A chromatic feature detector in the retina signals visual context changes.

2P imaging computational modelling convolutional neural networks mouse natural stimuli neuroscience retina visual ecology

Journal

eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614

Informations de publication

Date de publication:
04 Oct 2024
Historique:
received: 09 02 2023
accepted: 25 08 2024
medline: 4 10 2024
pubmed: 4 10 2024
entrez: 4 10 2024
Statut: epublish

Résumé

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

Identifiants

pubmed: 39365730
doi: 10.7554/eLife.86860
pii: 86860
doi:
pii:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Deutsche Forschungsgemeinschaft
ID : CRC 1233 project number 276693517
Organisme : Deutsche Forschungsgemeinschaft
ID : Heisenberg Professorship BE5601/8-1
Organisme : Deutsche Forschungsgemeinschaft
ID : EXC 2064 390727645
Organisme : Deutsche Forschungsgemeinschaft
ID : CRC 1456 project number 432680300
Organisme : Bundesministerium für Bildung und Forschung
ID : FKZ 01IS18039A
Organisme : NIH HHS
ID : NEI EY031029
Pays : United States
Organisme : NIH HHS
ID : NEI F30EY031565
Pays : United States
Organisme : European Research Council
ID : grant agreement No. 101041669
Pays : International
Organisme : NIH HHS
ID : NEI EY031329
Pays : United States
Organisme : NIH HHS
ID : NIGMS T32GM008152
Pays : United States

Informations de copyright

© 2024, Höfling et al.

Déclaration de conflit d'intérêts

LH, KS, CB, YD, YQ, DK, ZJ, GS, MB, PB, KF, AE, TE No competing interests declared

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Auteurs

Larissa Höfling (L)

Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.

Klaudia P Szatko (KP)

Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.

Christian Behrens (C)

Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.

Yuyao Deng (Y)

Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.

Yongrong Qiu (Y)

Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.

David Alexander Klindt (DA)

SLAC National Accelerator Laboratory, Stanford University, Menlo Park, United States.

Zachary Jessen (Z)

Feinberg School of Medicine, Department of Ophthalmology, Northwestern University, Chicago, United States.

Gregory W Schwartz (GW)

Feinberg School of Medicine, Department of Ophthalmology, Northwestern University, Chicago, United States.

Matthias Bethge (M)

Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.
Tübingen AI Center, University of Tübingen, Tübingen, Germany.

Philipp Berens (P)

Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.
Tübingen AI Center, University of Tübingen, Tübingen, Germany.
Hertie Institute for AI in Brain Health, Tübingen, Germany.

Katrin Franke (K)

Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.

Alexander S Ecker (AS)

Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Göttingen, Germany.
Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.

Thomas Euler (T)

Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.

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