OSCR

Role of fast-spiking interneurons in modulating across-trial variability and within-trial correlations in the striatum.

Code ↔ Paper

7 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 7 matches
  1. [1] § Methods › Input model ↔ runcor.py, lines 71 to 118 · score 0.65 · overlapping cortical, spike trains, cortical source, Poisson, connection, neuron · Report a mismatch
  2. [2] § Statistical analysis › Downstream effect ↔ rungpe.py, lines 40 to 105 · score 0.62 · spike generator, GPe neuron, Poisson, lognormal, noise, uniform · Report a mismatch
  3. [3] § Methods › Stimulus-evoked activities ↔ runcor.py, lines 71 to 118 · score 0.61 · partially overlapped, spike trains, cortical sources, FSI, MSNs · Report a mismatch
  4. [4] § Methods › Connectivity model ↔ runcor.py, lines 168 to 217 · score 0.59 · fixed indegree, feedforward inhibition, autapse, multapses, connections, model · Report a mismatch
  5. [5] § Methods › Neuron model ↔ rungpe.py, lines 40 to 105 · score 0.58 · postsynaptic neuron, membrane potential, reset, events, model, GPe · Report a mismatch
  6. [6] § Methods › Connectivity model ↔ runcor.py, lines 168 to 217 · score 0.57 · FSI MSN connection, recurrent inhibition, delays, model, cortical · Report a mismatch
  7. [7] § Statistical analysis › Downstream effect ↔ runcor.py, lines 120 to 166 · score 0.52 · background noise, spike generator, Poisson, cortical, neuron, MSN · Report a mismatch

Paper

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The authors' code

Python · 308 lines · 11 KB · no license · 5 matches

  1. """ A cortical neuron motif simulator
  2. """
  4. # system
  5. import time
  6. import os
  8. # tools
  9. import argparse
  10. import json
  11. import pandas as pd
  13. # computation libs
  14. import numpy as np
  16. # plot
  17. import matplotlib.pyplot as plt
  18. from lib.mviz import visSpk, visCurve, multiPlot
  19. from lib.mcalc import gcf2ann
  21. # simulation libs
  22. from lib import params
  23. import nest
  25. def main():
  26. """Interneuron motif PC-PV-SOM-VIP
  27. """
  28. # create argument parser
  29. parser = argparse.ArgumentParser(description='Striatal Microcircuit.')
  30. # profiling
  31. parser.add_argument('--dpath', type=str, help='data path', default='./data')
  32. parser.add_argument('--fpath', type=str, help='data path', default='./plot')
  33. # simulation setting
  34. parser.add_argument('--epoch', type=int, help='number of epochs', default=2)
  35. parser.add_argument('--T', type=int, help='simulation time', default=1000)
  36. parser.add_argument('--Nm', type=int, help='number of MSNs', default=2500)
  37. parser.add_argument('--Nf', type=int, help='number of FSIs', default=0)
  38. # input setting
  39. parser.add_argument('--W', type=float, help='within-pool correlation', default=0.1)
  40. parser.add_argument('--B', type=float, help='between-pool correlation', default=0.1)
  41. parser.add_argument('--D', type=float, help='inhibitory synapse delay', default=2.0)
  42. parser.add_argument('--delay-width', type=float, help='half-width for uniform delay jitter (ms)', default=1.0)
  43. parser.add_argument('--weight-sigma', type=float, help='sigma for lognormal weight jitter', default=0.5)
  44. parser.add_argument('--nthreads', type=int, help='number of local NEST threads', default=1)
  45. # neuron setting
  46. parser.add_argument('--ntype', type=str, help='neuron type', default='iaf_cond_alpha')
  47. # visualize
  48. parser.add_argument('--viz', action='store_true', default=False)
  50. # parsing
  51. print('Parsing arguments ... ... ')
  52. # parse argument
  53. args = parser.parse_args()
  54. simtime = args.T
  55. # paths
  56. mode = 'rec_0_str'
  57. suffixpath = '/%s/Nf%d-W%s-B%s/'%(mode, args.Nf, args.W, args.B)
  58. recpath = args.dpath + suffixpath # path for saving recordings (gdf files)
  59. os.makedirs(recpath, exist_ok=True)
  60. if args.viz:
  61. figpath = args.fpath + suffixpath # path for saving figures
  62. spkpath = figpath + 'spk/'
  63. ratpath = figpath + 'rat/'
  64. wtspath = figpath + 'wt/'
  65. for path in [spkpath, ratpath, wtspath]:
  66. os.makedirs(path, exist_ok=True)
  68. print('Simulation start ... ... ')
  69. print(suffixpath)
  71. #* Motif network
  72. # neurons
  73. nTypes = ['MSN', 'FSI']
  74. nNums = [args.Nm, args.Nf]
  75. paramMSN = params.paramMSN
  76. paramFSI = params.paramFSI
  78. MSN_deg = int(nNums[0]/10)
  79. FSI_deg = int(nNums[0]/100*0.6)
  80. CTX_deg = 100
  82. allow_autapses = True
  83. allow_multapses = True
  85. # input
  86. print('inputs')
  87. B, W = args.B, args.W
  89. # generate input spike trains
  90. msd = 4294967295
  91. nest.ResetKernel()
  92. nest.set_verbosity("M_WARNING")
  93. nest.set(resolution=0.1, rng_seed=msd)
  95. if W > 0:
  96. nstm = int(CTX_deg / W)
  97. # partially overlapped cortical sources
  98. stm = nest.Create('poisson_generator', int(nstm*(2-B)), params={'rate': 10.0})
  99. # input recording
  100. inDetector = nest.Create('spike_recorder', 1)
  101. nest.Connect(stm, inDetector)
  102. # run simulation
  103. nest.Simulate(simtime)
  104. # get spike trains
  105. inSpks = nest.GetStatus(inDetector, 'events')[0]
  107. # # visualize input spikes
  108. # plt.figure(figsize=(6,4))
  109. # visSpk(inSpks['times'], inSpks['senders'], path='./input')
  110. # assert False
  112. print(np.unique(inSpks['senders']).size)
  113. print(inSpks)
  115. Js = [ [2.0, 1.0],
  116. [4.8, 0.5],
  117. [0.03, 0.],
  118. [0.5, 0.]]
  120. # reset kernel
  121. msd = int(time.time() * 1000.0) % 4294967295
  122. nest.ResetKernel()
  123. nest.set_verbosity("M_WARNING")
  124. nest.set(resolution=0.1, rng_seed=msd)
  125. # set number of local threads to speed up NEST operations
  126. try:
  127. nest.SetKernelStatus({'local_num_threads': max(1, int(args.nthreads))})
  128. except Exception:
  129. pass
  131. # neurons
  132. print('create neurons')
  133. # scale = 1/30
  134. # scale = 1/60
  135. scale = 0
  136. # heterogeneous parameters
  137. param_keys = ['C_m', 'tau_syn_in', 't_ref', 'V_th', 'g_L']
  139. nPops = {}
  140. nPops['MSN'] = nest.Create('iaf_cond_alpha', nNums[0])
  141. if scale > 0:
  142. sample_msn = params.sample_params_normal(paramMSN, designated_keys=param_keys, scale=scale, n_samples=nNums[0])
  143. nest.SetStatus(nPops['MSN'], sample_msn)
  144. else:
  145. nest.SetStatus(nPops['MSN'], paramMSN)
  147. if nNums[-1] > 0:
  148. nPops['FSI'] = nest.Create('iaf_cond_alpha', nNums[-1])
  149. if scale > 0:
  150. sample_fsi = params.sample_params_normal(paramFSI, designated_keys=param_keys, scale=scale, n_samples=nNums[-1])
  151. nest.SetStatus(nPops['FSI'], sample_fsi)
  152. else:
  153. nest.SetStatus(nPops['FSI'], paramFSI)
  155. # background noise
  156. bkgF = nest.Create('poisson_generator', 1, {'rate': 5.75e3})
  157. if args.Nf == 0 and args.W > 0:
  158. bkgM = nest.Create('poisson_generator', 1, {'rate': 3.2e3})
  159. else:
  160. bkgM = nest.Create('poisson_generator', 1, {'rate': 5.95e3})
  161. # cortical input
  162. if W > 0:
  163. stm = nest.Create('spike_generator', int(nstm*(2-B)))
  164. for i in range(int(nstm*(2-B))):
  165. spks = inSpks['times'][inSpks['senders'] == (i + 1)]
  166. stm[i].spike_times = np.concatenate([spks + e*simtime for e in range(args.epoch)])
  168. # connections
  169. print('generating connections')
  170. # background input (excitatory)
  171. nest.Connect(bkgM, nPops['MSN'], syn_spec={'weight': Js[0][0], 'delay': 1.0})
  172. if args.Nf > 0:
  173. nest.Connect(bkgF, nPops['FSI'], syn_spec={'weight': Js[0][-1], 'delay': 1.0})
  174. if W > 0:
  175. # cortical stimulus (excitatory)
  176. con_spec = {'rule': 'fixed_indegree', 'indegree': CTX_deg, 'allow_multapses': allow_multapses}
  177. syn_spec ={'synapse_model': 'static_synapse', 'weight': Js[1][0], 'delay': 1.0}
  178. nest.Connect(stm[:nstm], nPops['MSN'][:nNums[0]//2], con_spec, syn_spec)
  179. nest.Connect(stm[-nstm:], nPops['MSN'][nNums[0]//2:], con_spec, syn_spec)
  180. if args.Nf > 0:
  181. syn_spec ={'synapse_model': 'static_synapse', 'weight': Js[1][-1]/2, 'delay': 1.0}
  182. nest.Connect(stm[:nstm], nPops['FSI'], con_spec, syn_spec)
  183. nest.Connect(stm[-nstm:], nPops['FSI'], con_spec, syn_spec)
  185. # Recurrent inhibitory MSN→MSN connections
  186. con_spec = {
  187. 'rule': 'fixed_indegree',
  188. 'indegree': MSN_deg,
  189. 'allow_autapses': False,
  190. 'allow_multapses': allow_multapses
  191. }
  192. # Compute lognormal mu
  193. mu_msn = np.log(abs(Js[2][0])) - 0.5 * args.weight_sigma**2
  194. syn_spec = {
  195. "synapse_model": "static_synapse",
  196. "weight": -1.0 * nest.random.lognormal(mean=mu_msn, std=args.weight_sigma),
  197. "delay": nest.random.uniform(
  198. min=max(0.1, args.D - args.delay_width),
  199. max=args.D + args.delay_width
  200. ),
  201. }
  202. nest.Connect(nPops["MSN"], nPops["MSN"], con_spec, syn_spec)
  204. # Feedforward inhibitory FSI→MSN connections
  205. if args.Nf > 0:
  206. con_spec = {'rule': 'fixed_indegree', 'indegree': FSI_deg, 'allow_multapses': allow_multapses, 'allow_autapses': allow_autapses}
  207. # Compute lognormal mu
  208. mu_fsi = np.log(abs(Js[-1][0])) - 0.5 * args.weight_sigma**2
  209. syn_spec = {
  210. "synapse_model": "static_synapse",
  211. "weight": -1.0 * nest.random.lognormal(mean=mu_fsi, std=args.weight_sigma),
  212. "delay": nest.random.uniform(
  213. min=max(0.1, args.D - args.delay_width),
  214. max=args.D + args.delay_width
  215. ),
  216. }
  217. nest.Connect(nPops["FSI"], nPops["MSN"], con_spec, syn_spec)
  219. # record device
  220. print('link devices')
  221. # 'record_to': 'ascii'
  222. spikeDetector = nest.Create('spike_recorder', 1, {'label':recpath + 'spk'})
  223. for npop in nPops.values():
  224. nest.Connect(npop, spikeDetector)
  225. # subthreshold recording
  226. if args.Nf == 0:
  227. del nTypes[-1]
  228. del nNums[-1]
  230. # Select 10 neurons per type
  231. subNeurons = [nPops[ntype][:25] for ntype in nTypes]
  232. mulDetects = [
  233. nest.Create('multimeter', params={'record_from': ['V_m', 'g_ex', 'g_in'], 'interval': 0.1}, n=len(subNeurons[i]))
  234. for i, ntype in enumerate(nTypes)
  235. ]
  236. for neurons, detects in zip(subNeurons, mulDetects):
  237. for det, nrn in zip(detects, neurons):
  238. nest.Connect(det, nrn)
  240. # simulation
  241. print('simulating')
  242. initstates = [np.random.uniform(-70., -55., len(npop)) for npop in nPops.values()]
  243. for e in range(args.epoch):
  244. # initial state
  245. for npop, rvs in zip(nPops.values(), initstates):
  246. nest.SetStatus(npop, params='V_m', val=rvs.tolist())
  248. # simulate
  249. nest.Simulate(simtime)
  251. # spikes
  252. spikeEvents = nest.GetStatus(spikeDetector, 'events')[0]
  253. print('number of spikes: ', len(spikeEvents['times']))
  254. spikeTimes, spikeIds = spikeEvents['times'], spikeEvents['senders']
  256. # visualize
  257. print('visualize')
  258. if args.viz:
  259. # # input spikes
  260. # if W > 0:
  261. # pulseEvents = nest.GetStatus(inDetector, 'events')[0]
  262. # pulseTimes, pulseIds = pulseEvents['times'], pulseEvents['senders']
  263. # visSpk(pulseTimes, pulseIds-nNums[0], path=spkpath + 'input')
  265. mask = (spikeIds > nNums[0]-100) & (spikeIds <= nNums[0] + 100)
  266. visSpk(spikeTimes[mask], spikeIds[mask]-nNums[0]+100, path=spkpath + 'spk')
  268. # membrane potentials
  269. multiEvents = []
  270. for detects in mulDetects: # mulDetects is a list of lists
  271. events_per_type = [nest.GetStatus(det)[0]['events'] for det in detects]
  272. multiEvents.append(events_per_type)
  273. multiPlot(multiEvents, [0., simtime], nTypes, ratpath + 'vg_')
  275. # # firing rates
  276. # nNums = [nNums[0]//2, nNums[0]//2, args.Nf]
  277. # nTypes = ['M1', 'M2', 'FSI']
  278. # if args.Nf == 0:
  279. # del nNums[-1]
  280. # del nTypes[-1]
  281. # binsize = 100
  282. # tmBins = np.arange(0, simtime*args.epoch+1, binsize)
  283. # idBins = np.concatenate([[0], np.cumsum(nNums)]) + 1
  284. # ratesPop = np.histogram2d(spikeTimes, spikeIds, bins=[tmBins, idBins])[0] * 1e3 / binsize / nNums
  285. # visCurve([tmBins[:-1]]*len(nTypes), ratesPop.T,
  286. # 'time (ms)', 'rate (Hz)', 'firing rate', ratpath + 'fr_', nTypes)
  287. # print('mean firing rate: ', np.mean(ratesPop, axis=0))
  289. # save data
  290. np.savez(recpath + 'spk.npz', spikeIds, spikeTimes)
  292. # Save multimeter recordings
  293. data_dict = {}
  294. for type_idx, detects in enumerate(mulDetects):
  295. for det_idx, det in enumerate(detects):
  296. events = nest.GetStatus(det)[0]['events']
  297. key_prefix = f"{nTypes[type_idx]}_neuron{det_idx}"
  298. for key, values in events.items():
  299. data_dict[f"{key_prefix}_{key}"] = values
  300. filename = recpath + "mem.npz"
  301. np.savez(filename, **data_dict)
  303. print('done')
  305. if __name__ == '__main__':
  306. start_time = time.time()
  307. main()
  308. print("--- %s seconds ---" % (time.time() - start_time))

runcor.py at commit d387bdf, no license · at the source

Overview

Authors: Lihao Guo1,2,3, Arvind Kumar1,2,4
ORCID iDs: Lihao Guo, Arvind Kumar
  1. Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden
  2. Science for Life Laboratory, Stockholm, Sweden
  3. Current affiliation: Okinawa Institute of Science and Technology, Okinawa, Japan
  4. Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden
Institutions: Okinawa Institute of Science and Technology Graduate University (Japan); Science for Life Laboratory (Sweden); Digital Futures (Sweden); KTH Royal Institute of Technology (Sweden)
Journal: PLoS computational biology, volume 22, issue 3, article e1014099
Dates: received 19 June 2025; accepted 7 March 2026; published online 27 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014099 · PMID 41894433 · PMCID PMC13095104 · OpenAlex W7141228404
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: clinical / translational (subfield)
Methods: Single-unit activity, calcium imaging
MeSH: Action Potentials*, Corpus Striatum*, Interneurons*, Models, Neurological*, Animals, Computational Biology, Medium Spiny Neurons, Nerve Net (* major topic)
Topic: Deep Brain Stimulation and Movement Disorders (Clinical Neurology, Medicine), according to OpenAlex
Funding: KTH Digital Futures (NA); Vetenskapsrådet (STRATNEURO, SeRC, ‭2018 03118‬); Institute of Advanced Studies, University of Strasbourg (NA)
Citations: cited by 1 paper (Europe PMC); 43 references in the paper

Abstract

The striatum comprises a network characterized by a highly shared feedforward inhibition (FFI) mediated by fast-spiking interneurons (FSI), which constitute only 1% of the striatal population. We investigated the dynamical consequences of this extensively shared FFI beyond inducing synchrony in a local striatal microcircuit. Our findings reveal that increased FFI sharing enhances the across-trial variability of striatal responses, activity of medium spiny neurons (MSNs), to cortical inputs, and endows the striatal network with the capacity to modulate output correlations in a bidirectional manner. Specifically, weakly shared cortical inputs become more correlated, whereas strongly shared cortical inputs are decorrelated in the presence of FSIs. These dynamic modulatory effects on MSNs, in turn, substantially alter the spiking statistics of downstream neurons in the globus pallidus, regarding across-trial variability and burstiness.

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 7 matches between paragraphs and lines of code.

weirdoglh/FSInet

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: d387bdf86a96a898f9f4e2d8a75bf52943558208, 22 December 2025
Languages: Python (5), Jupyter (2), Shell (2)
Size: 44 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (7 files), Matplotlib (5 files), NEST Simulator (3 files), pandas (3 files), SciPy (2 files), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
10 files
At the source: github.com/weirdoglh/FSInet

Code availability

The code for simulation and plotting is shared on GitHub https://github.com/weirdoglh/FSInet.

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

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 9 scripts, each with its path and the digest of its content;
  • 7 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

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

Data Availability

Data availability Processed simulated data is shared on GitHub https://github.com/weirdoglh/FSInet. Code availability The code is available on GitHub https://github.com/weirdoglh/FSInet.

Reproduced under the paper's license (CC BY), 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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 8 MeSH terms, 3 funders, 42 references.

Cite

This paper

Guo, L., & Kumar, A. (2026). Role of fast-spiking interneurons in modulating across-trial variability and within-trial correlations in the striatum. PLoS computational biology, 22(3), e1014099. https://doi.org/10.1371/journal.pcbi.1014099

BibTeX

@article{guo2026role,
author = {Guo, Lihao and Kumar, Arvind},
title = {{Role of fast-spiking interneurons in modulating across-trial variability and within-trial correlations in the striatum}},
journal = {PLoS computational biology},
year = {2026},
month = mar,
volume = {22},
number = {3},
pages = {e1014099},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014099},
url = {https://doi.org/10.1371/journal.pcbi.1014099},
pmid = {41894433},
pmcid = {PMC13095104}
}

RIS

TY - JOUR
AU - Guo, Lihao
AU - Kumar, Arvind
TI - Role of fast-spiking interneurons in modulating across-trial variability and within-trial correlations in the striatum
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/03/27
VL - 22
IS - 3
SP - e1014099
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014099
UR - https://doi.org/10.1371/journal.pcbi.1014099
LA - en
ER -

CSL-JSON

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"id": "10.1371/journal.pcbi.1014099",
"type": "article-journal",
"title": "Role of fast-spiking interneurons in modulating across-trial variability and within-trial correlations in the striatum",
"container-title": "PLoS computational biology",
"author": [
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"family": "Guo",
"given": "Lihao"
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{
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"given": "Arvind"
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],
"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "3",
"page": "e1014099",
"DOI": "10.1371/journal.pcbi.1014099",
"PMID": "41894433",
"PMCID": "PMC13095104",
"ISSN": "1553-734X",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pcbi.1014099",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
27
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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Journal: eLife
In common: seaborn, pandas, SciPy, 2 other tools, author Arvind Kumar
[10] doi:10.1371/journal.pcbi.1013569 [code]
Mouse and human striatal projection neurons compared - somatodendritic arbor, spines and in silico analyses.
Journal: PLoS computational biology
In common: seaborn, pandas, SciPy, 2 other tools, 3 references

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