Linear and nonlinear chromatic integration in the mouse retina
2021-03-26 | journal article; research paper. A publication with affiliation to the University of Göttingen.
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Details
- Authors
- Khani, Mohammad Hossein ; Gollisch, Tim
- Abstract
- Abstract
The computations performed by a neural circuit depend on how it integrates its input signals into an output of its own. In the retina, ganglion cells integrate visual information over time, space, and chromatic channels. Unlike the former two, chromatic integration is largely unexplored. Analogous to classical studies of spatial integration, we here study chromatic integration in mouse retina by identifying chromatic stimuli for which activation from the green or UV color channel is maximally balanced by deactivation through the other color channel. This reveals nonlinear chromatic integration in subsets of On, Off, and On–Off ganglion cells. Unlike the latter two, nonlinear On cells display response suppression rather than activation under balanced chromatic stimulation. Furthermore, nonlinear chromatic integration occurs independently of nonlinear spatial integration, depends on contributions from the rod pathway and on surround inhibition, and may provide information about chromatic boundaries, such as the skyline in natural scenes.
This study shows that ganglion cells in mouse retina integrate chromatic visual signals either linearly or nonlinearly. Nonlinear chromatic integration depends on rod photoreceptor activity and on surround inhibition and may help detect chromatic boundaries, such as the skyline in natural scenes. - Issue Date
- 26-March-2021
- Journal
- Nature Communications
- Project
- SFB 1456: Mathematik des Experiments: Die Herausforderung indirekter Messungen in den Naturwissenschaften
SFB 1456 | Cluster B: Data with Incomplete Information
SFB 1456 | Cluster B | B05: Inference of functional networks in the neuronal circuit of the retina from large-scale spike-train recordings - eISSN
- 2041-1723
- Language
- English
- Sponsor
- Deutsche Forschungsgemeinschaft (German Research Foundation) https://doi.org/10.13039/501100001659
EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) https://doi.org/10.13039/100010663