate, circles, Filled, bars

Paper

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The authors' code

Python · 335 lines · 12 KB · no license · 2 matches

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It can be read at the source: processing/pop_coupling/pop_coupling_control.py.

Overview

Authors: Robin Kim1,2, Roy Lycke1,2, Pavlo Zolotavin1,2, Jon Montes2,3, Chong Xie1,2, Lan Luan1,2
ORCID iDs: Robin Kim, Pavlo Zolotavin, Jon Montes, Chong Xie, Lan Luan
  1. Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005 USA
  2. Rice Neuroengineering Initiative, Rice University, Houston, TX 77005 USA
  3. Department of Bioengineering, Rice University, Houston, TX 77005 USA
Institutions: Rice University (United States)
Journal: Science advances, volume 12, issue 36, article eaef0343
Dates: received 26 December 2025; accepted 20 July 2026; published online 4 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.aef0343 · PMID 42696589 · PMCID PMC13544205 · OpenAlex W7208724621
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Learning*, Neuronal Plasticity*, Neurons*, Animals, Electric Stimulation, Male (* major topic)
Journal subjects: Neuroscience, Engineering
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Eye Institute (R01EY036094); National Institute of Neurological Disorders and Stroke (U01NS115588, U01NS131086, R01NS102917)
Citations: not cited yet (Europe PMC); 72 references in the paper

Abstract

Electrical microstimulation provides high-resolution control of neural circuits for causal studies and restoration of impaired functions, yet how responses to artificial activation evolve with learning remains unclear. Here, we deploy a detection task and pair ultraflexible electrodes for stable intracortical microstimulation (ICMS) with longitudinal imaging and recordings to track single-cell and population responses across weeks of learning. Detection thresholds decreased with learning, indicating plasticity. Chronic imaging showed that stimulus-evoked recruitment expanded at a fixed current, while a consistent number of neurons continued to underlie behavioral responses. A subset of learning-sensitive cells enhanced modulation and reduced latency. Electrophysiological recordings further distinguished two forms of adaptation: Directly activated, pulse-locked neurons strengthened their excitability, whereas polysynaptically recruited neurons expanded in number and were predictive of behavioral outcomes. These results show that learning in an ICMS task reshapes cortical circuits through activation-mechanism–dependent plasticity, underscoring the need for stimulation paradigms that adapt to both cell-intrinsic and network dynamics.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 28 matches between paragraphs and lines of code.

XieLuanLab/icms-activation-plasticity

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: d1922d16913b50bf6338ff4e0c2953cb8ef43d15, 15 July 2026
Languages: Python (96), MATLAB (34)
Size: 135 files, 130 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, environment (requirements.txt, processing/requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (73 files), Matplotlib (53 files), pandas (39 files), SpikeInterface (32 files), SciPy (31 files), seaborn (21 files), Statistics and Machine Learning Toolbox (19 files), statsmodels (9 files), Image Processing Toolbox (7 files), scikit-learn (4 files), Signal Processing Toolbox (2 files), UMAP (2 files), Neo (1 file), Pillow (1 file), psignifit (1 file), XGBoost (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
131 files, not copied: shown from their source

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At the source: github.com/XieLuanLab/icms-activation-plasticity

Zenodo 21384216

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (73 files), Matplotlib (53 files), pandas (39 files), SpikeInterface (32 files), SciPy (31 files), seaborn (21 files), Statistics and Machine Learning Toolbox (19 files), statsmodels (9 files), Image Processing Toolbox (7 files), scikit-learn (4 files), Signal Processing Toolbox (2 files), UMAP (2 files), Neo (1 file), Pillow (1 file), psignifit (1 file), XGBoost (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
131 files
At the source: zenodo.org/records/21384216

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 260 scripts, each with its path and the digest of its content;
  • 28 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, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. Processed electrophysiology, behavioral, and 2P imaging data are available from the DANDI Archive at https://dandiarchive.org/dandiset/001868/0.260715.2016 (DOI: 10.48324/dandi.001868/0.260715.2016 (http://dx.doi.org/10.48324/dandi.001868/0.260715.2016)). Figure source data used to regenerate the figures are available from Zenodo at https://zenodo.org/records/21382755 (DOI: 10.5281/zenodo.21382755 (http://dx.doi.org/10.5281/zenodo.21382755)). Custom code for analysis and figure reproduction is available on GitHub at https://github.com/XieLuanLab/icms-activation-plasticity and archived on Zenodo at https://zenodo.org/records/21384216 (DOI: 10.5281/zenodo.21384216 (http://dx.doi.org/10.5281/zenodo.21384216)). This study did not generate new materials.

Reproduced under the paper's license (CC BY-NC), 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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 6 MeSH terms, 2 funders, 69 references.

Cite

This paper

Kim, R., Lycke, R., Zolotavin, P., Montes, J., Xie, C., & Luan, L. (2026). Learning induces activation-mechanism-dependent neural plasticity in an intracortical microstimulation task. Science advances, 12(36), eaef0343. https://doi.org/10.1126/sciadv.aef0343

BibTeX

@article{kim2026learning,
author = {Kim, Robin and Lycke, Roy and Zolotavin, Pavlo and Montes, Jon and Xie, Chong and Luan, Lan},
title = {{Learning induces activation-mechanism-dependent neural plasticity in an intracortical microstimulation task}},
journal = {Science advances},
year = {2026},
month = sep,
volume = {12},
number = {36},
pages = {eaef0343},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aef0343},
url = {https://doi.org/10.1126/sciadv.aef0343},
pmid = {42696589},
pmcid = {PMC13544205}
}

RIS

TY - JOUR
AU - Kim, Robin
AU - Lycke, Roy
AU - Zolotavin, Pavlo
AU - Montes, Jon
AU - Xie, Chong
AU - Luan, Lan
TI - Learning induces activation-mechanism-dependent neural plasticity in an intracortical microstimulation task
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/09/04
VL - 12
IS - 36
SP - eaef0343
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aef0343
UR - https://doi.org/10.1126/sciadv.aef0343
LA - en
ER -

CSL-JSON

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