OSCR

Density-based longitudinal neuron tracking in high-density electrophysiological recordings.

Code ↔ Paper

18 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 18 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Neural data processing ↔ QualityMetrics/computeQualityMetrics.m, lines 1–123 · score 0.82 · amplitude cutoff, quality metrics, ISI violations, presence ratio, nearest neighbor
  2. [2] § Methods › Neural data processing ↔ QualityMetrics/computeQualityMetrics.m, lines 1–113 · score 0.82 · amplitude cutoff, quality metrics, ISI violations, presence ratio, nearest neighbor
  3. [3] § Methods › Unit localization ↔ Functions/preprocessSpikeInfo.m, lines 1–64 · score 0.73 · trough amplitude, nearest channels, peak channel, localization, monopolar, algorithm
  4. [4] § Methods › Unit localization ↔ Functions/spikeLocation.m, the whole file · a weak match · score 0.72 · trough amplitude, nearest channels, localization, lsqcurvefit, monopolar, inferred
  5. [5] § Methods › Neural data processing ↔ eMouse_drift/make_noise_model.m, lines 1–136 · score 0.70 · high pass filter, spike detection, firing rate, truncated, preprocessing, ops
  6. [6] § Methods › HDBSCAN ↔ pyDANT/IterativeClustering.py, lines 178–323 · score 0.68 · single linkage tree, distance matrix, precomputed, optimalleaforder, MATLAB, HDBSCAN
  7. [7] § Methods › Neural data processing ↔ configFiles/StandardConfig_MOVEME.m, lines 1–32 · score 0.65 · high pass filter, ops.Th, firing rate, Kilosort, Hz, split
  8. [8] § Methods › Neural data processing ↔ UnitMatchPy/UnitMatchPy/default_params.py, the whole file · a weak match · score 0.61 · ISI violations, refractory period, contaminating, ms, window, metric
  9. [9] § Methods › Neural data processing ↔ MATLAB/UnitMatchPipeline/AssignUniqueIDAlgorithm.m, lines 12–46 · score 0.59 · ISI violations, refractory period, ISIs, ms, window, spikes
  10. [10] § Methods › Iterative clustering algorithm ↔ Functions/iterativeClustering.m, lines 1–77 · score 0.57 · Iterative HDBSCAN, unmatched pair, LDA, weights, matrix, cluster
  11. [11] § Methods › Neural data processing ↔ QualityMetrics/computeQualityMetrics.m, lines 1–123 · score 0.57 · principal component, Nearest neighbor, subsample, metric, spikes, cluster
  12. [12] § Methods › Neural data processing ↔ QualityMetrics/computeQualityMetrics.m, lines 1–113 · score 0.57 · principal component, Nearest neighbor, subsample, metric, spikes, cluster
  13. [13] § Methods › Iterative clustering algorithm ↔ pyDANT/IterativeClustering.py, lines 178–323 · score 0.56 · Updated weights, distance matrix, LDA, selection, iterative, HDBSCAN
  14. [14] § Methods › Comparison with UnitMatch and EMD ↔ MATLAB/Curation/EvaluatingUnitMatch.m, lines 91–190 · score 0.56 · matches identified, UnitMatch, FN, FP, Kilosort, validate
  15. [15] § Methods › Similarity scores › Waveforms ↔ Functions/spikeLocation.m, the whole file · a weak match · score 0.56 · largest amplitude channel, nearest channels, probe, Waveform
  16. [16] § Methods › Comparison with UnitMatch and EMD ↔ MATLAB/Paper_Figures/EMD_integration/run_EMD_batch_onMerged.m, lines 1–83 · score 0.56 · GitHub, neuron tracking, EMD, MATLAB, pipeline, UnitMatch
  17. [17] § Results › Pipeline overview ↔ Functions/iterativeClustering.m, lines 1–77 · score 0.53 · unmatched pairs, feature weights, LDA, HDBSCAN, iterations, pairwise
  18. [18] § Methods › Neural data processing ↔ UnitMatchPy/Demo Notebooks/UMPy_spike_interface_demo.ipynb, lines 68–192 · score 0.51 · Amplitude cutoff, spike amplitudes, kernel, drift, metric, bin

Paper

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

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The authors' code

MATLAB · 190 lines · 7.6 KB · GPL-3.0 · 2 matches

  1. function computeQualityMetrics(folder_data, user_settings)
  2. % COMPUTEQUALITYMETRICS compute all the quality metrics of non-noise clusters
  3. %
  4. % - Input
  5. % - folder_data: the folder where the data is located
  6. % - user_settings: the global settings or the qualityMetrics settings block
  7. %
  8. % Output:
  9. % QualityMetrics.mat will be generated
  10. %
  11. % The default value from https://github.com/AllenInstitute/ecephys_spike_sorting/blob/919992748a5324724ba87169ecdcf9eb6e3b9973/ecephys_spike_sorting/scripts/create_input_json.py#L185
  12. % "quality_metrics_params" : {
  13. % "isi_threshold" : 0.0015,
  14. % "min_isi" : 0.000166,
  15. % "num_channels_to_compare" : 7,
  16. % "max_spikes_for_unit" : 500,
  17. % "max_spikes_for_nn" : 10000,
  18. % "n_neighbors" : 4,
  19. % 'n_silhouette' : 10000,
  20. % "quality_metrics_output_file" : os.path.join(kilosort_output_directory, "metrics_test.csv"),
  21. % "drift_metrics_interval_s" : 51,
  22. % "drift_metrics_min_spikes_per_interval" : 10,
  23. %
  24. % "include_pc_metrics" : True
  25. %
  26. % From https://github.com/AllenInstitute/ecephys_spike_sorting/blob/919992748a5324724ba87169ecdcf9eb6e3b9973/ecephys_spike_sorting/modules/quality_metrics/_schemas.py#L7
  27. %
  28. % isi_threshold = Float(required=False, default=0.0015, help='Maximum time (in seconds) for ISI violation')
  29. % min_isi = Float(required=False, default=0.00, help='Minimum time (in seconds) for ISI violation')
  30. % num_channels_to_compare = Int(required=False, default=13, help='Number of channels to use for computing PC metrics; must be odd')
  31. % max_spikes_for_unit = Int(required=False, default=500, help='Number of spikes to subsample for computing PC metrics')
  32. % max_spikes_for_nn = Int(required=False, default=10000, help='Further subsampling for NearestNeighbor calculation')
  33. % n_neighbors = Int(required=False, default=4, help='Number of neighbors to use for NearestNeighbor calculation')
  34. % n_silhouette = Int(required=False, default=10000, help='Number of spikes to use for calculating silhouette score')
  35. %
  36. % drift_metrics_min_spikes_per_interval = Int(required=False, default=10, help='Minimum number of spikes for computing depth')
  37. % drift_metrics_interval_s = Float(required=False, default=100, help='Interval length is seconds for computing spike depth')
  38. %
  39. % quality_metrics_output_file = String(required=True, help='CSV file where metrics will be saved')
  40. %
  41. % include_pc_metrics = Bool(required=False, default=True, help='Compute features that require principal components')
  42. %
  44. if nargin < 1
  45. folder_data = './';
  46. end
  48. num_channels_to_compare = 7;
  49. max_spikes_for_unit = 500;
  50. max_spikes_for_nn = 10000;
  51. n_neighbors = 4;
  53. if nargin >= 2 && ~isempty(user_settings)
  54. if isfield(user_settings, 'qualityMetrics')
  55. user_settings = user_settings.qualityMetrics;
  56. end
  58. if isfield(user_settings, 'num_channels_to_compare')
  59. num_channels_to_compare = user_settings.num_channels_to_compare;
  60. end
  61. if isfield(user_settings, 'max_spikes_for_unit')
  62. max_spikes_for_unit = user_settings.max_spikes_for_unit;
  63. end
  64. if isfield(user_settings, 'max_spikes_for_nn')
  65. max_spikes_for_nn = user_settings.max_spikes_for_nn;
  66. end
  67. if isfield(user_settings, 'n_neighbors')
  68. n_neighbors = user_settings.n_neighbors;
  69. end
  70. end
  72. % read the data
  73. spike_times = readNPY(fullfile(folder_data, 'spike_times.npy'));
  74. spike_clusters = readNPY(fullfile(folder_data, 'spike_clusters.npy'));
  75. amplitudes = readNPY(fullfile(folder_data, 'amplitudes.npy'));
  76. cluster_ids = unique(spike_clusters);
  78. n_cluster = length(cluster_ids);
  80. cluster_group = readtable(fullfile(folder_data, 'cluster_group.tsv'), 'Delimiter', '\t', 'FileType', 'text');
  81. labels = cell(n_cluster, 1);
  82. if any(strcmpi(cluster_group.Properties.VariableNames, 'group'))
  83. for k = 1:n_cluster
  84. idx = find(cluster_group.cluster_id == cluster_ids(k));
  85. if isempty(idx)
  86. continue
  87. end
  89. labels{k} = cluster_group.group{idx};
  90. end
  91. end
  93. % spike_locations = zeros(n_cluster, 2);
  94. isi_violations = NaN(n_cluster, 1);
  95. amplitude_cutoffs = NaN(n_cluster, 1);
  96. presence_ratio = NaN(n_cluster, 1);
  97. amplitude_median = NaN(n_cluster, 1);
  99. disp('Computing Non-PC features...');
  100. for k = 1:n_cluster
  101. if strcmpi(labels{k}, 'noise')
  102. if mod(k, 10) == 1
  103. fprintf('%d / %d done!\n', k, n_cluster);
  104. end
  105. continue
  106. end
  108. spike_time_this = double(spike_times(spike_clusters==cluster_ids(k)))./30000*1000; % in ms
  109. amplitude_this = double(amplitudes(spike_clusters==cluster_ids(k)));
  110. amplitude_this = rmoutliers(amplitude_this, 'median', 'ThresholdFactor', 5);
  112. t_begin = 0;
  113. t_end = double(max(spike_times))./30000*1000;
  115. isi_violations(k) = isiViolations(spike_time_this);
  116. amplitude_cutoffs(k) = amplitudeCutoffs(amplitude_this);
  117. amplitude_median(k) = median(amplitude_this);
  118. presence_ratio(k) = presenceRatio(spike_time_this, t_begin, t_end);
  120. if mod(k, 10) == 1
  121. fprintf('%d / %d done!\n', k, n_cluster);
  122. end
  123. end
  125. %% PC based metrics
  126. % load files
  127. chanMap = getKilosortChanMap(folder_data);
  128. pc_features = readNPY(fullfile(folder_data, 'pc_features.npy'));
  129. pc_feature_ind = double(readNPY(fullfile(folder_data, 'pc_feature_ind.npy')));
  130. spike_templates = double(readNPY(fullfile(folder_data, 'spike_templates.npy')));
  132. assert(mod(num_channels_to_compare, 2) == 1);
  133. half_spread = floor((num_channels_to_compare - 1) / 2);
  135. cluster_ids = double(unique(spike_clusters));
  136. template_ids = double(unique(spike_templates));
  138. template_peak_channels = zeros(length(template_ids), 1);
  139. cluster_peak_channels = zeros(length(cluster_ids), 1); % 0 ~ n_channel-1
  141. for idx = 1:length(template_ids)
  142. template_id = template_ids(idx);
  143. for_template = squeeze(spike_templates == template_id);
  144. pc_max = find(mean(pc_features(for_template, 1, :), 1) == max(mean(pc_features(for_template, 1, :), 1)), 1);
  145. template_peak_channels(idx) = pc_feature_ind(template_id+1, pc_max); % matlab index from 1
  146. end
  148. for idx = 1:length(cluster_ids)
  149. cluster_id = cluster_ids(idx);
  150. for_unit = squeeze(spike_clusters == cluster_id);
  151. templates_for_unit = unique(spike_templates(for_unit));
  152. template_positions = ismember(template_ids, templates_for_unit);
  153. cluster_peak_channels(idx) = round(median(template_peak_channels(template_positions)));
  154. end
  156. isolation_distance = NaN(n_cluster, 1);
  157. d_prime = NaN(n_cluster, 1);
  158. nn_miss_rate = NaN(n_cluster, 1);
  159. nn_hit_rate = NaN(n_cluster, 1);
  160. l_ratio = NaN(n_cluster, 1);
  162. disp('Computing PC features...');
  163. for idx = 1:n_cluster
  164. if strcmpi(labels{idx}, 'noise')
  165. continue
  166. end
  168. cluster_id = cluster_ids(idx);
  169. [isolation_distance(idx), d_prime(idx), nn_miss_rate(idx), nn_hit_rate(idx), l_ratio(idx)] = ...
  170. calculate_pc_metrics_one_cluster(...
  171. cluster_peak_channels, idx, cluster_id, cluster_ids,...
  172. half_spread, pc_features, pc_feature_ind,...
  173. spike_clusters, spike_templates,...
  174. max_spikes_for_unit, max_spikes_for_nn, n_neighbors, chanMap);
  176. if mod(idx, 10) == 1
  177. fprintf('%d / %d done!\n', idx, n_cluster);
  178. end
  179. end
  181. %% save to files
  182. metric_names = {'ISI violations', 'Amplitude cutoffs', 'Presence ratio', 'Median Amplitude', 'Isolation distance', 'D prime', 'Nearest-neighbor miss rate', 'Nearest-neighbor hit rate', 'L ratio'};
  183. metrics = {isi_violations, amplitude_cutoffs, presence_ratio, amplitude_median, isolation_distance, d_prime, nn_miss_rate, nn_hit_rate, l_ratio};
  185. save(fullfile(folder_data, 'QualityMetrics.mat'),...
  186. 'cluster_ids', 'isi_violations', 'amplitude_cutoffs', 'presence_ratio', 'labels', 'amplitude_median',...
  187. 'isolation_distance', 'd_prime', 'nn_miss_rate', 'nn_hit_rate', 'l_ratio',...
  188. 'metrics', 'metric_names');
  190. end

computeQualityMetrics.m at commit ebf982a, under GPL-3.0 · at the source

Overview

Authors: Yue Huang1,2, Hanbo Wang1,3, Jiaming Cao1,3, Yu Chen1,2, Xuanning Wang1,2, Yujie Zhao1,3, Hengkun Ren1,3, Qiang Zheng1,2, Jianing Yu1,2,4
ORCID iDs: Jianing Yu
  1. State Key Laboratory of Membrane Biology, Peking University, Beijing 100871, China
  2. School of Life Sciences, Peking University, Beijing 100871, China
  3. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
  4. IDG/McGovern Institute for Brain Research at Peking University, Beijing 100871, China
Institutions: Peking University (China); State Key Laboratory of Membrane Biology
Journal: Patterns (New York, N.Y.), volume 7, issue 8, article 101590
Dates: received 8 December 2025; accepted 20 May 2026; published online 17 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.patter.2026.101590 · PMID 42630833 · PMCID PMC13494646 · OpenAlex W7165056200
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Neuropixels, single-neuron tracking, density-based clustering, HDBSCAN, chronic electrophysiology, probe-motion correction, spike sorting, cross-session matching, representational drift
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (32070983, 32271052, 32571183); Natural Science Foundation of Beijing Municipality (5212007); Peking-Tsinghua Center for Life Sciences; State Key Laboratory of Membrane Biology
Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

Tracking neurons across days in high-density extracellular recordings is essential for investigating the mechanisms of learning and representational drift. However, in weeks-long recordings, identifying matches across sessions is hindered by changes in spike waveforms and unit turnover. We introduce DANT (density-based across-day neuron tracking), a framework that iterates between density-based clustering in feature space and probe-motion estimation inferred from provisional matches. The estimated motion is then used to reregister spike waveforms across sessions before clustering is recomputed in the next iteration. Within this loop, DANT learns a decision boundary from match and non-match labels and uses it in post hoc curation. Applied to weeks-long Neuropixels recordings from cortex and striatum in freely moving rats during reaction-time and self-timing tasks, DANT substantially increases match yield while maintaining a low false-positive rate relative to existing approaches. These results establish DANT as a general unsupervised solution for longitudinal tracking in chronic recordings.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 18 matches between paragraphs and lines of code.

cortex-lab/phy

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 807cf1825c16a3d95cb591091c291bad6803e81d, 26 September 2026
Languages: Python (150), Shell (2), JavaScript (2)
Size: 276 files, 154 scripts
Software Heritage: archived
Found in: “Data and code availability”
Holds: README, license file, environment (pyproject.toml, uv.lock, deprecated/environment.yml, deprecated/requirements-dev.txt, deprecated/requirements.txt, deprecated/setup.cfg, deprecated/setup.py), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: Phy (98 files), NumPy (67 files), Matplotlib (8 files), SciPy (2 files), imageio (1 file), Pillow (1 file), scikit-learn (1 file), UMAP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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156 files
At the source: github.com/cortex-lab/phy

jiumao2/PhyWaveformPlugin

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 03097410523d991c704b5a58c28f5e8d911f02dd, 21 March 2025
Languages: Python (5)
Size: 11 files, 5 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, documentation
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: Phy (5 files), NumPy (4 files), Matplotlib (2 files), Neo (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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7 files
At the source: github.com/jiumao2/PhyWaveformPlugin

jiumao2/AutoCurationKilosort

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ebf982a976f477cbe59ad53495a9f61e4fc13c5b, 13 July 2026
Languages: MATLAB (158)
Size: 183 files, 158 scripts
Software Heritage: not archived
Found in: the text, “Neural data processing”
Holds: README, license file, documentation, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: Statistics and Machine Learning Toolbox (3 files), Signal Processing Toolbox (2 files), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
160 files
At the source: github.com/jiumao2/AutoCurationKilosort

jiumao2/DANT_UI

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e86f4419c252ca6f917cebdd35ac0a2c5bcce919, 4 December 2025
Languages: Python (2)
Size: 12 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Manual curation”
Holds: README, license file, environment (pyproject.toml, requirements.txt), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: Matplotlib (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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4 files
At the source: github.com/jiumao2/DANT_UI

EnnyvanBeest/UnitMatch

License: CC-BY-NC-SA-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 95b6fed5db84d12f67487b92d07f0a2664a6122f, 1 September 2026
Languages: MATLAB (138), Python (68), Jupyter (12), C (1), Fortran (1)
Size: 560 files, 220 scripts
Software Heritage: not archived
Found in: the text, “Comparison with UnitMatch and EMD”
Holds: README, license file, environment (UnitMatchPy/pyproject.toml, UnitMatchPy/requirements-cuda.txt), tests, continuous integration, 12 notebooks
Not found: CITATION.cff, documentation
Tools: Statistics and Machine Learning Toolbox (52 files), NumPy (52 files), Matplotlib (27 files), pandas (20 files), PyTorch (17 files), h5py (12 files), SciPy (12 files), Signal Processing Toolbox (5 files), scikit-learn (5 files), SpikeInterface (3 files), statsmodels (3 files), Violinplot-Matlab (3 files), Optimization Toolbox (2 files), Phy (2 files), CircStat (1 file), Kilosort (1 file), Parallel Computing Toolbox (1 file), seaborn (1 file), shadedErrorBar (1 file), UMAP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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222 files
At the source: github.com/EnnyvanBeest/UnitMatch

Zenodo 19973060

License: GPL-2.0
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Size: 1 file
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Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Parallel Computing Toolbox (24 files), Kilosort (13 files), Signal Processing Toolbox (6 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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120 files
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Zenodo 20021262

License: GPL-3.0
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Size: 1 file
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Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Phy (5 files), NumPy (4 files), Matplotlib (2 files), Neo (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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7 files
At the source:

Zenodo 19973259

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (11 files), Signal Processing Toolbox (4 files), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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175 files
At the source:

Zenodo 19972266

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Parallel Computing Toolbox (3 files), Optimization Toolbox (2 files), Statistics and Machine Learning Toolbox (2 files), NumPy (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
160 files
At the source:

Zenodo 19972858

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (8 files), Matplotlib (5 files), SciPy (5 files), h5py (2 files), scikit-learn (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
12 files
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Zenodo 19973154

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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4 files
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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.

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  • 11 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,005 scripts, each with its path and the digest of its content;
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Data

Datasets cited

Data and code availability

Example data for the rat presented in Figures 2, 3, and 4 are available as part of the software demo at figshare: https://doi.org/10.6084/m9.figshare.30596258.v2.60,61 All additional data and code are available at Zenodo: https://doi.org/10.5281/ZENODO.20019488.62

DANT is available as both a MATLAB implementation on GitHub at https://github.com/jiumao2/DANT (GPL-3.0 license)63 and a Python implementation at https://github.com/jiumao2/pyDANT (GPL-3.0 license).64 It utilizes the HDBSCAN Python package for clustering27 (BSD-3-Clause license). Neuropixels data acquisition was performed using SpikeGLX at http://billkarsh.github.io/SpikeGLX/ (Janelia Research Campus Software Copyright 1.2). The Python application for manual curation of DANT results is available at https://github.com/jiumao2/DANT_UI (GPL-3.0 license).65 Spike sorting was conducted with a modified version of Kilosort 2.5 at https://github.com/jiumao2/Kilosort_2_5 (GPL-2.0 license),51 followed by manual curation using Phy2 at https://github.com/cortex-lab/phy (BSD-3-Clause license), with custom plugins available at https://github.com/jiumao2/PhyWaveformPlugin (GPL-3.0 license).52 For a subset of datasets, automated curation was performed using AutoCurationKilosort at https://github.com/jiumao2/AutoCurationKilosort (GPL-3.0 license).53

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

Versions

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Version 2, 28 September 2026

  • Authors: added Jianing Yu (0009-0006-4924-821X); removed Jianing Yu

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 9 keywords, 4 funders, 59 references.

Cite

This paper

Huang, Y., Wang, H., Cao, J., Chen, Y., Wang, X., Zhao, Y., Ren, H., Zheng, Q., & Yu, J. (2026). Density-based longitudinal neuron tracking in high-density electrophysiological recordings. Patterns (New York, N.Y.), 7(8), 101590. https://doi.org/10.1016/j.patter.2026.101590

BibTeX

@article{huang2026density,
author = {Huang, Yue and Wang, Hanbo and Cao, Jiaming and Chen, Yu and Wang, Xuanning and Zhao, Yujie and Ren, Hengkun and Zheng, Qiang and Yu, Jianing},
title = {{Density-based longitudinal neuron tracking in high-density electrophysiological recordings}},
journal = {Patterns (New York, N.Y.)},
year = {2026},
month = jun,
volume = {7},
number = {8},
pages = {101590},
publisher = {Elsevier},
issn = {2666-3899},
doi = {10.1016/j.patter.2026.101590},
url = {https://doi.org/10.1016/j.patter.2026.101590},
pmid = {42630833},
pmcid = {PMC13494646}
}

RIS

TY - JOUR
AU - Huang, Yue
AU - Wang, Hanbo
AU - Cao, Jiaming
AU - Chen, Yu
AU - Wang, Xuanning
AU - Zhao, Yujie
AU - Ren, Hengkun
AU - Zheng, Qiang
AU - Yu, Jianing
TI - Density-based longitudinal neuron tracking in high-density electrophysiological recordings
T2 - Patterns (New York, N.Y.)
J2 - Patterns (N Y)
PY - 2026
DA - 2026/06/17
VL - 7
IS - 8
SP - 101590
SN - 2666-3899
PB - Elsevier
DO - 10.1016/j.patter.2026.101590
UR - https://doi.org/10.1016/j.patter.2026.101590
LA - en
ER -

CSL-JSON

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