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A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity.

Code ↔ Paper

8 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 8 matches
  1. [1] § Methods › Neuron model ↔ network_simulations/utils/netGen_utils.py, lines 23–35 · score 0.70 · leak reversal potential, leak conductance, external, threshold, synaptic, neurons
  2. [2] § Results › Homeostatic mechanisms shape connectivity in both systems ↔ degree_analysis.py, lines 377–440 · score 0.56 · Pearson correlation coefficient, uncoupled TM Triplet, degree strength, SL STDP, weight
  3. [3] § Results › SL-STDP model improves information capacity in RNNs ↔ network_simulations/network_models.py, lines 214–336 · score 0.55 · facilitating connections, network model, facilitating synapses, ratios, inhibitory, TM
  4. [4] § Methods › Network analysis ↔ plot_raster_synchrony.py, lines 188–263 · score 0.55 · spike contrast, autocorrelation, peak, smoothing, bursting, kernel
  5. [5] § Methods › Working memory task › Target generation. ↔ network_simulations/run_network_simulations.py, lines 1086–1125 · score 0.54 · round robin fashion, capacity
  6. [6] § Results › SL-STDP synapse and Triplet synapse generate different connectivity profiles in RNNs ↔ plot_raster_synchrony.py, lines 22–64 · score 0.52 · ActiveST, spike contrast, synchrony, bin
  7. [7] § Results › SL-STDP synapse and Triplet synapse generate different connectivity profiles in RNNs ↔ plot_raster_synchrony.py, lines 22–64 · score 0.52 · ActiveST, Spike contrast, Synchrony, spike trains, Raster, bin
  8. [8] § Methods › Input encoding ↔ network_simulations/network_models.py, lines 104–195 · score 0.51 · inhomogeneous Poisson process, noise, encoding, signal

Paper

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The authors' code

Python · 265 lines · 11 KB · Apache-2.0 · 3 matches

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3. from holoviews.plotting.bokeh.styles import alpha
  4. import matplotlib as mpl
  5. from matplotlib.ticker import FixedLocator, FixedFormatter
  6. import pandas as pd
  7. import os
  8. from elephant.statistics import isi, cv, mean_firing_rate, instantaneous_rate
  9. from elephant.spike_train_synchrony import spike_contrast
  10. from elephant import kernels
  11. import sympy as sp
  12. import neo
  13. import quantities as q
  15. from network_simulations.utils.nest_utils import convert_spikes_df2neo, smooth_spike_trains
  16. import viziphant as vp
  17. import scipy
  22. def plot_spike_contrast(trace, ax, lw=1.0,
  23. xscale='log',):
  24. """
  25. Plot Spike-contrast synchrony measure Ciba et al. 2018
  27. Parameters
  28. ----------
  29. trace : SpikeContrastTrace
  30. The trace output from
  31. :func:`elephant.spike_train_synchrony.spike_contrast` function.
  32. ax : plt.Axes
  33. Axis on which to plot
  34. title : str or None.
  35. The plot title. If None, an automatic description will be set.
  36. Default: None
  37. lw : float, optional
  38. The curves line width.
  39. Default: 1.0
  40. xscale : str, optional
  41. X axis scale.
  42. Default: 'log'
  43. """
  45. units = trace.bin_size.units
  46. bin_sizes = trace.bin_size.magnitude
  47. plot_inds = np.nonzero(bin_sizes < 1000)[0] # 1 s max bin
  48. bins = bin_sizes[plot_inds]
  49. contrast = np.array(trace.contrast)[plot_inds]
  50. active_st = np.array(trace.active_spiketrains)[plot_inds]
  51. synch = np.array(trace.synchrony)[plot_inds]
  52. ax.plot(bins, contrast, lw=lw, label=r'Contrast($\Delta$)',
  53. linestyle='dashed', color='limegreen')
  54. ax.plot(bins, active_st, lw=lw,
  55. label=r'ActiveST($\Delta$)',
  56. linestyle='dashdot', color='dodgerblue')
  57. ax.plot(bins, synch, lw=lw,
  58. label=r'Synchrony($\Delta$)', color='black')
  59. bin_id_max = np.argmax(synch)
  60. print("Max synchrony {} with bin size {} ms".format(synch[bin_id_max], bins[bin_id_max]))
  61. ax.legend(frameon=False, fontsize=7)
  62. ax.set_xscale(xscale)
  63. ax.set_xlabel(fr"Bin size $\Delta$ ({units.dimensionality})")
  64. ax.set_ylabel(r"Trace values")
  67. if __name__ == '__main__':
  69. dir_path = os.path.dirname(os.path.realpath(__file__))
  70. print(dir_path)
  72. model1 = "minimal_SL-STDP"
  73. f1 = "results/{}/spikeTimesExc_{}_seed10_noNorm_eta3.0.csv".format(model1, model1)
  74. f2 = "results/{}/spikeTimesInh_{}_seed10_noNorm_eta3.0.csv".format(model1, model1)
  76. model2 = "minimal_triplet"
  77. f3 = "results/{}/spikeTimesExc_{}_seed10_noNorm_eta3.0.csv".format(model2, model2)
  78. f4 = "results/{}/spikeTimesInh_{}_seed10_noNorm_eta3.0.csv".format(model2, model2)
  80. # Correlated input
  81. f5 = "results/{}/spikeTimesExc_{}_seed10_noNorm_eta1.5_sinp.csv".format(model1, model1)
  82. f6 = "results/{}/spikeTimesInh_{}_seed10_noNorm_eta1.5_sinp.csv".format(model1, model1)
  84. f51 = "results/{}/spikeTimesExc_{}_seed10_noNorm_eta1.2_sinp.csv".format(model1, model1)
  85. f61 = "results/{}/spikeTimesInh_{}_seed10_noNorm_eta1.2_sinp.csv".format(model1, model1)
  87. # the last is plotting time (ms) for the raster
  88. files = [ (f3, f4, model2, "noNorm_eta3.0", 700, 150),
  89. (f5, f6, model1, "sinp_eta1.5", 450, 0),
  90. (f51, f61, model1, "sinp_eta1.2", 450, 0)]
  92. # Optional: test with different setups
  93. #"""
  94. f13 = "results/{}/spikeTimesExc_{}_seed10_noNorm_eta3.0_e2i.csv".format(model1, model1)
  95. f14 = "results/{}/spikeTimesInh_{}_seed10_noNorm_eta3.0_e2i.csv".format(model1, model1)
  96. f7 = "results/{}/spikeTimesExc_{}_seed10_noNorm_eta3.0_60min.csv".format(model1, model1)
  97. f8 = "results/{}/spikeTimesInh_{}_seed10_noNorm_eta3.0_60min.csv".format(model1, model1)
  98. f9 = "results/{}/spikeTimesExc_{}_seed10_wnorm_eta3.0.csv".format(model1, model1)
  99. f10 = "results/{}/spikeTimesInh_{}_seed10_wnorm_eta3.0.csv".format(model1, model1)
  100. f11 = "results/{}/spikeTimesExc_{}_seed10_wnorm_eta3.0.csv".format(model2, model2)
  101. f12 = "results/{}/spikeTimesInh_{}_seed10_wnorm_eta3.0.csv".format(model2, model2)
  102. files = [(f13, f14, model1, "noNorm_eta3.0_e2i", 200, 0),
  103. (f7, f8, model1, "noNorm_eta3.0_60min", 200, 0),
  104. (f9, f10, model1, "wnorm_eta3.0", 200, 0),
  105. (f11, f12, model2, "wnorm_eta3.0", 200, 0),]
  106. #"""
  107. for (file1, file2, dir_name, name_end, cutoff, offset) in files:
  108. try:
  109. spikes_e = pd.read_csv(os.path.join(dir_path, file1))
  110. spikes_i = pd.read_csv(os.path.join(dir_path, file2))
  111. except:
  112. print("Could not load ", file1, " or ", file2)
  113. break
  115. t_start = 0. * q.ms
  116. t_end = 5000. * q.ms
  118. spikes_e_neo = convert_spikes_df2neo(spikes_e, t_start=t_start, t_stop=t_end)
  119. spikes_i_neo = convert_spikes_df2neo(spikes_i, t_start=t_start, t_stop=t_end)
  121. frates_e = np.array([mean_firing_rate(spike_train).item() for spike_train in spikes_e_neo])
  122. frates_e = np.sort(frates_e)
  124. # CV calculation
  125. cv_list_e = [cv(isi(spike_train)) for spike_train in spikes_e_neo]
  126. cv_list_i = [cv(isi(spike_train)) for spike_train in spikes_i_neo]
  128. cv_mean_e = np.mean(cv_list_e)
  129. cv_mean_i = np.mean(cv_list_i)
  131. print("After training")
  132. print("Mean CV exc: ", cv_mean_e)
  133. print("Mean CV inh: ", cv_mean_i)
  135. plt.figure(figsize=(5,5))
  136. plt.hist(cv_list_e)
  137. plt.xlabel('CV')
  138. plt.ylabel('count')
  139. plt.title("Coefficient of Variation, Excitatory")
  141. plt.figure(figsize=(5,5))
  142. plt.hist(cv_list_i)
  143. plt.xlabel('CV')
  144. plt.ylabel('count')
  145. plt.title("Coefficient of Variation, Inhibitory")
  147. # Calculate spike-contrast synchrony measure
  148. e_synchrony, e_trace = spike_contrast(spikes_e_neo, t_start=t_start, t_stop=t_end, min_bin=0.1*q.ms, return_trace=True, bin_shrink_factor=0.9)
  149. print("Exc synch value: ", e_synchrony)
  150. i_synchrony, i_trace = spike_contrast(spikes_i_neo, min_bin=0.1*q.ms, t_start=t_start, t_stop=t_end, return_trace=True, bin_shrink_factor=0.9)
  151. print("Inh synch value: ", i_synchrony)
  154. # --------------------
  155. spikes_e = spikes_e[spikes_e["times"] <= cutoff]
  156. spikes_i = spikes_i[spikes_i["times"] <= cutoff]
  158. exc_times = spikes_e["times"].values
  159. exc_ids = spikes_e["senders"].values.astype("float")
  161. inh_times = spikes_i["times"].values
  162. inh_ids = spikes_i["senders"].values.astype("float")
  165. plt.rcParams["text.usetex"] = True
  166. fsize = 9
  167. markersize = 2
  168. mpl.rcParams.update({
  169. 'font.family': 'sans-serif', # Choose font family
  170. 'font.sans-serif': ['Arial'], # Specify a list of sans-serif fonts
  171. 'font.size': fsize, # Base font size for text
  172. 'axes.titlesize': fsize, # Font size for axes titles
  173. 'axes.labelsize': fsize, # Font size for x and y labels
  174. 'xtick.labelsize': fsize - 2, # Font size for x tick labels
  175. 'ytick.labelsize': fsize - 2, # Font size for y tick labels
  176. 'legend.fontsize': fsize # Font size for legends
  177. })
  179. fig, axs = plt.subplots(1, 2, figsize=(5.2, 2.08))
  181. axs[0].scatter(exc_times, exc_ids, marker="|", color="black", lw=0.1, s=0.5)
  182. axs[0].scatter(inh_times, inh_ids, marker="|", color="red", lw=0.1, s=0.5)
  183. axs[0].set_xlabel(r"Time (ms)")
  184. axs[0].set_ylabel(r"Neuron ID")
  186. axs[0].axhline(y=-20, color="k")
  188. # Mean activity
  189. resolution=1
  190. rates, _ = smooth_spike_trains(spikes_e, 800, sim_time=5000, resolution=resolution, cpu_count=6,
  191. kernel_width=3, out_resolution=1)
  192. rates_mean_full = np.mean(rates, axis=0)
  193. rates_mean = rates_mean_full[:int(cutoff/resolution)]
  194. max_rate = np.max(rates_mean)
  195. min_rate = np.min(rates_mean)
  196. scaler = 200/max_rate
  197. rates_mean = rates_mean*scaler - 220 # scale and offset
  198. axs[0].plot(rates_mean, "black", lw=2)
  199. # set labels for firing rate
  200. neg_ticks = [-220, -120, -20] # positions
  201. neg_labels = [int(min_rate), int(max_rate/2*1000), int(max_rate*1000)] # corresponding labels
  202. auto_ticks = axs[0].get_yticks()
  203. pos_ticks = auto_ticks[auto_ticks > 0]
  204. all_ticks = np.concatenate([neg_ticks, pos_ticks])
  205. all_labels = neg_labels + [f"{t:.0f}" for t in pos_ticks]
  206. axs[0].yaxis.set_major_locator(FixedLocator(all_ticks))
  207. axs[0].yaxis.set_major_formatter(FixedFormatter(all_labels))
  209. #inhibitory rates
  210. resolution = 1
  211. rates_i, _ = smooth_spike_trains(spikes_i, 200, sim_time=5000, resolution=resolution, cpu_count=6,
  212. kernel_width=3, out_resolution=1)
  213. rates_mean_full_i = np.mean(rates_i, axis=0)
  214. rates_mean_i = rates_mean_full_i[:int(cutoff / resolution)]
  215. rates_mean_i = rates_mean_i * scaler - 220 # scale and offset
  216. axs[0].plot(rates_mean_i, "red", lw=2)
  218. axs[0].set_xlim([0+offset, cutoff-50])
  221. # Find the average cycle length
  222. rates_mean_full_0 = rates_mean_full - np.mean(rates_mean_full)
  223. autocorr = np.correlate(rates_mean_full_0, rates_mean_full_0, mode="full")
  224. autocorr = autocorr[len(autocorr)//2:]
  225. peaks, _ = scipy.signal.find_peaks(autocorr)
  226. if len(peaks)>0:
  227. print("Average oscillation period (ms): ", peaks[0])
  228. print("And frequency (H_z): ", 1/peaks[0]*1000)
  229. else:
  230. print("No autocorrelation peaks found")
  234. # Find the average cycle length, inhibitory
  235. rates_mean_full_i_0 = rates_mean_full_i - np.mean(rates_mean_full_i)
  236. autocorr_i = np.correlate(rates_mean_full_i_0, rates_mean_full_i_0, mode="full")
  237. autocorr_i = autocorr_i[len(autocorr_i)//2:]
  238. peaks, _ = scipy.signal.find_peaks(autocorr_i)
  239. if len(peaks)>0:
  240. print("Average oscillation period inh (ms): ", peaks[0])
  241. print("And frequency inh (H_z): ", 1/peaks[0]*1000)
  242. else:
  243. print("No autocorrelation peaks found (inh)")
  245. plot_spike_contrast(e_trace, axs[1], lw=2)
  247. plt.tight_layout(pad=0.1)
  249. # Add A B
  250. from matplotlib import font_manager
  251. prop = font_manager.FontProperties(weight='extra bold')
  252. fig.text(0.03, 0.97, 'A', fontsize=10, fontproperties=prop, va='top', ha='right')
  253. fig.text(0.53, 0.97, 'B', fontsize=10, fontproperties=prop, va='top', ha='right')
  255. # add lambda
  256. fig.text(0.03, 0.33, r"$\lambda$ (Hz)", fontsize=fsize, va='top', ha='right', rotation="vertical")
  258. plt.savefig('results/{}/raster_burst_{}_{}.eps'.format(dir_name, dir_name, name_end), format='eps', dpi=600)
  259. plt.savefig('results/{}/raster_burst_{}_{}.png'.format(dir_name, dir_name, name_end), format='png', dpi=600)
  261. plt.figure()
  262. plt.plot(np.arange(0, 5000)[:500], autocorr[:500])
  263. plt.show()

plot_raster_synchrony.py at commit 99aa557, under Apache-2.0 · at the source

Overview

Authors: Iiro Ahokainen1, Marja-Leena Linne1
ORCID iDs: Iiro Ahokainen, Marja-Leena Linne
  1. Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
Institutions: Tampere University (Finland); University of Tampere (Finland)
Journal: PLoS computational biology, volume 22, issue 9, article e1014730
Dates: received 8 November 2025; accepted 17 August 2026; published online 2 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014730 · PMID 42685137 · PMCID PMC13581215 · OpenAlex W4416026038
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.07.687160
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism)
Methods: Machine learning, Single-unit activity, calcium imaging
MeSH: Models, Neurological*, Nerve Net*, Neuronal Plasticity*, Action Potentials, Animals, Computational Biology, Computer Simulation, Humans, Neurons, Recurrent Neural Networks, Synapses, Visual Cortex (* major topic)
Journal subjects: Biology and Life Sciences, Neuroscience, Cellular Neuroscience, Synaptic Plasticity, Developmental Neuroscience, Anatomy, Nervous System, Synapses, Medicine and Health Sciences, Physiology, Electrophysiology, Neurophysiology, Cell Biology, Cellular Types, Animal Cells, Neurons, Neuronal Plasticity, Computer and Information Sciences, Neural Networks, Membrane Potential, Action Potentials, Mental Health and Psychiatry, Mood Disorders, Depression, Cognitive Science, Cognitive Neuroscience, Working Memory, Cognition, Memory, Learning and Memory
Topic: Neural Networks and Reservoir Computing (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Opetus- ja Kulttuuriministeriö (VN/3137/2024-OKM-6)
Citations: not cited yet (Europe PMC); 97 references in the paper

Abstract

Activity-dependent synaptic plasticity is a fundamental learning mechanism that shapes the connectivity and activity of neural circuits. Existing computational models of Spike-Timing-Dependent Plasticity (STDP) capture long-term synaptic changes with varying degrees of biological detail. A common approach is to neglect the influence of short-term dynamics on long-term plasticity, which may be an oversimplification for certain neuron types. Thus, there is a need for new models to investigate how short-term dynamics influence long-term plasticity. To address this gap, we introduce a novel phenomenological model, the Short-Long-Term STDP (SL-STDP) rule, which directly integrates the Tsodyks-Markram model of short-term dynamics with postsynaptic long-term plasticity. We fit the new model to recordings from layer 5 of the visual cortex and study how short-term plasticity affects the firing rate frequency dependence of long-term plasticity in a single synapse. Our analysis revealed that the pre- and postsynaptic frequency dependence of long-term plasticity plays a crucial role in shaping the self-organization of recurrent neural networks (RNNs) and their information processing through the emergence of sink and source nodes. We applied the SL-STDP rule to RNNs and found that neurons in the SL-STDP network self-organize into distinct firing rate clusters, stabilizing the dynamics. We extended the experiments by including homeostatic balancing, namely weight normalization and excitatory-to-inhibitory plasticity, and observed differences in degree correlations between the SL-STDP network and a network without direct coupling between short-term and long-term plasticity. Finally, we evaluated how the modified connectivity affects the networks’ information capacity in reservoir computing tasks. The SL-STDP rule outperformed the uncoupled system in the majority of tasks, and including excitatory-to-inhibitory facilitating synapses further improved information capacity. Our study demonstrates that short-term dynamics–induced changes in the frequency dependence of long-term plasticity play a pivotal role in shaping network dynamics and link synaptic mechanisms to information processing in RNNs.

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

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Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

IiroAhokainen/SL-STDP

License: Apache-2.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 99aa5577ac5ce87647ecce7ceb30d36c37b10011, 12 August 2026
Languages: Python (22), Shell (5)
Size: 58 files, 27 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, environment (requirements.txt), tests
Not found: CITATION.cff, continuous integration, documentation
Tools: NumPy (20 files), Matplotlib (13 files), pandas (12 files), NEST Simulator (5 files), scikit-learn (4 files), SciPy (4 files), Elephant (3 files), Neo (3 files), NetworkX (2 files), seaborn (2 files), SymPy (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
29 files
At the source: github.com/IiroAhokainen/SL-STDP

Zenodo 22011941

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 Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (20 files), Matplotlib (13 files), pandas (12 files), NEST Simulator (5 files), scikit-learn (4 files), SciPy (4 files), Elephant (3 files), Neo (3 files), NetworkX (2 files), seaborn (2 files), SymPy (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
29 files
At the source: doi.org/10.5281/zenodo.22011941

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

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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;
  • 54 scripts, each with its path and the digest of its content;
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Data

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

Data Availability

All data and code used for running experiments, model fitting, and plotting is available on a GitHub repository at https://github.com/IiroAhokainen/SL-STDP. Additionally, Zenodo entry is available at https://doi.org/10.5281/zenodo.22011941.

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 12 MeSH terms, 1 funder, 91 references.

Cite

This paper

Ahokainen, I., & Linne, M.-L. (2026). A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity. PLoS computational biology, 22(9), e1014730. https://doi.org/10.1371/journal.pcbi.1014730

BibTeX

@article{ahokainen2026unified,
author = {Ahokainen, Iiro and Linne, Marja-Leena},
title = {{A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity}},
journal = {PLoS computational biology},
year = {2026},
month = sep,
volume = {22},
number = {9},
pages = {e1014730},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014730},
url = {https://doi.org/10.1371/journal.pcbi.1014730},
pmid = {42685137},
pmcid = {PMC13581215}
}

RIS

TY - JOUR
AU - Ahokainen, Iiro
AU - Linne, Marja-Leena
TI - A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/09/02
VL - 22
IS - 9
SP - e1014730
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014730
UR - https://doi.org/10.1371/journal.pcbi.1014730
LA - en
ER -

CSL-JSON

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Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience
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[3] doi:10.1371/journal.pcbi.1014752 [code]
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Journal: PLoS computational biology
In common: Elephant, Neo, pandas, 3 other tools, computational modeling (no new data), 3 references
[4] doi:10.1371/journal.pcbi.1014283 [code]
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Journal: PLoS computational biology
In common: Elephant, SymPy, Neo, 4 other tools, computational modeling (no new data)
[5] doi: [code]
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Journal: bioRxiv : the preprint server for biology
In common: Elephant, Neo, NetworkX, 6 other tools
[6] doi:10.1038/s41467-026-74460-8 [code]
Spike-based alignment learning solves the weight transport problem.
Journal: Nature communications
In common: seaborn, scikit-learn, pandas, 3 other tools, computational modeling (no new data), 4 references
[7] doi:10.1038/s41467-026-74466-2 [code]
Neuromorphic hierarchical modular reservoirs.
Journal: Nature communications
In common: NetworkX, seaborn, scikit-learn, 4 other tools, 3 references
[8] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: NEST Simulator, NetworkX, seaborn, 5 other tools
[9] doi:10.1093/pnasnexus/pgag213 [code]
Two-factor synaptic plasticity enables memory consolidation during neuronal burst firing.
Journal: PNAS nexus
In common: 6 references
[10] doi:10.7554/elife.110588 [code]
Opening the black box toward a modular approach to spike sorting.
Journal: eLife
In common: Neo, NetworkX, seaborn, 5 other tools

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