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A quality measure for repeating multiple-unit spike patterns.

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  1. [1] § Computing quality › Simulation-based estimation of ubiquity ↔ codes/compute_simulated_signature_histogram.py, lines 102–156 · score 0.60 · signature histogram, simple novelty, accumulated, summed, simulations, mining

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The authors' code

Python · 174 lines · 7.1 KB · CC-BY-4.0 · 1 match

  1. import sys
  2. import os
  3. import yaml
  4. from yaml import Loader
  6. import numpy as np
  7. import quantities as pq
  8. from elephant.conversion import BinnedSpikeTrain
  9. from elephant.spade import concepts_mining
  11. from utils import concatenate_spiketrains, compute_isi_based_bin_firing_probability, generate_simulated_spiketrains
  14. # Example command line to run this script:
  15. # python compute_simulated_signature_histogram.py {session} {epoch} {trialtype} config.yaml
  17. # Parse command line options
  18. args = sys.argv
  19. if len(args) != 5:
  20. raise ValueError("Following arguments need to be given: session, epoch, trial type, config file")
  21. session = args[1]
  22. epoch = args[2]
  23. trialtype = args[3]
  24. configfile = args[4]
  26. spiketrain_file = f"../data/spiketrains/{session}_{epoch}_{trialtype}.npy"
  27. firing_rate_file = f"../data/firing_rates/{session}_{epoch}_{trialtype}.npy"
  29. # Load parameters from the config file
  30. with open(configfile, 'r') as stream:
  31. config = yaml.load(stream, Loader=Loader)
  33. param_keys = ['min_firing_rate', 'binsize', 'winlen', 'min_size', 'max_size', 'min_occ', 'max_occ',
  34. 'n_sim', 'bin_firing_prob_smooth_width', 'min_nov', 'max_nov', 'nov_step']
  35. params = {key:config[key] for key in param_keys}
  36. params['min_firing_rate'] = params['min_firing_rate'] * pq.Hz
  37. params['binsize'] = params['binsize'] * pq.s
  38. for key in ['max_size', 'max_occ',]:
  39. if params[key] == 'None':
  40. raise ValueError(f"`{key}` cannot be set to None")
  42. # Load data from files
  43. spiketrains = np.load(spiketrain_file, allow_pickle=True).item()
  44. firing_rates = np.load(firing_rate_file, allow_pickle=True).item()
  46. # Discard units with low firing rates
  47. firing_rate_threshold = params['min_firing_rate'].rescale('Hz').magnitude
  48. num_discard = 0
  49. for unit_id, firing_rate in firing_rates.items():
  50. if firing_rate < firing_rate_threshold:
  51. del spiketrains[unit_id]
  52. num_discard += 1
  53. print(f"{session}/{epoch}_{trialtype}: {num_discard} units are discarded due to low firing rates (< {params['min_firing_rate'].magnitude} Hz). {len(spiketrains)} units remain.")
  55. unit_ids = np.array(list(spiketrains.keys()))
  57. # Compute spike novelties based on bin occupation ratio
  58. bin_occupation_ratio = {}
  59. for unit_id, sts in spiketrains.items():
  60. unit_bin_counts = []
  61. num_bins = []
  62. for st in sts:
  63. binned_st = BinnedSpikeTrain(st, params['binsize'], tolerance=None).binarize().to_array().flatten()
  64. num_bins.append(len(binned_st))
  65. unit_bin_counts.append(np.sum(binned_st))
  66. bin_occupation_ratio[unit_id] = np.sum(unit_bin_counts) / np.sum(num_bins)
  68. spike_novelties = {unit_id: -np.log(bin_occupation_ratio[unit_id]) for unit_id in unit_ids}
  70. # Compute 4D signature histogram from simulated FIM patterns
  71. num_simulations = params['n_sim']
  73. winlen = params['winlen']
  74. min_size = params['min_size']
  75. max_size = params['max_size']
  76. min_occ = params['min_occ']
  77. max_occ = params['max_occ']
  78. min_nov = params['min_nov']
  79. max_nov = params['max_nov']
  80. nov_step = params['nov_step']
  82. ## Set up bins for histograms
  83. bins_size = np.arange(1.5, max_size+1, 1)
  84. bins_occ = np.arange(0.5, max_occ+1, 1)
  85. bins_dur = np.arange(-0.5, winlen, 1)
  86. bins_nov = np.arange(min_nov, max_nov + nov_step/2, nov_step)
  88. bins = (bins_size, bins_occ, bins_dur, bins_nov)
  89. signature_hist = np.zeros([len(x)-1 for x in bins])
  90. signature_hist_simple_nov = np.zeros([len(x)-1 for x in bins])
  91. nov_hist = np.zeros((len(bins_nov)-1, len(bins_nov)-1))
  93. # Compute time-varying bin firing probability based on ISIs of the original data
  94. spiketrains_orig = concatenate_spiketrains(spiketrains, 0.) # inter-trial interval is set to 0 for the manuscript
  95. bin_firing_probs = compute_isi_based_bin_firing_probability(
  96. spiketrains_orig, params['binsize'], params['bin_firing_prob_smooth_width'], normalize=True)
  98. print(f"Applying FIM to {num_simulations} simulated data...")
  99. print(f"(binsize: {params['binsize']}, winlen: {winlen}, min_size: {min_size}, max_size: {max_size}, min_occ: {min_occ}, max_occ: {max_occ},"
  100. f" min_nov: {min_nov}, max_nov: {max_nov}, n_sim: {num_simulations}, smooth_width: {params['bin_firing_prob_smooth_width']})")
  102. # Collect signatures from simulated data and compute the 4D signature histogram
  103. for i_sim in range(num_simulations):
  104. # Generate simulated spike trains
  105. spiketrains_sim = generate_simulated_spiketrains(bin_firing_probs, params['binsize'])
  107. # Compute time-varying bin novelty
  108. novelty_array = np.copy(bin_firing_probs)
  109. novelty_array[novelty_array == 0] = np.nan # replace 0 with nan to avoid taking log(0)
  110. novelty_array = -np.log(novelty_array)
  112. for size in range(min_size, max_size + 1):
  113. # Apply FIM
  114. raw_fim_patterns, _ = concepts_mining(spiketrains_sim, params['binsize'], winlen,
  115. min_spikes=size, min_occ=min_occ, max_spikes=size, max_occ=max_occ, min_neu=size, report='a')
  117. # Compute signatures
  118. sizes = []
  119. occurrences = []
  120. durations = []
  121. novelties = []
  122. simple_novelties = []
  123. for i, fim_pattern in enumerate(raw_fim_patterns):
  124. pattern_bin_idxs, sample_idxs = fim_pattern
  125. pattern_unit_idxs = np.array([int(x // winlen) for x in pattern_bin_idxs])
  127. if len(pattern_unit_idxs) != len(np.unique(pattern_unit_idxs)): # skip auto-patterns
  128. continue
  130. pattern_unit_ids = unit_ids[pattern_unit_idxs]
  131. lags = np.array([int(x % winlen) for x in pattern_bin_idxs])
  133. sizes.append(len(pattern_unit_ids))
  134. occurrences.append(len(sample_idxs))
  135. durations.append(np.max(lags))
  136. simple_novelties.append(np.sum([spike_novelties[x] for x in pattern_unit_ids]))
  138. nov_sum = 0
  139. for sample_idx in sample_idxs:
  140. for unit_idx, lag in zip(pattern_unit_idxs, lags):
  141. nov_sum += novelty_array[unit_idx, sample_idx + lag]
  142. novelties.append(nov_sum / len(sample_idxs))
  144. # Accumulate signatures in multiple histograms
  145. signatures = np.column_stack((sizes, occurrences, durations, novelties))
  146. signature_hist += np.histogramdd(signatures, bins=bins)[0]
  148. signatures = np.column_stack((sizes, occurrences, durations, simple_novelties))
  149. signature_hist_simple_nov += np.histogramdd(signatures, bins=bins)[0]
  151. signatures = np.column_stack((novelties, simple_novelties))
  152. nov_hist += np.histogramdd(signatures, bins=(bins_nov, bins_nov))[0]
  154. if (i_sim + 1) % int(num_simulations//10) == 0:
  155. print(f"\t{i_sim+1} simulations done...")
  156. print("\t...finished.")
  158. # Save result
  159. result = {
  160. "num_surrogates": num_simulations,
  161. "sim_method": "isi_based_bin_firing_prob",
  162. "bin_firing_prob_smooth_width": params['bin_firing_prob_smooth_width'],
  163. "bins_size": bins_size,
  164. "bins_occ": bins_occ,
  165. "bins_dur": bins_dur,
  166. "bins_nov": bins_nov,
  167. "signature_hist": signature_hist,
  168. "signature_hist_simple_nov": signature_hist_simple_nov,
  169. "nov_hist": nov_hist
  170. }
  171. result_dir = '../results/signature_histograms'
  172. if not os.path.exists(result_dir):
  173. os.makedirs(result_dir)
  174. np.save(f"{result_dir}/{session}_{epoch}_{trialtype}.npy", result)

compute_simulated_signature_histogram.py, under CC-BY-4.0 · at the source

Overview

Authors: Günther Palm1,2, Monica Paoletti1,3, Junji Ito1, Alessandra Stella1, Sonja Grün1,4,5
ORCID iDs: Günther Palm, Monica Paoletti, Junji Ito, Sonja Grün
  1. Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich,Jülich, Germany
  2. Institute of Neural Information Processing, University of Ulm,Ulm, Germany
  3. Cognitive Neuroscience Department, Scuola Internazionale Superiore di Studi Avanzati (SISSA),Trieste, Italy
  4. JARA-Institute Brain Structure-Function Relationships (INM-10), Forschungszentrum Jülich,Wilhelm-Johnen-Str., Jülich, 52428 Germany
  5. Theoretical Systems neurobiology, RWTH Aachen University,Aachen, Germany
Institutions: Forschungszentrum Jülich (Germany); Universität Ulm (Germany); Scuola Internazionale Superiore di Studi Avanzati (Italy); RWTH Aachen University (Germany)
Journal: Biological cybernetics, volume 120, issue 5-6, article 25
Dates: received 31 January 2026; accepted 22 June 2026; published online 7 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s00422-026-01048-2 · PMID 42566086 · PMCID PMC13451522 · OpenAlex W7201832390
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), none (in silico) (organism), computational (subfield)
Methods: Statistics, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions
Keywords: Spatio-temporal spike pattern, Multiple-unit spike trains, Statistical analysis, Significance test
MeSH: Action Potentials*, Models, Neurological*, Neurons*, Algorithms, Animals, Computer Simulation (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NRW-network ’iBehave’ (NW21-049); Deutsche Forschungsgemeinschaft (368482240/GRK2416)
Citations: not cited yet (Europe PMC); 89 references in the paper

Abstract

We propose a quality measure for spatio-temporal spike patterns (STPs) in multiple-neuron recordings. In such recordings, repeating STPs or pattern repetitions (PRs) are often found, with many of these generated by chance. To rule those out, statistical tests have been developed to discriminate the unlikely from the more likely PRs. This statistical problem is complicated by the fact that there are several obvious quality criteria for a PR, such as the size (the number of spikes) of the pattern and the number of its occurrences. Here, we propose a canonical way of combining several criteria (which we collect in the so-called signature of the pattern) into a single quality measure, based on the ’unlikeliness’ of the pattern. This measure is defined mathematically, and a formula for its computation is derived for stationary spike trains. It can be used to compare PRs. Since spike trains are not stationary in practice, we discuss, for two experimental data sets, how well the stationary formula correlates with the defined quality measure as determined from simulations. Sometimes the calculated values are far off, but one can still use the stationary formula or also some simpler, related formulas as ’proxies’ for the quality, to compare PRs and also for statistical tests that avoid the multiple testing problem incurred by using several quality criteria. Based on our results, we propose a few test statistics, i.e., random variables on the space of multi-unit spike trains with an appropriate null-hypothesis distribution, to evaluate STPs with less computational and sampling efforts.

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 1 match between paragraphs and lines of code.

Zenodo 18424512

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availibility”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (4 files), Elephant (3 files), Matplotlib (1 file), Neo (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
5 files
At the source: doi.org/10.5281/zenodo.18424512

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

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Data Availibility

The data and codes that support the findings of this study are available in Zenodo with the identifier: https://doi.org/10.5281/zenodo.18424512.

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

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Version 3, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 6 MeSH terms, 2 funders, 84 references.

Cite

This paper

Palm, G., Paoletti, M., Ito, J., Stella, A., & Grün, S. (2026). A quality measure for repeating multiple-unit spike patterns. Biological cybernetics, 120(5-6), 25. https://doi.org/10.1007/s00422-026-01048-2

BibTeX

@article{palm2026quality,
author = {Palm, Günther and Paoletti, Monica and Ito, Junji and Stella, Alessandra and Grün, Sonja},
title = {{A quality measure for repeating multiple-unit spike patterns}},
journal = {Biological cybernetics},
year = {2026},
month = aug,
volume = {120},
number = {5-6},
pages = {25},
publisher = {Springer Science+Business Media},
issn = {0340-1200},
doi = {10.1007/s00422-026-01048-2},
url = {https://doi.org/10.1007/s00422-026-01048-2},
pmid = {42566086},
pmcid = {PMC13451522}
}

RIS

TY - JOUR
AU - Palm, Günther
AU - Paoletti, Monica
AU - Ito, Junji
AU - Stella, Alessandra
AU - Grün, Sonja
TI - A quality measure for repeating multiple-unit spike patterns
T2 - Biological cybernetics
J2 - Biol Cybern
PY - 2026
DA - 2026/08/07
VL - 120
IS - 5-6
SP - 25
SN - 0340-1200
PB - Springer Science+Business Media
DO - 10.1007/s00422-026-01048-2
UR - https://doi.org/10.1007/s00422-026-01048-2
LA - en
ER -

CSL-JSON

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