Sustained activity of human substantia nigra neurons reflect prior rewards.
The 10 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
- [1] § STAR★Methods › Method details › Model implementation and inference ↔ modeling/likelihood_functions/lik_M2WSLS_v1.m, the whole file · a weak match · score 0.77 · negative log likelihood, lose shift, choice probabilities, Win stay, models, Rewards · Report a mismatch
- [2] § STAR★Methods › Method details › Computational modeling of behavior ↔ modeling/simulation_functions/simulate_M3RescorlaWagner_v1_ZI.m, lines 11 to 36 · score 0.66 · Rescorla Wagner, prediction error, simulated, SoftMax, RPE, model · Report a mismatch
- [3] § STAR★Methods › Method details › Model implementation and inference ↔ modeling/likelihood_functions/lik_M1random_v1.m, the whole file · a weak match · score 0.65 · negative log likelihood, choice probabilities, M1, fitted, models · Report a mismatch
- [4] § STAR★Methods › Method details › Computational modeling of behavior ↔ modeling/simulation_functions/simulate_M3RescorlaWagner_v1.m, lines 11 to 36 · score 0.65 · Rescorla Wagner, prediction error, simulated, SoftMax, model, reward · Report a mismatch
- [5] § STAR★Methods › Method details › Computational modeling of behavior ↔ modeling/fitting_functions/fit_all_v1.m, lines 8 to 26 · score 0.63 · better fit, model parameters, Rescorla Wagner · Report a mismatch
- [6] § STAR★Methods › Method details › Computational modeling of behavior ↔ modeling/fitting_functions/fit_all_v1_ZI.m, lines 8 to 30 · score 0.63 · better fit, model parameters, Rescorla Wagner · Report a mismatch
- [7] § STAR★Methods › Method details › Model implementation and inference ↔ modeling/simulation_functions/simulate_M3RescorlaWagner_v1.m, lines 11 to 36 · score 0.58 · Rescorla Wagner, choice probabilities, M3, softmax, model · Report a mismatch
- [8] § STAR★Methods › Method details › Model implementation and inference ↔ modeling/simulation_functions/simulate_M3RescorlaWagner_v1_ZI.m, lines 11 to 36 · score 0.58 · Rescorla Wagner, choice probabilities, M3, softmax, model · Report a mismatch
- [9] § STAR★Methods › Method details › Model implementation and inference ↔ modeling/fitting_functions/fit_M3RescorlaWagner_v1.m, lines 5 to 26 · score 0.56 · Bayesian Information Criterion, LL, likelihood, RW, BIC, model · Report a mismatch
- [10] § STAR★Methods › Method details › Electrophysiological data collection ↔ N2_Standalone_textGUI_AlphaOmega.m, lines 38 to 90 · score 0.51 · Alpha Omega, threshold, signal, clusters, spiking · Report a mismatch
Paper
Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC
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The authors' code
MATLAB · 37 lines · 967 B · no license · 2 matches
- % As an input, this function will take:
- % - number of trials
- % - the mean reward for each bandit
- % - learning rate alpha
- % - softmax parameter beta
- % As an output, this function will return:
- % - the sequence of actions chosen by the model
- % - the sequence of rewards received by the model (0/1)
- function [a, r, rpe] = simulate_M3RescorlaWagner_v1_ZI(T, mu, alpha, beta, reversal_trials)
- % initialise Q values for each of the two options
- Q = [0.5 0.5];
- % loop over trials
- for t = 1:T
- if any(reversal_trials(:) == t)
- mu = flip(mu);
- end
- % compute choice probabilities using softmax formula
- p = exp(beta*Q) / sum(exp(beta*Q));
- % make choice according to choice probababilities
- a(t) = choose(p);
- % generate reward based on choice
- r(t) = rand < mu(a(t));
- % update values using prediction error term
- delta = r(t) - Q(a(t));
- rpe(t) = delta;
- Q(a(t)) = Q(a(t)) + alpha * delta;
- end
simulate_M3RescorlaWagner_v1_ZI.m at commit 95a7b8f, no license · at the source
Overview
- Nash Family Department of Neuroscience and the Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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 10 matches between paragraphs and lines of code.
zarghonai/single_unit
95a7b8fd4c85e3f075d9feeb2aa99f6d8334696e, 3 July 2024Availability: 1 check, the latest on 30 September 2026: the link answers
- 30 September 2026: the link answers
47 files
- A1_Alignment.m, MATLAB, 130 lines
- B1_JSON.m, MATLAB, 8 lines
- M1_modeling_parameters.m
, MATLAB, 61 lines - M2_rpe_calcs.m, MATLAB, 94 lines
- N1_OSort_prep.m, MATLAB, 43 lines
- N2_Standalone_textGUI_Al
phaOmega.m , MATLAB, 95 lines, 1 match - N3_5_plotwithlegend.m, MATLAB, 52 lines
- N4_SpikePostProcessing.m
, MATLAB, 128 lines - N6_firing_rate.m, MATLAB, 45 lines
- N7_waveform_calculations
.m , MATLAB, 21 lines - P_concat_fr_easy_ZI.m, MATLAB, 57 lines
- all_data_code.m, MATLAB, 274 lines
- baseline_fr.m, MATLAB, 103 lines
- condition_sequence.m, MATLAB, 53 lines
- decision_period.m, MATLAB, 148 lines
- group_ttest.m, MATLAB, 92 lines
- magnitude_outcome_calc.m
, MATLAB, 194 lines - modeling/
1-simulations.mlx , MATLAB, to read at its source - modeling/
2-parameterRecovery.mlx , MATLAB, to read at its source - modeling/
3-confusionMatrix.mlx , MATLAB, to read at its source - modeling/
3-confusionMatrix_ZI.mlx , MATLAB, to read at its source - modeling/
analysis_WSLS_v1.m , MATLAB, 9 lines - modeling/
confusion_matrix_new.mlx , MATLAB, to read at its source - modeling/
fitting_functions/ , MATLAB, 11 linesfit_M1random_v1.m - modeling/
fitting_functions/ , MATLAB, 11 linesfit_M2WSLS_v1.m - modeling/
fitting_functions/ , MATLAB, 26 lines, 1 matchfit_M3RescorlaWagner_v1. m - modeling/
fitting_functions/ , MATLAB, 26 lines, 1 matchfit_all_v1.m - modeling/
fitting_functions/ , MATLAB, 30 lines, 1 matchfit_all_v1_ZI.m - modeling/
helper_functions/ , MATLAB, 64 linesaddABCs.m - modeling/
helper_functions/ , MATLAB, 15 linesaddFacetLines.m - modeling/
helper_functions/ , MATLAB, 46 lineseasy_gridOfEqualFigures. m - modeling/
helper_functions/ , MATLAB, 18 linesimageTextMatrix.m - modeling/
likelihood_functions/ , MATLAB, 22 lines, 1 matchlik_M1random_v1.m - modeling/
likelihood_functions/ , MATLAB, 46 lines, 1 matchlik_M2WSLS_v1.m - modeling/
likelihood_functions/ , MATLAB, 28 lineslik_M3RescorlaWagner_v1. m - modeling/
simulation_functions/ , MATLAB, 3 lineschoose.m - modeling/
simulation_functions/ , MATLAB, 361 linesfull_data_analysis.m - modeling/
simulation_functions/ , MATLAB, 20 linessimulate_M1random_v1.m - modeling/
simulation_functions/ , MATLAB, 44 linessimulate_M2WSLS_v1.m - modeling/
simulation_functions/ , MATLAB, 37 lines, 2 matchessimulate_M3RescorlaWagne r_v1.m - modeling/
simulation_functions/ , MATLAB, 37 lines, 2 matchessimulate_M3RescorlaWagne r_v1_ZI.m - mpsth_SEQ_slots.m, MATLAB, 191 lines
- raster_script.m, MATLAB, 21 lines
- raster_steps.m, MATLAB, 233 lines
- regression_analysis.m, MATLAB, 29 lines
- reward_outcomes_func.m, MATLAB, 60 lines
- README.md, Text, 37 lines
The paper's code and data availability statement is in the Data section.
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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: zarghonai/
single_unit - 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.2026.115458.
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Version 1, 30 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 1 keyword, 4 funders, 62 references.
Cite
This paper
Imtiaz, Z., Kato, A., Kopell, B. H., Qasim, S. E., Davis, A. N., Martinez, L. N., Heflin, M., Kulkarni, K., Morsi, A., Charney, A. W., Gu, X., & Saez, I. (2026). Sustained activity of human substantia nigra neurons reflect prior rewards. iScience, 29(4), 115458. https://
BibTeX
@article{imtiaz2026susta
author = {Imtiaz, Zarghona and Kato, Ayaka and Kopell, Brian H. and Qasim, Salman E. and Davis, Arianna Neal and Martinez, Lizbeth Nunez and Heflin, Matthew and Kulkarni, Kaustubh and Morsi, Amr and Charney, Alexander W. and Gu, Xiaosi and Saez, Ignacio},
title = {{Sustained activity of human substantia nigra neurons reflect prior rewards}},
journal = {iScience},
year = {2026},
month = mar,
volume = {29},
number = {4},
pages = {115458},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/
url = {https://
pmid = {42006305},
pmcid = {PMC13090738}
}
RIS
TY - JOUR
AU - Imtiaz, Zarghona
AU - Kato, Ayaka
AU - Kopell, Brian H.
AU - Qasim, Salman E.
AU - Davis, Arianna Neal
AU - Martinez, Lizbeth Nunez
AU - Heflin, Matthew
AU - Kulkarni, Kaustubh
AU - Morsi, Amr
AU - Charney, Alexander W.
AU - Gu, Xiaosi
AU - Saez, Ignacio
TI - Sustained activity of human substantia nigra neurons reflect prior rewards
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/
VL - 29
IS - 4
SP - 115458
SN - 2589-0042
PB - Elsevier
DO - 10.1016/
UR - https://
LA - en
ER -
CSL-JSON
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