Population coding under the scale invariance of high-dimensional noise.
The 6 matches
- [1] § MATERIALS AND METHODS › Simulation of neural activity with bounded and unbounded LFI ↔ data_simulated_gamma2_manyrep/gen_simdata.py, lines 12–99 · score 0.85 · inverse Wishart distribution, lognormal distribution, firing rates, mouse TX42, Simulation, matrix
- [2] § MATERIALS AND METHODS › Bias correcting the eigenvalues and angular occupations › Eigenvalue bias correction ↔ code/code_janelia_trialeffect_new/bias_correct_morevals_allrec.ipynb, lines 12–106 · score 0.58 · initial guess, Bias corrected eigenvalues, iteratively, naive
- [3] § MATERIALS AND METHODS › Bias correcting the eigenvalues and angular occupations › Eigenvalue bias correction ↔ code/code_simdata_alphapos_gamma2/bias_correct.ipynb, lines 32–82 · score 0.58 · initial guess, Bias corrected eigenvalues, iteratively, naive
- [4] § MATERIALS AND METHODS › Bias correcting the eigenvalues and angular occupations › Angular occupation bias correction › Eigenvector bias correction. ↔ code/code_janelia_trialeffect_new/bias_correct_morevals_allrec.ipynb, lines 12–106 · score 0.55 · initial guess, bias corrected, algorithm, naive, cosines, squared
- [5] § MATERIALS AND METHODS › Bias correcting the eigenvalues and angular occupations › Angular occupation bias correction › Eigenvector bias correction. ↔ code/code_simdata_alphapos_gamma2/bias_correct.ipynb, lines 32–82 · score 0.55 · initial guess, bias corrected, algorithm, naive, cosines, squared
- [6] § MATERIALS AND METHODS › Analysis of mouse V1 data ↔ code/functions/data_extract.py, lines 26–75 · score 0.51 · 43–47 deg, static, orientations, 43 deg, stimuli
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 · 109 lines · 6.2 KB · no license · 2 matches
bias_correct_morevals_allrec.ipynb at commit ec17015, no license · at the source
Overview
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
- Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Sapporo 060-0812, Japan
Abstract
High-dimensional scale-invariant neural activity is ubiquitous across brain regions and species, but its implications for information coding remain unclear. Here, we ask how stimulus information in the high-dimensional activity of mouse V1 scales with neuron number: Does it saturate due to noise correlations or increase without bound as subpopulations grow? Contrary to previous reports, we find that leading noise components that scale linearly with population size, and thus can limit information, are not sufficiently aligned with the signal to impose a bound. This conclusion follows from two scale-invariant power-law properties of neuronal responses in mouse V1: the noise eigenspectrum and alignment of noise components with the signal. We show that population subsampling links the observed power-law exponents to information boundedness and that information scaling depends on the full eigenspectrum rather than its leading modes. Last, we prove that, under subsampling, information-limiting correlations, if present, are differential correlations. Our findings clarify how information scales in high-dimensional neuronal activity under scale-invariant noise.
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 6 matches between paragraphs and lines of code.
Zenodo 19504768
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
- 28 September 2026: the link answers (HTTP 200)
38 files
- code/
code_janelia_trialeffect — Jupyter, 205 lines_new/ alpha_gamma_est.ipynb - code/
code_janelia_trialeffect — Jupyter, 145 lines_new/ analyse_janelia_mult_tri als_all_recordings.ipynb - code/
code_janelia_trialeffect — Jupyter, 179 lines_new/ bc_alpha_gamma_est_allre c.ipynb - code/
code_janelia_trialeffect — Jupyter, 362 lines_new/ bc_figure_main_janelia.i pynb - code/
code_janelia_trialeffect — Jupyter, 109 lines_new/ bias_correct_morevals_al lrec.ipynb - code/
code_janelia_trialeffect — Jupyter, 481 lines_new/ make_main_figure_2.ipynb - code/
code_janelia_trialeffect — Jupyter, 394 lines_new/ make_supp_figures.ipynb - code/
code_janelia_trialeffect — Jupyter, 163 lines_new/ normalized_eigs_bias_cor rect.ipynb - code/
code_simdata_alphapos_ga — Jupyter, 167 linesmma2/ bias_correct.ipynb - code/
code_simdata_alphapos_ga — Jupyter, 212 linesmma2/ directest_simunb_multrep .ipynb - code/
code_simdata_alphapos_ga — Jupyter, 419 linesmma2/ simdata_figures_bc.ipynb - code/
code_simdata_unb_alpha0g — Jupyter, 130 linesamma1/ alpha_gamma_est.ipynb - code/
code_simdata_unb_alpha0g — Jupyter, 246 linesamma1/ directest_simunb_multrep .ipynb - code/
code_simdata_unb_alpha0g — Jupyter, 221 linesamma1/ make_simunb_supp_fig.ipy nb - code/
code_simdata_unb_alpha0g — Jupyter, 273 linesamma1/ pls_simunb_multrep.ipynb - code/
code_simdata_unb_alpha0g — Jupyter, 124 linesamma1/ scalinganalysis_simunb_m ultrep.ipynb - code/
code_simdata_unb_gamma1/ — Jupyter, 152 linesalpha_gamma_est.ipynb - code/
code_simdata_unb_gamma1/ — Jupyter, 241 linesdirectest_simunb_multrep .ipynb - code/
code_simdata_unb_gamma1/ — Jupyter, 221 linesmake_simunb_supp_fig.ipy nb - code/
code_simdata_unb_gamma1/ — Jupyter, 273 linespls_simunb_multrep.ipynb - code/
code_simdata_unb_gamma1/ — Jupyter, 124 linesscalinganalysis_simunb_m ultrep.ipynb - code/
functions/ — Python, 1 line__init__.py - code/
functions/ — Python, 193 linesbias_correction_function s.py - code/
functions/ — Python, 41 linescompute_fi.py - code/
functions/ — Python, 75 linesdata_extract.py - code/
functions/ — Python, 91 linesmake_figures.py - code/
functions/ — Python, 352 linespls_analysis.py - code/
functions/ — Python, 337 linestrial_analysis.py - data_simulated_gamma2_ma
nyrep/ — Python, 63 linesfunctions.py - data_simulated_gamma2_ma
nyrep/ — Python, 100 linesgen_simdata.py - data_simulated_gamma2_ma
nyrep/ — Jupyter, 39 linesgenerate_data_simulated. ipynb - data_simulated_unb_alpha
0gamma1/ — Python, 63 linesfunctions.py - data_simulated_unb_alpha
0gamma1/ — Python, 100 linesgen_simdata.py - data_simulated_unb_alpha
0gamma1/ — Jupyter, 39 linesgenerate_data_simulated. ipynb - data_simulated_unb_gamma
1/ — Python, 63 linesfunctions.py - data_simulated_unb_gamma
1/ — Python, 100 linesgen_simdata.py - data_simulated_unb_gamma
1/ — Jupyter, 39 linesgenerate_data_simulated. ipynb - README.md — Text, 4 lines
Sai-Sumedh/scaling-population-coding
ec170158e146aba4c94296cf2a412d4b4dae8b2f, 24 July 2026Availability: 1 check, the latest on 28 September 2026: the link answers
- 28 September 2026: the link answers
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code_janelia_trialeffect — Jupyter, 205 lines, shown from its source_new/ alpha_gamma_est.ipynb - code/
code_janelia_trialeffect — Jupyter, 145 lines, shown from its source_new/ analyse_janelia_mult_tri als_all_recordings.ipynb - code/
code_janelia_trialeffect — Jupyter, 179 lines, shown from its source_new/ bc_alpha_gamma_est_allre c.ipynb - code/
code_janelia_trialeffect — Jupyter, 362 lines, shown from its source_new/ bc_figure_main_janelia.i pynb - code/
code_janelia_trialeffect — Jupyter, 109 lines, 2 matches, shown from its source_new/ bias_correct_morevals_al lrec.ipynb - code/
code_janelia_trialeffect — Jupyter, 481 lines, shown from its source_new/ make_main_figure_2.ipynb - code/
code_janelia_trialeffect — Jupyter, 394 lines, shown from its source_new/ make_supp_figures.ipynb - code/
code_janelia_trialeffect — Jupyter, 163 lines, shown from its source_new/ normalized_eigs_bias_cor rect.ipynb - code/
code_janelia_trialeffect — Jupyter, 25 lines, shown from its source_new/ num_neurons.ipynb - code/
code_simdata_alphapos_ga — Jupyter, 167 lines, 2 matches, shown from its sourcemma2/ bias_correct.ipynb - code/
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code_simdata_alphapos_ga — Jupyter, 419 lines, shown from its sourcemma2/ simdata_figures_bc.ipynb - code/
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code_simdata_unb_alpha0g — Jupyter, 273 lines, shown from its sourceamma1/ pls_simunb_multrep.ipynb - code/
code_simdata_unb_alpha0g — Jupyter, 124 lines, shown from its sourceamma1/ scalinganalysis_simunb_m ultrep.ipynb - code/
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functions/ — Python, 1 line, shown from its source__init__.py - code/
functions/ — Python, 193 lines, shown from its sourcebias_correction_function s.py - code/
functions/ — Python, 41 lines, shown from its sourcecompute_fi.py - code/
functions/ — Python, 75 lines, 1 match, shown from its sourcedata_extract.py - code/
functions/ — Python, 91 lines, shown from its sourcemake_figures.py - code/
functions/ — Python, 352 lines, shown from its sourcepls_analysis.py - code/
functions/ — Python, 337 lines, shown from its sourcetrial_analysis.py - data_simulated_gamma2_ma
nyrep/ — Python, 63 lines, shown from its sourcefunctions.py - data_simulated_gamma2_ma
nyrep/ — Python, 100 lines, 1 match, shown from its sourcegen_simdata.py - data_simulated_gamma2_ma
nyrep/ — Jupyter, 39 lines, shown from its sourcegenerate_data_simulated. ipynb - data_simulated_unb_alpha
0gamma1/ — Python, 63 lines, shown from its sourcefunctions.py - data_simulated_unb_alpha
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1/ — Python, 63 lines, shown from its sourcefunctions.py - data_simulated_unb_gamma
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1/ — Jupyter, 39 lines, shown from its sourcegenerate_data_simulated. ipynb - README.md — Text, 10 lines, shown from its source
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Cite
This paper
Moosavi, S. A., Hindupur, S. S. R., & Shimazaki, H. (2026). Population coding under the scale invariance of high-dimensional noise. Science advances, 12(39), eadz9632. https://
BibTeX
@article{moosavi2026popu
author = {Moosavi, S Amin and Hindupur, Sai Sumedh R and Shimazaki, Hideaki},
title = {{Population coding under the scale invariance of high-dimensional noise}},
journal = {Science advances},
year = {2026},
month = sep,
volume = {12},
number = {39},
pages = {eadz9632},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/
url = {https://
pmid = {42789721},
pmcid = {PMC13614404}
}
RIS
TY - JOUR
AU - Moosavi, S Amin
AU - Hindupur, Sai Sumedh R
AU - Shimazaki, Hideaki
TI - Population coding under the scale invariance of high-dimensional noise
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/
VL - 12
IS - 39
SP - eadz9632
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/
UR - https://
LA - en
ER -
CSL-JSON
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