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Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies.

Code ↔ Paper

23 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 23 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Modeling › Subunit model. ↔ sc_model/scripts/run_compute_stc.py, lines 1–45 · score 0.81 · spike triggered covariance, stimulus features, spatially cropped, enclosed, absolute, eigenvalue
  2. [2] § Methods › Visual stimulation and basic cell characterizations › Estimation of receptive fields and stimulus filters. ↔ sc_model/utils/gaussian.py, lines 131–202 · score 0.73 · curve_fit, Gaussian function, Gaussian fit, ellipse, scipy, squares
  3. [3] § Methods › Visual stimulation and basic cell characterizations › Estimation of receptive fields and stimulus filters. ↔ sc_model/utils/receptive_fields.py, lines 35–69 · score 0.68 · median absolute deviation, selected pixels, receptive field, STA, spatiotemporal, filters
  4. [4] § Methods › Modeling › Linear-nonlinear (LN) model. ↔ sc_model/scripts/run_ln_model.py, lines 1–74 · score 0.64 · linear filter, activation function, LN model, temporal filter, spatial filter, white noise
  5. [5] § Methods › Modeling › Model fitting and evaluation. ↔ sc_model/utils/training.py, lines 147–211 · score 0.64 · subunit norms, subunit weights, functional subunits, subunit model, pruned, training
  6. [6] § Methods › Modeling › Model fitting and evaluation. ↔ sc_model/utils/minimization.py, lines 8–101 · score 0.64 · optimization algorithm, minimized, BFGS, scipy, bounds, argument
  7. [7] § Methods › Modeling › Subunit model. ↔ sc_model/utils/stc.py, lines 15–90 · score 0.63 · spike triggered covariance, absolute deviations, eigenvalue, STC, eigenvectors, filters
  8. [8] § Results › Predictive model with spatial-contrast sensitivity ↔ sc_model/utils/minimization.py, lines 104–135 · score 0.61 · Poisson spiking process, negative log likelihood, minimizing, SC model, nonlinearity
  9. [9] § Methods › Modeling › Subunit model. ↔ sc_model/models/subunit_model.py, lines 19–51 · score 0.60 · STC eigenvectors, subunit model, functional subunit, logical, threshold, weight
  10. [10] § Methods › Spatial smoothing and optimal scale of spatial integration ↔ sc_model/utils/convolutions.py, lines 115–234 · score 0.60 · gaussian_filter, convolved stimulus, standard deviations, SC model, smoothed, computation
  11. [11] § Methods › Modeling › Subunit model. ↔ sc_model/scripts/run_subunit_pipeline.py, lines 1–53 · score 0.60 · STC eigenvectors, functional subunit, subunit model, pruning, spatial filter, norm
  12. [12] § Methods › Modeling › Model fitting and evaluation. ↔ sc_model/utils/minimization.py, lines 8–101 · score 0.59 · negative log likelihood, spike trains, minimizing, activation, optimized, fitting
  13. [13] § Methods › Power spectrum analysis ↔ sc_model/utils/convolutions.py, lines 21–112 · score 0.59 · spatial dimensions, Stimulus smoothing, convolved stimulus, naturalistic movie, temporal filter, frames
  14. [14] § Results › Identifying spatial scales of stimulus integration with the SC model ↔ sc_model/utils/convolutions.py, lines 115–234 · score 0.58 · Gaussian filter, SC model, convolved stimulus, Imean, dimensional, smoothing
  15. [15] § Methods › Modeling › Model fitting and evaluation. ↔ sc_model/models/ln_model.py, lines 13–93 · score 0.58 · LN model, BFGS, scipy, minimized, bounds, scored
  16. [16] § Methods › Modeling › Model fitting and evaluation. ↔ sc_model/utils/training.py, lines 467–528 · score 0.57 · PyTorch, Adam, loss, epochs, log, batch
  17. [17] § Methods › Modeling › Subunit model. ↔ sc_model/models/logical_or_model.py, lines 18–129 · score 0.55 · STC eigenvectors, temporally convolved, logical, probabilistic, subunit, linearly
  18. [18] § Methods › Modeling › Model fitting and evaluation. ↔ postProcess/ccg.m, the whole file · a weak match · score 0.55 · Poisson process, spike train, probability, likelihood, binned
  19. [19] § Methods › Modeling › Subunit model. ↔ sc_model/models/subunit_model.py, lines 19–51 · score 0.54 · ReLU, subunit nonlinearity, softplus, weight, filter, model
  20. [20] § Methods › Electrophysiological recordings ↔ eMouse/make_eMouseData.m, the whole file · a weak match · score 0.53 · refractory period, Kilosort, clusters, signals, Spikes
  21. [21] § Methods › Electrophysiological recordings ↔ postProcess/find_merges.m, the whole file · a weak match · score 0.53 · refractory period violations, clusters, binned, Spikes
  22. [22] § Methods › Modeling › Linear-nonlinear (LN) model. ↔ sc_model/scripts/run_ln_model.py, lines 1–74 · score 0.51 · linear activation, LN model, spatial filter, receptive field, pixel, Gaussian
  23. [23] § Results › Cell-type-specific comparison of LN and SC models ↔ sc_model/models/ln_model.py, lines 13–93 · score 0.50 · ground truth, LN model, naturalistic movie, SC model, convolved, temporally

Paper

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The authors' code

Python · 172 lines · 6.4 KB · MIT · 3 matches

  1. import numpy as np
  2. from scipy.optimize import (
  3. check_grad,
  4. minimize,
  5. )
  8. def fit_parameters_mle(
  9. input_signal,
  10. response_counts,
  11. fit_func,
  12. starter_params,
  13. method="L-BFGS-B",
  14. fit_func_der=None,
  15. bounds=None,
  16. check_gradient=False,
  17. options=None,
  18. ):
  19. """
  20. Fit parameters using maximum likelihood estimation (MLE) for Poisson spike trains.
  22. This function minimizes the negative log-likelihood function for Poisson spike trains
  23. using the provided fit function and its derivative. It uses the `scipy.optimize.minimize`
  24. function to perform the optimization.
  26. The function takes the input signal, response counts, fit function, initial parameters,
  27. and other optional parameters for the optimization. It returns the optimized parameters
  28. that minimize the negative log-likelihood function.
  30. :param input_signal:
  31. The input signal to the nonlinearity defined in the `fit-func` function.
  32. :param response_counts:
  33. The response counts that the output of the `fit-func` function should be fitted to.
  34. :param fit_func:
  35. The non-linear activation function to be used in the model.
  36. :param starter_params:
  37. The initial parameters for the optimization.
  38. :param method:
  39. The optimization method to be used. This argument is passed directly to
  40. `scipy.optimize.minimize`. Default is "L-BFGS-B".
  41. :param fit_func_der:
  42. The derivative of the non-linear activation function with respect to the parameters to be optimized.
  43. This function is used to compute the gradient of the negative log-likelihood function.
  44. If None, the gradient is not computed.
  45. :param bounds:
  46. The bounds for the parameters to be optimized.
  47. The bounds should be specified as a list of tuples, where each tuple contains the lower and upper
  48. bounds for each parameter. For example, [(0, 1), (0, None), (0, None)] specifies that the first
  49. parameter is bounded between 0 and 1, while the second and third parameters are unbounded.
  50. :param check_gradient:
  51. If True, the gradient of the negative log-likelihood function is checked using finite differences.
  52. This is useful for debugging and ensuring that the gradient is computed correctly.
  53. Default is False.
  54. :param options:
  55. Additional options for the optimization algorithm. This argument is passed directly to
  56. `scipy.optimize.minimize`. For example, you can specify the maximum number of iterations or
  57. the tolerance for convergence.
  59. :return:
  60. The optimized parameters that minimize the negative log-likelihood function.
  61. The output is a list of the fitted parameters.
  62. """
  63. loglike_fn = poisson_negative_log_likelihood
  65. if fit_func_der is not None:
  66. loglike_prime_fn = poisson_negative_log_likelihood_derivative
  67. else:
  68. loglike_prime_fn = None
  70. if check_gradient and loglike_prime_fn is not None:
  71. grads = []
  72. for i in range(10):
  73. grad_error = check_grad(
  74. loglike_fn,
  75. loglike_prime_fn,
  76. np.random.uniform(0, 1, len(starter_params)),
  77. fit_func,
  78. fit_func_der,
  79. input_signal,
  80. response_counts,
  81. )
  82. grads.append(grad_error)
  84. print(f"Gradient error: {np.mean(grads)} +- {np.std(grads)}")
  86. result = minimize(
  87. fun=loglike_fn,
  88. jac=loglike_prime_fn,
  89. x0=starter_params,
  90. args=(
  91. fit_func,
  92. fit_func_der,
  93. input_signal,
  94. response_counts,
  95. ),
  96. bounds=bounds,
  97. method=method,
  98. options=options
  99. )
  101. return result.x
  104. def poisson_negative_log_likelihood(
  105. function_parameters: list,
  106. *args,
  107. ):
  108. """
  109. General negative log-likelihood function for poisson spike trains.
  111. :param function_parameters:
  112. The parameters of the non-linear activation function.
  113. These parameters are passed to the nonlinear function.
  114. :param args:
  115. Other arguments for the negative log-likelihood function.
  116. These include the nonlinear function, its derivative, the input signal, and the response counts.
  118. :return:
  119. The negative log-likelihood value for the given parameters.
  120. This value is computed under the assumption of a Poisson spiking process.
  121. """
  122. function, function_derivative, input_signal, response_counts = args
  124. # for reference in this function:
  125. #
  126. # M is the size of `response counts`
  127. # shape of input signal: (..., M)
  128. # there are no requirements on the first dimension of the `input signal`
  129. # and this must be taken care of by the `function`
  130. #
  131. # function output shape: (M,)
  132. function_output = function(input_signal, function_parameters)
  133. sum_1 = function_output.sum()
  134. sum_2 = (response_counts * np.log(function_output)).sum()
  135. return (sum_1 - sum_2) / response_counts.sum()
  138. def poisson_negative_log_likelihood_derivative(
  139. function_parameters: list,
  140. *args,
  141. ):
  142. """
  143. First derivative of the negative log-likelihood function for poisson spike trains.
  145. :param function_parameters:
  146. The parameters of the non-linear activation function.
  147. These parameters are passed to the nonlinear function and its derivative.
  148. :param args:
  149. Other arguments for the negative log-likelihood function.
  150. These include the nonlinear function, its derivative, the input signal, and the response counts.
  151. :return:
  152. """
  153. function, function_derivative, input_signal, response_counts = args
  155. # for reference in this function:
  156. #
  157. # M is the size of `response counts`
  158. # N is the number of independent function parameters
  159. # shape of input signal: (..., M)
  160. # there are no requirements on the first dimension of the `input signal`
  161. # and this must be taken care of by the `function` and `function derivative`
  162. #
  163. # derivative output shape: (M, N)
  164. derivative_output = function_derivative(input_signal, function_parameters)
  165. # function output shape: (M,)
  166. function_output = function(input_signal, function_parameters)
  167. # sum_1 shape: (N,)
  168. sum_1 = derivative_output.sum(axis=0)
  169. # sum_2 shape: (N,)
  170. # (M,) @ (M, N)
  171. sum_2 = (response_counts / function_output) @ derivative_output
  172. return (sum_1 - sum_2) / response_counts.sum()

minimization.py at commit 76b7334, under MIT · at the source

Overview

Authors: Shashwat Sridhar1,2, Michaela Vystrčilová3, Mohammad H. Khani1,2, Dimokratis Karamanlis1,2, Helene M. Schreyer1,2, Varsha Ramakrishna1,2,4, Steffen Krüppel1,2, Sören J. Zapp1,2, Matthias Mietsch5,6, Alexander S. Ecker2,3,7,8, Tim Gollisch1,2,8,9
ORCID iDs: Shashwat Sridhar, Michaela Vystrčilová, Mohammad H. Khani, Dimokratis Karamanlis, Helene M. Schreyer, Varsha Ramakrishna, Steffen Krüppel, Sören J. Zapp, Matthias Mietsch, Alexander S. Ecker, Tim Gollisch
  1. Department of Ophthalmology, University Medical Center Göttingen, Göttingen, Germany
  2. Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany
  3. University of Göttingen, Institute of Computer Science and Campus Institute Data Science, Göttingen, Germany
  4. International Max Planck Research School for Neurosciences, Göttingen, Germany
  5. German Primate Center, Laboratory Animal Science Unit, Göttingen, Germany
  6. German Center for Cardiovascular Research, Partner Site Göttingen, Göttingen, Germany
  7. Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
  8. Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells“ (MBExC), University of Göttingen, Göttingen, Germany
  9. Else Kröner Fresenius Center for Optogenetic Therapies, University Medical Center Göttingen, Göttingen, Germany
Institutions: Bernstein Center for Computational Neuroscience Göttingen (Germany); Universitätsmedizin Göttingen (Germany); Campus-Institut Data Science (CIDAS) (Germany); University of Göttingen (Germany); German Primate Center (Germany); German Centre for Cardiovascular Research (Germany); Max Planck Institute for Dynamics and Self-Organization (Germany); Multiscale Bioimaging
Journal: PLoS computational biology, volume 22, issue 4, article e1014157
Dates: received 10 April 2025; accepted 23 March 2026; published online 7 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014157 · PMID 41945609 · PMCID PMC13099094 · OpenAlex W4392633162
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.03.05.583449
Status: code verified
Categories: computational modeling (no new data) (modality), non-human primate (organism)
Methods: Spectral & time-frequency, Connectivity, Smoothing, state filtering, decompositions, Machine learning, Statistics, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
MeSH: Contrast Sensitivity*, Models, Neurological*, Retinal Ganglion Cells*, Animals, Callithrix, Computational Biology, Computer Simulation, Photic Stimulation (* major topic)
Journal subjects: Biology and Life Sciences, Anatomy, Ocular System, Ocular Anatomy, Retina, Medicine and Health Sciences, Engineering and Technology, Signal Processing, Signal Filtering, White Noise, Cell Biology, Cellular Types, Animal Cells, Neurons, Ganglion Cells, Neuroscience, Cellular Neuroscience, Research and Analysis Methods, Animal Studies, Experimental Organism Systems, Animal Models, Marmosets, Organisms, Eukaryota, Animals, Vertebrates, Amniotes, Mammals, Primates, Monkeys, New World monkeys, Zoology, Old World monkeys, Macaque, Afferent Neurons, Retinal Ganglion Cells, Physiology, Sensory Physiology, Visual System, Eye Movements, Sensory Systems
Topic: Visual perception and processing mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (432680300, 528760423, 515774656); European Research Council (101041669)
Citations: cited by 3 papers (Europe PMC); 87 references in the paper

Abstract

Retinal ganglion cells, the output neurons of the vertebrate retina, often display nonlinear summation of visual signals over their receptive fields. This creates sensitivity to spatial contrast, letting the cells respond to spatially structured visual stimuli even when no net change in overall illumination of the receptive field occurs. Yet, computational models of ganglion cell responses are often based on linear receptive fields, and typical nonlinear extensions, which separate receptive fields into nonlinearly combined subunits, are often cumbersome to fit to experimental data. Previous work has suggested to model spatial-contrast sensitivity in responses to flashed images by combining signals from the mean and variance of light intensity inside the receptive field. Here, we extend and adjust this spatial contrast model for application to spatiotemporal stimulation and explore its performance on spiking responses that we recorded from ganglion cells of marmosets under artificial and naturalistic movies. We show how the model can be fitted to experimental data and that it outperforms common models with linear spatial integration to different degrees for different types of ganglion cells. Finally, we use the model framework to infer the cells’ spatial scale of nonlinear spatial integration. Our work shows that the spatial contrast model can capture aspects of nonlinear spatial integration in the primate retina with only few free parameters. The model can be used to assess the cells’ functional properties under natural stimulation and provides a simple-to-obtain benchmark for comparison with more detailed nonlinear encoding models.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 23 matches between paragraphs and lines of code.

gollischlab/SpatiotemporalSCModel

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 76b733421cc16131c2229e66a7714d8892de39d7, 30 January 2026
Languages: Python (23)
Size: 26 files, 23 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, environment (pyproject.toml)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (18 files), SciPy (6 files), PyTorch (5 files), CuPy (3 files), pandas (3 files), Numba (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
25 files
At the source: github.com/gollischlab/SpatiotemporalSCModel

dimokaramanlis/KiloSortMEA

License: GPL-2.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: b7d54fe7068801f5a847e7119eb4b3dd414e9840, 31 July 2024
Languages: MATLAB (100), CUDA (3), C (3), C/C++ (2)
Size: 142 files, 108 scripts
Software Heritage: not archived
Found in: the text, “Electrophysiological recordings”
Holds: README, license file, documentation
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: Parallel Computing Toolbox (14 files), Kilosort (3 files), Signal Processing Toolbox (3 files), Statistics and Machine Learning Toolbox (3 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
110 files
At the source: github.com/dimokaramanlis/KiloSortMEA

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 131 scripts, each with its path and the digest of its content;
  • 23 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

Datasets cited

Data Availability

The data of this study have been made publicly available on G-Node (https://doi.org/10.12751/g-node.t43ph1) together with information for reconstructing the visual stimuli. The code for fitting and evaluating the models has been made available on GitHub (https://github.com/gollischlab/SpatiotemporalSCModel).

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 8 MeSH terms, 2 funders, 80 references.

Cite

This paper

Sridhar, S., Vystrčilová, M., Khani, M. H., Karamanlis, D., Schreyer, H. M., Ramakrishna, V., Krüppel, S., Zapp, S. J., Mietsch, M., Ecker, A. S., & Gollisch, T. (2026). Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies. PLoS computational biology, 22(4), e1014157. https://doi.org/10.1371/journal.pcbi.1014157

BibTeX

@article{sridhar2026modeling,
author = {Sridhar, Shashwat and Vystrčilová, Michaela and Khani, Mohammad H. and Karamanlis, Dimokratis and Schreyer, Helene M. and Ramakrishna, Varsha and Krüppel, Steffen and Zapp, Sören J. and Mietsch, Matthias and Ecker, Alexander S. and Gollisch, Tim},
title = {{Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies}},
journal = {PLoS computational biology},
year = {2026},
month = apr,
volume = {22},
number = {4},
pages = {e1014157},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014157},
url = {https://doi.org/10.1371/journal.pcbi.1014157},
pmid = {41945609},
pmcid = {PMC13099094}
}

RIS

TY - JOUR
AU - Sridhar, Shashwat
AU - Vystrčilová, Michaela
AU - Khani, Mohammad H.
AU - Karamanlis, Dimokratis
AU - Schreyer, Helene M.
AU - Ramakrishna, Varsha
AU - Krüppel, Steffen
AU - Zapp, Sören J.
AU - Mietsch, Matthias
AU - Ecker, Alexander S.
AU - Gollisch, Tim
TI - Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/04/07
VL - 22
IS - 4
SP - e1014157
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014157
UR - https://doi.org/10.1371/journal.pcbi.1014157
LA - en
ER -

CSL-JSON

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