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Neural subspace reorganization reflects value-based decision-making.

Code ↔ Paper

3 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 3 matches
  1. [1] § STAR★Methods › Method details › Single-neuron selectivity and population overlap analysis ↔ neural_selectivity_analysis/neural_selectivity_pre_decision.ipynb, lines 22–84 · score 0.65 · pre decision, linear regression, selectivity, model, fitted, neuron
  2. [2] § STAR★Methods › Method details › Decoding analysis ↔ decoding_analysis/information_decoding.ipynb, lines 193–258 · score 0.59 · linear discriminant, fold, decoder, trained, accuracy, scored
  3. [3] § STAR★Methods › Quantification and statistical analysis ↔ state_space_analysis/subspace_alignment.ipynb, lines 232–343 · score 0.55 · subspace alignment, 1.8 s, 3.2 s, pseudopopulation

Paper

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

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The authors' code

Jupyter notebook · 90 lines · 3.3 KB · no license · 1 match

  1. # %%
  2. import numpy as np
  3. import matplotlib.pyplot as plt
  4. import os
  5. from os import listdir
  6. import pandas as pd
  7. from sklearn.linear_model import Lasso, LinearRegression
  8. import statsmodels.api as sm
  9. import statsmodels.formula.api as smf
  10. from statsmodels.stats.multitest import multipletests
  12. # %%
  13. def t_to_idx(t):
  14. return int((t-(-2))/0.05)
  15. def z_score(X):
  16. mean = np.mean(X, axis=0)[np.newaxis,:]
  17. sd = np.std(X, axis=0)[np.newaxis,:]
  18. sd[sd==0] = 1
  20. return (X-mean)/sd
  22. # %%
  23. subject = 'Tir'
  25. trial_type = 'correct'
  26. path = f'/Users/huidili/Desktop/wmupdate/analysis_data/{trial_type}_trials/'
  27. area_path = path+'area_df/'
  29. for filename in listdir(area_path):
  30. if ('area' in filename):
  31. if 'ISA' in filename:
  32. sub_name, session_num, _, _ = filename.split('_')
  33. session_name = sub_name+'_'+session_num
  34. elif 'Tir' in filename:
  35. session_name = filename.split('_')[0]
  37. if (subject == 'Tir' and 'Tir' in session_name) or (subject == 'ISA' and 'ISA' in session_name):
  38. condition_path = path+'condition_df/'
  39. label_path = path+'labels/'
  41. spk_path = path+'spike_rate/150bin_50step/'
  42. spk_data = np.load(spk_path+session_name+'_spike_rate.npz')
  43. spike_rate = spk_data['spike_rate']
  45. area_df = pd.read_pickle(area_path+session_name+'_area_df.pkl')
  46. condition_df = pd.read_pickle(condition_path+session_name+'_condition_df.pkl')
  47. label_data = np.load(label_path+session_name+'_labels.npy')
  49. ## select PFC neurons and intermediate T1 trials for selectivity analysis
  50. sel_idx = (condition_df['retain']==True) | (condition_df['update']==True)
  51. sel_spike_rate = spike_rate[sel_idx][:,area_df['PFC']]
  52. sel_label = label_data[:,sel_idx]
  53. n_neurons = sel_spike_rate.shape[1]
  55. start_t = t_to_idx(1.8)
  56. end_t = t_to_idx(2.4)
  57. n_time = int(end_t-start_t)
  59. z_spike_rate = z_score(sel_spike_rate)
  60. coef_array = np.zeros((n_neurons, 6))
  61. pvalue_array = np.zeros((n_neurons, 6))
  63. # linear regression for each neuron
  64. for n in range(n_neurons):
  65. df = pd.DataFrame({
  66. 'r': np.mean(z_spike_rate[:,n,start_t:end_t], axis=-1),
  67. 'A': sel_label[0],
  68. 'B': sel_label[1]
  69. })
  71. ols_model = smf.ols('r ~ C(A) + C(B)', data=df).fit()
  72. pvalue_array[n] = ols_model.pvalues.iloc[1:]
  73. coef_array[n] = ols_model.params.iloc[1:]
  75. sig_T1_neurons = (pvalue_array[:,:3]<0.05).any(axis=-1)
  76. sig_T2_neurons = (pvalue_array[:,3:]<0.05).any(axis=-1)
  78. mix_idx = np.where(sig_T1_neurons & sig_T2_neurons)[0]
  79. T1_idx = np.where(sig_T1_neurons)[0]
  80. T2_idx = np.where(sig_T2_neurons)[0]
  82. # save data
  83. np.savez(f'/Users/huidili/Desktop/wmupdate/analysis_result/selectivity/{subject}/pre_decision/{session_name}_ols_params_pvalues.npz', params=coef_array, pvalues=pvalue_array)
  84. np.savez(f'/Users/huidili/Desktop/wmupdate/analysis_result/selectivity/{subject}/pre_decision/{session_name}_selective_neuron_index', n_neurons=n_neurons, T1=T1_idx, T2=T2_idx, mix=mix_idx)
  86. # %%
  89. # %%

neural_selectivity_pre_decision.ipynb at commit f0c261e, no license · at the source

Overview

Authors: Huidi Li1, Nikolaos Chrysanthidis1, Scott L Brincat1, Jonas Rose1,2, Earl K Miller1
ORCID iDs: Huidi Li, Nikolaos Chrysanthidis, Earl K Miller
  1. The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  2. Neural Basis of Learning, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
Institutions: Massachusetts Institute of Technology (United States); Ruhr University Bochum (Germany)
Journal: iScience, volume 29, issue 10, article 117492
Dates: received 11 February 2026; accepted 25 August 2026; published online 9 September 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.117492 · PMID 42757072 · PMCID PMC13584006 · OpenAlex W7212073756
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: value-based decision making, working memory, neural population analysis, prefrontal cortex, subspace coding
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NEI NIH HHS (R01 EY033430)
Citations: not cited yet (Europe PMC); 83 references in the paper
Research resources: Macaca mulatta RRID:NCBITaxon_9544, MATLAB RRID:SCR_001622, scikit-learn RRID:SCR_002577, Python 3 RRID:SCR_008394, statsmodels RRID:SCR_016074

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

hdl1115/wmUpdate

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: f0c261e972922b224ee71e70ac5050eff1442448, 15 June 2026
Languages: Jupyter (4)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, 4 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (4 files), NumPy (4 files), pandas (4 files), scikit-learn (4 files), statsmodels (2 files), SciPy (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
5 files
At the source: github.com/hdl1115/wmUpdate

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

Tracing map

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  • 4 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: hdl1115/wmUpdate
  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1016/j.isci.2026.117492.

Versions

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Version 3, 28 September 2026

  • Authors: added Huidi Li (0009-0005-7621-3031); Nikolaos Chrysanthidis (0009-0005-3091-7586); Earl K Miller (0000-0002-0582-6958); removed Huidi Li; Nikolaos Chrysanthidis; Earl K Miller

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 1 funder, 82 references, 5 RRIDs.

Cite

This paper

Li, H., Chrysanthidis, N., Brincat, S. L., Rose, J., & Miller, E. K. (2026). Neural subspace reorganization reflects value-based decision-making. iScience, 29(10), 117492. https://doi.org/10.1016/j.isci.2026.117492

BibTeX

@article{li2026neural,
author = {Li, Huidi and Chrysanthidis, Nikolaos and Brincat, Scott L and Rose, Jonas and Miller, Earl K},
title = {{Neural subspace reorganization reflects value-based decision-making}},
journal = {iScience},
year = {2026},
month = sep,
volume = {29},
number = {10},
pages = {117492},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117492},
url = {https://doi.org/10.1016/j.isci.2026.117492},
pmid = {42757072},
pmcid = {PMC13584006}
}

RIS

TY - JOUR
AU - Li, Huidi
AU - Chrysanthidis, Nikolaos
AU - Brincat, Scott L
AU - Rose, Jonas
AU - Miller, Earl K
TI - Neural subspace reorganization reflects value-based decision-making
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/09/09
VL - 29
IS - 10
SP - 117492
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117492
UR - https://doi.org/10.1016/j.isci.2026.117492
LA - en
ER -

CSL-JSON

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