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

  1. """
  2. Population coupling analysis for control animals.
  4. Adapted from pop_coupling_v2.py for experimental animals.
  5. Computes STPR (spike-triggered population rate) and shuffle-normalized
  6. population coupling (pc_norm) — matching the experimental pipeline output.
  8. Uses session_responses.pkl for PL/NPL classification and
  9. condensed_trials.csv for trial windows (to segment spikes).
  11. Output: pop_coupling/{animal}_pop_coupling_control.pkl
  12. """
  14. import sys
  15. import os
  16. import ast
  17. import traceback
  18. from pathlib import Path
  19. from collections import OrderedDict
  21. # Project root for imports (needed to unpickle session_responses.pkl)
  22. PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
  23. if PROJECT_ROOT not in sys.path:
  24. sys.path.insert(0, PROJECT_ROOT)
  26. import dill as pickle
  28. import numpy as np
  29. import pandas as pd
  30. from scipy.ndimage import gaussian_filter1d
  33. # ─── Config ───────────────────────────────────────────────────────────
  34. CONTROL_ROOT = Path("E:/ICMS plasticity control")
  35. ANIMAL_IDS = ["ICMS43", "ICMS45", "ICMS48", "ICMS54", "ICMS56"]
  36. FS = 30000.0
  37. OUT_DIR = Path("pop_coupling")
  38. MIN_SPIKES = 50 # minimum spikes for valid STPR
  41. # ─── STPR + PC computation (from pop_coupling_v2.py) ─────────────────
  43. def compute_stpr_and_pc(pop_raster, neuron_id, bin_size=0.001, win=0.10,
  44. gauss_sigma=0.01, n_shuffles=200, rng=None):
  45. """
  46. Spike-triggered population rate and population coupling.
  48. Returns: lags (s), stpr (array), pc (float), n_spikes_used (int), pc_norm (float or None)
  49. pc_norm is pc normalized by median(|pc_shuffled|) if n_shuffles>0.
  50. """
  51. T = max((spikes.max() if len(spikes) else 0.0)
  52. for spikes in pop_raster.values())
  53. if T <= 0:
  54. return None, None, np.nan, 0, None
  56. edges = np.arange(0.0, T + bin_size, bin_size)
  57. nbins = edges.size - 1
  59. # Population rate (exclude target neuron)
  60. pop_counts = np.zeros(nbins, dtype=float)
  61. for j, spikes in pop_raster.items():
  62. if j == neuron_id or len(spikes) == 0:
  63. continue
  64. pop_counts += np.histogram(spikes, bins=edges)[0]
  65. pop_rate = pop_counts / bin_size
  66. sigma_bins = max(1e-9, gauss_sigma / bin_size)
  67. pop_rate_smooth = gaussian_filter1d(pop_rate, sigma=sigma_bins, mode='nearest')
  69. # Mean-center
  70. pop_mc = pop_rate_smooth - np.mean(pop_rate_smooth)
  72. # Spike-triggered average around neuron i's spikes
  73. spikes_i = np.asarray(pop_raster.get(neuron_id, []), float)
  74. win_bins = int(round(win / bin_size))
  75. lags = np.arange(-win_bins, win_bins + 1) * bin_size
  77. if spikes_i.size == 0:
  78. return lags, np.full(lags.size, np.nan), np.nan, 0, None
  80. spike_bins = np.floor(spikes_i / bin_size).astype(int)
  82. windows = []
  83. for sb in spike_bins:
  84. a, b = sb - win_bins, sb + win_bins
  85. if a < 0 or b >= nbins:
  86. continue
  87. windows.append(pop_mc[a:b + 1])
  89. if len(windows) == 0:
  90. return lags, np.full(lags.size, np.nan), np.nan, 0, None
  92. stpr = np.vstack(windows).mean(axis=0)
  93. pc = float(stpr[win_bins]) # value at zero lag
  94. n_used = len(windows)
  96. # Shuffle normalization
  97. pc_norm = None
  98. if n_shuffles and n_used > 0:
  99. if rng is None:
  100. rng = np.random.default_rng()
  101. shuf_vals = []
  102. for _ in range(int(n_shuffles)):
  103. shift = rng.integers(low=win_bins + 1, high=nbins - win_bins - 1)
  104. pop_mc_sh = np.roll(pop_mc, shift)
  105. vals = [pop_mc_sh[sb - win_bins: sb + win_bins + 1][win_bins]
  106. for sb in spike_bins
  107. if (sb - win_bins) >= 0 and (sb + win_bins) < nbins]
  108. if len(vals):
  109. shuf_vals.append(np.mean(vals))
  110. if len(shuf_vals):
  111. den = np.median(np.abs(shuf_vals))
  112. pc_norm = np.nan if den <= 1e-12 else float(pc / den)
  114. return lags, stpr, pc, n_used, pc_norm
  117. # ─── Helper: concatenate spike segments rebased ──────────────────────
  119. def concat_segments_rebased(spike_train, segments):
  120. """Concatenate spikes from segments, rebasing each to start at 0."""
  121. out = []
  122. offset = 0.0
  123. for a, b in segments:
  124. seg = spike_train[(spike_train >= a) & (spike_train < b)]
  125. if seg.size:
  126. out.append((seg - a) + offset)
  127. offset += (b - a)
  128. return np.concatenate(out) if out else np.array([], dtype=float)
  131. # ─── Control-specific helpers ─────────────────────────────────────────
  133. def discover_sessions(animal_dir):
  134. """Find spike_sorting dirs with session_responses.pkl + condensed_trials.csv."""
  135. sessions = []
  136. for spike_dir in sorted(animal_dir.rglob("spike_sorting")):
  137. sr_path = spike_dir / "session_responses.pkl"
  138. csv_path = spike_dir / "condensed_trials.csv"
  139. analyzer_path = spike_dir / "analyzer_final.zarr"
  140. if sr_path.exists() and csv_path.exists() and analyzer_path.exists():
  141. sessions.append(spike_dir)
  142. return sessions
  145. def get_session_date(spike_sorting_dir):
  146. """Extract session date string from path."""
  147. exp_dir = spike_sorting_dir.parent
  148. return exp_dir.parent.name
  151. def parse_stim_timestamps(ts_str):
  152. """Parse stim_timestamps string from CSV."""
  153. if isinstance(ts_str, str):
  154. try:
  155. return ast.literal_eval(ts_str)
  156. except (ValueError, SyntaxError):
  157. return []
  158. if isinstance(ts_str, (list, np.ndarray)):
  159. return list(ts_str)
  160. return []
  163. def get_trial_windows_per_condition(csv_path):
  164. """
  165. Build trial windows from condensed_trials.csv.
  167. Returns: dict[(current, channel)] -> list of (start_s, end_s) tuples
  168. """
  169. df = pd.read_csv(csv_path)
  170. condition_windows = {}
  172. for (cur, ch), sub_df in df.groupby(['current_uA', 'channel']):
  173. segments = []
  174. for _, row in sub_df.iterrows():
  175. ts = parse_stim_timestamps(row['stim_timestamps'])
  176. if not ts or len(ts) == 0:
  177. continue
  178. segments.append((ts[0], ts[-1]))
  180. if segments:
  181. condition_windows[(int(cur), int(ch))] = segments
  183. return condition_windows
  186. def get_pl_npl_unit_ids(session_responses, channel, current):
  187. """Get PL and NPL unit IDs for a given (channel, current)."""
  188. pl_ids = []
  189. npl_ids = []
  191. for uid, ur in session_responses.unit_responses.items():
  192. key = (channel, current)
  193. if key not in ur.stim_responses:
  194. continue
  195. scr = ur.stim_responses[key]
  196. if scr.pulse_response.is_pulse_locked:
  197. pl_ids.append(int(uid))
  198. else:
  199. npl_ids.append(int(uid))
  201. return pl_ids, npl_ids
  204. def get_pop_coupling_session(spike_sorting_dir):
  205. """
  206. Compute STPR-based population coupling for PL vs NPL units.
  208. Matches experimental pop_coupling_v2.py output format:
  209. condition_results[(cur, ch)] = {
  210. "pl_stpr_dict": {unit_id: stpr_array},
  211. "pl_pc_dict": {unit_id: pc_hz},
  212. "pl_pc_norm_dict": {unit_id: pc_normalized},
  213. "npl_stpr_dict": {unit_id: stpr_array},
  214. "npl_pc_dict": {unit_id: pc_hz},
  215. "npl_pc_norm_dict": {unit_id: pc_normalized},
  216. }
  217. """
  218. from spikeinterface import full as si
  220. # Load session responses for PL/NPL labels
  221. sr_path = spike_sorting_dir / "session_responses.pkl"
  222. with open(sr_path, 'rb') as f:
  223. session_responses = pickle.load(f)
  225. # Load spike trains from analyzer
  226. analyzer_path = spike_sorting_dir / "analyzer_final.zarr"
  227. analyzer = si.load_sorting_analyzer(str(analyzer_path))
  228. unit_spike_train_dict = {
  229. int(uid): analyzer.sorting.get_unit_spike_train(uid)
  230. for uid in analyzer.unit_ids
  231. }
  233. # Get trial windows per condition from CSV
  234. csv_path = spike_sorting_dir / "condensed_trials.csv"
  235. condition_windows = get_trial_windows_per_condition(csv_path)
  237. condition_results = {}
  239. for (cur, ch), segments in condition_windows.items():
  240. if len(segments) < 3:
  241. continue
  243. # Get PL/NPL unit IDs for this condition
  244. pl_unit_ids, npl_unit_ids = get_pl_npl_unit_ids(session_responses, ch, cur)
  245. mod_ids = sorted(set(pl_unit_ids + npl_unit_ids))
  247. if len(mod_ids) < 2:
  248. continue
  250. # Build pop_raster: concatenate stim segments rebased
  251. segments_s = [(float(a), float(b)) for a, b in segments]
  252. pop_raster = {}
  253. for uid in mod_ids:
  254. if uid not in unit_spike_train_dict:
  255. continue
  256. st = np.asarray(unit_spike_train_dict[uid]).astype(float) / FS
  257. pop_raster[uid] = concat_segments_rebased(st, segments_s)
  259. if len(pop_raster) < 2:
  260. continue
  262. pl_stpr_dict, pl_pc_dict, pl_pc_norm_dict = {}, {}, {}
  263. npl_stpr_dict, npl_pc_dict, npl_pc_norm_dict = {}, {}, {}
  265. rng = np.random.default_rng(0)
  266. for uid in pop_raster:
  267. lags, stpr, pc, n_used, pc_norm = compute_stpr_and_pc(
  268. pop_raster, uid, win=0.1, n_shuffles=200, rng=rng
  269. )
  271. if n_used < MIN_SPIKES:
  272. stpr = None
  273. pc = np.nan
  274. pc_norm = np.nan
  276. if uid in pl_unit_ids:
  277. pl_stpr_dict[uid] = stpr
  278. pl_pc_dict[uid] = pc
  279. pl_pc_norm_dict[uid] = pc_norm
  280. elif uid in npl_unit_ids:
  281. npl_stpr_dict[uid] = stpr
  282. npl_pc_dict[uid] = pc
  283. npl_pc_norm_dict[uid] = pc_norm
  285. condition_results[(cur, ch)] = {
  286. "pl_stpr_dict": pl_stpr_dict,
  287. "pl_pc_dict": pl_pc_dict,
  288. "pl_pc_norm_dict": pl_pc_norm_dict,
  289. "npl_stpr_dict": npl_stpr_dict,
  290. "npl_pc_dict": npl_pc_dict,
  291. "npl_pc_norm_dict": npl_pc_norm_dict,
  292. }
  294. return condition_results
  297. # ─── Main ─────────────────────────────────────────────────────────────
  299. if __name__ == "__main__":
  300. OUT_DIR.mkdir(exist_ok=True)
  302. for animal_id in ANIMAL_IDS:
  303. print(f"\nProcessing {animal_id}...")
  304. animal_dir = CONTROL_ROOT / animal_id
  305. sessions = discover_sessions(animal_dir)
  307. if not sessions:
  308. print(f" No sessions found")
  309. continue
  311. session_dict = OrderedDict()
  313. for spike_dir in sessions:
  314. session_date = get_session_date(spike_dir)
  315. print(f" {session_date}...", end=" ", flush=True)
  317. try:
  318. condition_results = get_pop_coupling_session(spike_dir)
  319. session_dict[session_date] = condition_results
  320. n_conds = len(condition_results)
  321. n_pl = sum(
  322. len(v["pl_pc_dict"]) for v in condition_results.values()
  323. )
  324. n_npl = sum(
  325. len(v["npl_pc_dict"]) for v in condition_results.values()
  326. )
  327. print(f"{n_conds} conditions, {n_pl} PL, {n_npl} NPL responses")
  328. except Exception as e:
  329. print(f"[WARN] {e}")
  330. traceback.print_exc()
  332. out_pkl = OUT_DIR / f"{animal_id}_pop_coupling_control.pkl"
  333. with open(out_pkl, "wb") as f:
  334. pickle.dump(session_dict, f)
  335. print(f" -> Saved {out_pkl}")

pop_coupling_control.py at commit d1922d1, no license · at the source

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: U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI) (R01EY036094); NINDS (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
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.

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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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"id": "10.1126/sciadv.aef0343",
"type": "article-journal",
"title": "Learning induces activation-mechanism-dependent neural plasticity in an intracortical microstimulation task",
"container-title": "Science advances",
"author": [
{
"family": "Kim",
"given": "Robin"
},
{
"family": "Lycke",
"given": "Roy"
},
{
"family": "Zolotavin",
"given": "Pavlo"
},
{
"family": "Montes",
"given": "Jon"
},
{
"family": "Xie",
"given": "Chong"
},
{
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"given": "Lan"
}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "36",
"page": "eaef0343",
"DOI": "10.1126/sciadv.aef0343",
"PMID": "42696589",
"PMCID": "PMC13544205",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aef0343",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
4
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]
}
}

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