Spatial Adaptation of Primate Retinal Ganglion Cells Between Artificial and Natural Stimuli.
The 6 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
- [1] § Methods › Training ↔ training/cross_stimulus_training.py, the whole file · a weak match · score 0.67 · ReduceLROnPlateau, learning rate scheduler, Adam, patience, epochs, optimizer
- [2] § Methods › Training ↔ ln_model_factorized_marmoset_nm.py, lines 272–350 · score 0.66 · ReduceLROnPlateau, learning rate scheduler, Adam, patience, epochs, optimizer
- [3] § Methods › Power spectra calculation ↔ notebooks/adaptation_paper_figures.ipynb, lines 848–884 · score 0.61 · Fourier Transform, frequency components, FFT, spectra, filtered
- [4] § Methods › Receptive field size and surround amplitude estimation ↔ notebooks/atick_redlich.ipynb, lines 89–194 · score 0.57 · whitening filter, NM spectra, styles, Power, noise, midget
- [5] § Methods › Data acquisition and stimuli ↔ datasets/natural_stimuli/create_data.py, lines 148–205 · score 0.54 · refresh rate, movements, naturalistic stimuli, pixels
- [6] § Methods › Receptive field size and surround amplitude estimation ↔ notebooks/adaptation_paper_figures.ipynb, lines 276–347 · score 0.53 · natural movies, white noise, boxes, styles, parasol, midget
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 · 1,089 lines · 401 KB · no license · 2 matches
adaptation_paper_figures.ipynb at commit 0d48996, no license · at the source
Overview
- University of Göttingen, Institute of Computer Science and Campus Institute Data Science, Göttingen 37037, Germany
- Department of Ophthalmology, University Medical Center Göttingen, Göttingen 37073, Germany
- Bernstein Center for Computational Neuroscience Göttingen, Göttingen 14195, Germany
- International Max Planck Research School for Intelligent Systems, Tübingen 72076, Germany
- Tübingen AI Center, University of Tübingen, Tübingen 72076, Germany
- International Max Planck Research School for Neurosciences, Göttingen 72074, Germany
- Laboratory Animal Science Unit, German Primate Center, Göttingen 37077, Germany
- German Center for Cardiovascular Research, Partner Site Göttingen, Göttingen 37073, Germany
- Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Göttingen 37075, Germany
- Max Planck Institutefor Dynamics and Self-Organization, Göttingen 37077, Germany
Abstract
The retina encodes a broad range of stimuli, adapting its computations to features like brightness, contrast, and motion. However, it is unclear whether it also adapts when switching between natural scenes and white noise (WN). To address this, we analyzed the neural activity of male marmoset retinal ganglion cells (RGCs) in response to WN and naturalistic movies. We trained linear–nonlinear models on both stimuli, evaluated their performance, and compared their receptive fields across stimulus domains. We found that models with spatial filters trained on one stimulus ensemble were less accurate when predicting neural activity on the other compared to models trained directly on the target stimulus. This suggests that spatial processing adapts to stimulus statistics. Different RGC types exhibited distinct changes: The OFF midget cells’ receptive fields became enlarged under natural movies (NMs), resulting in a lower cutoff frequency. Parasol cells and large OFF cells did not significantly change their receptive field sizes. All cell types exhibited stronger surrounds under NMs, resembling the whitening filters predicted by efficient coding for stimulus decorrelation, prompting us to test whether these changes were related to the different spectral content of the two stimulus types. Quantifying the effects of the filters’ enhanced surrounds on the stimulus power spectrum showed a significant contribution toward whitening only in ON parasol cells, where a whitening effect emerged regardless of the training stimulus. These results suggest that while RGCs adapt to the differences between WN and NM stimuli, efficient coding can only partially account for this adaptation.
Reproduced under the paper's license (CC BY), from the paper cited above.
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doi.gin.g-node.org/10.12751/g-node.t43ph1
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
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ecker-lab/retina-adaptation
0d489964e2c9efd3fb04c815e33b2a67a1051c08, 16 February 2025Availability: 1 check, the latest on 28 September 2026: the link answers
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- datasets/
FrameWiseDataset.py — Python, 253 lines, shown from its source - datasets/
TrialWiseDataset.py — Python, 447 lines, shown from its source - datasets/
__init__.py — Python, 2 lines, shown from its source - datasets/
natural_stimuli/ — Python, 599 lines, 1 match, shown from its sourcecreate_data.py - datasets/
natural_stimuli/ — Python, 21 lines, shown from its sourcecreate_dataset.py - datasets/
naturalmovie_marmoset_lo — Python, 430 lines, shown from its sourceader.py - datasets/
retina_reliability.py — Python, 186 lines, shown from its source - datasets/
stas.py — Python, 236 lines, shown from its source - datasets/
whitenoise_salamander_lo — Python, 428 lines, shown from its sourceaders.py - evaluations/
ln_model_performance.py — Python, 866 lines, shown from its source - evaluations/
sizes_estimations.py — Python, 277 lines, shown from its source - ln_model_factorized.py — Python, 372 lines, shown from its source
- ln_model_factorized_marm
oset_nm.py — Python, 351 lines, 1 match, shown from its source - models/
__init__.py — Python, 1 line, shown from its source - models/
ln_model.py — Python, 291 lines, shown from its source - models/
ln_models/ — MATLAB, not shown hereln_models_factorized__et e/ marmoset/ retina04/ cell_2/ cs_lr_0.005_whole_rf_20_ ch_25_l2_0.0_g_0.0_bs_12 _tr_250_s_1_c_0_fn_1_do_ n_1_dog_0/ weights/ seed_8/ best_model.m - models/
ln_models/ — MATLAB, not shown hereln_models_factorized__et e/ marmoset/ retina04/ cell_2/ cs_lr_0.005_whole_rf_20_ ch_25_l2_0.0_g_0.0_bs_12 _tr_250_s_1_c_0_fn_1_do_ n_1_dog_0/ weights/ seed_8/ initial_model.m - notebooks/
adaptation_paper_figures — Jupyter, 1,089 lines, 2 matches, shown from its source.ipynb - notebooks/
atick_redlich.ipynb — Jupyter, 313 lines, 1 match, shown from its source - notebooks/
rank2models.ipynb — Jupyter, 191 lines, shown from its source - power_spectrum_3d.py — Python, 142 lines, shown from its source
- power_spectrum_analysis.
py — Python, 261 lines, shown from its source - train_multiretinal_ln_nm
_for_wn.py — Python, 93 lines, shown from its source - train_multiretinal_ln_wn
_for_nm.py — Python, 89 lines, shown from its source - training/
cross_stimulus_training. — Python, 150 lines, 1 match, shown from its sourcepy - training/
measures.py — Python, 94 lines, shown from its source - training/
regularizers.py — Python, 26 lines, shown from its source - training/
train_steps.py — Python, 100 lines, shown from its source - training/
trainers.py — Python, 403 lines, shown from its source - utils/
global_functions.py — Python, 140 lines, shown from its source - ReadME.md — Text, 78 lines, shown from its source
The paper's code and data availability statement is in the Data section.
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Cite
This paper
Vystrčilová, M., Sridhar, S., Burg, M. F., Khani, M. H., Karamanlis, D., Schreyer, H. M., Ramakrishna, V., Krüppel, S., Zapp, S. J., Mietsch, M., Gollisch, T., & Ecker, A. S. (2026). Spatial Adaptation of Primate Retinal Ganglion Cells Between Artificial and Natural Stimuli. eNeuro, 13(5), ENEURO.0060-26.2026. https://
BibTeX
@article{vystrcilova2026
author = {Vystrčilová, Michaela and Sridhar, Shashwat and Burg, Max F 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 Gollisch, Tim and Ecker, Alexander S},
title = {{Spatial Adaptation of Primate Retinal Ganglion Cells Between Artificial and Natural Stimuli}},
journal = {eNeuro},
year = {2026},
month = may,
volume = {13},
number = {5},
pages = {ENEURO.0060--26.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/
url = {https://
pmid = {41856791},
pmcid = {PMC13138850}
}
RIS
TY - JOUR
AU - Vystrčilová, Michaela
AU - Sridhar, Shashwat
AU - Burg, Max F
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 - Gollisch, Tim
AU - Ecker, Alexander S
TI - Spatial Adaptation of Primate Retinal Ganglion Cells Between Artificial and Natural Stimuli
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/
VL - 13
IS - 5
SP - ENEURO.0060
EP - 26.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/
UR - https://
LA - en
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