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Combining ultra-flexible electrodes with two-photon imaging to illuminate brain-wide neural dynamics

Code ↔ Paper

2 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 2 matches
  1. [1] § Data analysis › Reduced rank regression ↔ python/fitting.py, lines 4–45 · score 0.67 · ridge regularized reduced, rank regression, fitting, lambda, matrix, RRR
  2. [2] § Data analysis › Reduced rank regression ↔ python/fitting.py, lines 4–45 · score 0.60 · ridge regularized reduced, rank regression, RRR, neurons

Paper

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

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The authors' code

Python · 92 lines · 3 KB · no license · 2 matches

  1. import numpy as np
  2. from scipy.linalg import sqrtm, inv
  4. def svd_RRR(X, Y, rnk, lambda_=0):
  5. """
  6. Perform Ridge Regularized Reduced Rank Regression (RRR) using SVD.
  7. Parameters:
  8. X : np.ndarray
  9. Input data matrix (n_samples, n_input_neurons).
  10. Y : np.ndarray
  11. Output data matrix (n_samples, n_output_neurons).
  12. rnk : int
  13. Dimensionaility of communication
  14. lambda_ : float
  15. Regularization parameter (default is 0 for no regularization).
  16. Returns:
  17. w0 : np.ndarray
  18. Estimate of the communication strength (n_input_neurons, n_output_neurons).
  19. urrr : np.ndarray
  20. Input axes (n_input_neurons, rnk).
  21. vrrr : np.ndarray
  22. Output axes, orthonormal (n_output_neurons, rnk).
  23. """
  24. # Check if X and Y are 2D arrays
  25. # Ridge regularization
  26. XX = X.T @ X + lambda_ * np.eye(X.shape[1])
  28. # Least squares estimate with ridge
  29. if np.linalg.cond(XX) < 1e10:
  30. wridge = np.linalg.solve(XX, X.T @ Y)
  31. else:
  32. wridge = np.linalg.pinv(XX) @ (X.T @ Y)
  34. # SVD of relevant matrix
  35. _, _, vrrr = np.linalg.svd(Y.T @ X @ wridge)
  37. # Get the top 'rnk' components
  38. vrrr = vrrr[:rnk, :].T # shape: (features, rnk)
  39. urrr = wridge @ vrrr # shape: (features, rnk)
  41. # Construct full RRR estimate
  42. w0 = urrr @ vrrr.T
  43. vrrr = vrrr.T # for compatibility with original code's return
  45. return w0, urrr, vrrr
  48. def svd_RRR_noniso(X, Y, rnk, C=None):
  49. """
  50. Perform Reduced Rank Regression (RRR) using SVD with non-isotropic noise.
  51. Parameters:
  52. X : np.ndarray
  53. Input data matrix (n_samples, n_input_neurons).
  54. Y : np.ndarray
  55. Output data matrix (n_samples, n_output_neurons).
  56. rnk : int
  57. Dimensionaility of communication
  58. C : np.ndarray, optional
  59. Covariance matrix of the noise (default is None, estimate from data).
  60. Returns:
  61. w0 : np.ndarray
  62. Estimate of the communication strength (n_input_neurons, n_output_neurons).
  63. urrr : np.ndarray
  64. Input axes (n_input_neurons, rnk).
  65. vrrr : np.ndarray
  66. Output axes (n_output_neurons, rnk).
  67. """
  68. # Least squares estimate
  69. wls = np.linalg.solve(X.T @ X, X.T @ Y)
  71. # Compute covariance of residuals if C is not provided
  72. if C is None:
  73. res_wls = Y - X @ wls
  74. C = (res_wls.T @ res_wls) / (X.shape[0] - 1)
  76. # Compute inverse sqrt and sqrt of C
  77. C_sqrt = sqrtm(C)
  78. C_inv_sqrt = inv(C_sqrt)
  80. # SVD of whitened cross-covariance
  81. _, _, vrrr = np.linalg.svd(C_inv_sqrt @ Y.T @ X @ wls @ C_inv_sqrt)
  82. vrrr = vrrr[:rnk, :].T # shape: (features, rnk)
  84. # Adjust for non-isotropic noise
  85. vrrr = C_sqrt @ vrrr
  86. urrr = np.linalg.solve(X.T @ X, X.T @ Y @ inv(C) @ vrrr)
  88. # Reconstruct estimate
  89. w0 = urrr @ vrrr.T
  91. return w0, urrr, vrrr

fitting.py at commit e696dd7, no license · at the source

Overview

Authors: Adrian Roggenbach1,2, Linus Meienberg1, Tansel Baran Yasar2,3, Peter Gombkoto3, Nicolas Junghanns1, Wolfger von der Behrens2,3, Mehmet Fatih Yanik2,3, Fritjof Helmchen1,2,4, Christopher M. Lewis1
ORCID iDs: Adrian Roggenbach, Linus Meienberg, Tansel Baran Yasar, Peter Gombkoto, Mehmet Fatih Yanik, Fritjof Helmchen, Christopher M. Lewis
  1. Brain Research Institute, University of Zurich, Zurich, Switzerland
  2. Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
  3. Neurotechnology Group, Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
  4. University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning, University of Zurich, Zurich, Switzerland
Institutions: University of Zurich (Switzerland); ETH Zurich (Switzerland); Institute of Neuroinformatics (Switzerland)
Journal: Research Square (preprint)
Dates: published 4 August 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.21203/rs.3.rs-10218667/v1 · OpenAlex W7172378176
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions, Physiology & signal measures
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (41220, CRSII5_198739/1, 170269, 180316, 192617); Universität Zürich (K-41220-04)
Citations: not cited yet (OpenAlex); 56 references in the paper

Abstract

Brain activity consists of neural signals dynamically coordinated across spatial and temporal scales. To sample distributed brain-wide activity, we combine chronically implanted ultra-flexible electrodes for subcortical recordings with simultaneous two-photon calcium imaging in mouse neocortex. Flexible electrodes preserve optical access even at steep insertion angles and enable weeks-long single-neuron tracking. With these combined recordings, we demonstrate how subcortical-cortical coupling is modulated across slow brain states and around fast ripple events.

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 2 matches between paragraphs and lines of code.

bichanw/RRR

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e696dd77a4e0a93b87d02e72b098d0732def4f38, 16 December 2025
Languages: MATLAB (10), Python (5), Jupyter (2)
Size: 19 files, 17 scripts
Software Heritage: not archived
Found in: the text, “Reduced rank regression”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (7 files), SciPy (3 files), Statistics and Machine Learning Toolbox (2 files), Matplotlib (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
18 files
At the source: github.com/bichanw/RRR

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

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;
  • 17 scripts, each with its path and the digest of its content;
  • 2 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.

Code and data availability

The code and data that support the findings of this study will be made publicly available upon publication of this manuscript.

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 3, 28 September 2026

  • Authors: added Adrian Roggenbach (0000-0002-3072-0733); removed Adrian Roggenbach
  • Funding: added National Science Foundation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung: 41220, CRSII5_198739/1, 170269, 180316, 192617; Universität Zürich: K-41220-04

Version 1, 27 September 2026: the first record

Recorded: type, journal, 9 authors, 55 references.

Cite

This paper

Roggenbach, A., Meienberg, L., Yasar, T. B., Gombkoto, P., Junghanns, N., von der Behrens, W., Yanik, M. F., Helmchen, F., & Lewis, C. M. (2026). Combining ultra-flexible electrodes with two-photon imaging to illuminate brain-wide neural dynamics. Research Square (preprint). https://doi.org/10.21203/rs.3.rs-10218667/v1

BibTeX

@article{roggenbach2026combining,
author = {Roggenbach, Adrian and Meienberg, Linus and Yasar, Tansel Baran and Gombkoto, Peter and Junghanns, Nicolas and von der Behrens, Wolfger and Yanik, Mehmet Fatih and Helmchen, Fritjof and Lewis, Christopher M.},
title = {{Combining ultra-flexible electrodes with two-photon imaging to illuminate brain-wide neural dynamics}},
journal = {Research Square (preprint)},
year = {2026},
month = aug,
publisher = {Research Square},
issn = {2693-5015},
doi = {10.21203/rs.3.rs-10218667/v1},
url = {https://doi.org/10.21203/rs.3.rs-10218667/v1}
}

RIS

TY - JOUR
AU - Roggenbach, Adrian
AU - Meienberg, Linus
AU - Yasar, Tansel Baran
AU - Gombkoto, Peter
AU - Junghanns, Nicolas
AU - von der Behrens, Wolfger
AU - Yanik, Mehmet Fatih
AU - Helmchen, Fritjof
AU - Lewis, Christopher M.
TI - Combining ultra-flexible electrodes with two-photon imaging to illuminate brain-wide neural dynamics
T2 - Research Square (preprint)
J2 - Res Sq
PY - 2026
DA - 2026/08/04
SN - 2693-5015
PB - Research Square
DO - 10.21203/rs.3.rs-10218667/v1
UR - https://doi.org/10.21203/rs.3.rs-10218667/v1
ER -

CSL-JSON

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"given": "Adrian"
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