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Neuron. 2018 Jul 11;99(1):117-134.e11. doi: 10.1016/j.neuron.2018.06.001. Epub 2018 Jun 21.

How Diverse Retinal Functions Arise from Feedback at the First Visual Synapse.

Author information

1
Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland; Institute of Molecular and Clinical Ophthalmology Basel, 4031 Basel, Switzerland; Faculty of Natural Sciences, University of Basel, 4003 Basel, Switzerland.
2
Bio Engineering Laboratory, Department of Biosystems Science and Engineering of ETH Zurich, 4058 Basel, Switzerland.
3
Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland; Institute of Molecular and Clinical Ophthalmology Basel, 4031 Basel, Switzerland.
4
Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland.
5
Department of Physics, Ecole Normale Supérieure, 75005 Paris, France; Laboratoire de Physique Statistique, École Normale Supérieure, PSL Research University; Université Paris Diderot Sorbonne Paris-Cité; Sorbonne Universités UPMC Univ Paris 06; CNRS, 75005 Paris, France; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA. Electronic address: rava@ens.fr.
6
Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland; Institute of Molecular and Clinical Ophthalmology Basel, 4031 Basel, Switzerland; Department of Ophthalmology, University of Basel, 4031 Basel, Switzerland. Electronic address: botond.roska@iob.ch.

Abstract

Many brain regions contain local interneurons of distinct types. How does an interneuron type contribute to the input-output transformations of a given brain region? We addressed this question in the mouse retina by chemogenetically perturbing horizontal cells, an interneuron type providing feedback at the first visual synapse, while monitoring the light-driven spiking activity in thousands of ganglion cells, the retinal output neurons. We uncovered six reversible perturbation-induced effects in the response dynamics and response range of ganglion cells. The effects were enhancing or suppressive, occurred in different response epochs, and depended on the ganglion cell type. A computational model of the retinal circuitry reproduced all perturbation-induced effects and led us to assign specific functions to horizontal cells with respect to different ganglion cell types. Our combined experimental and theoretical work reveals how a single interneuron type can differentially shape the dynamical properties of distinct output channels of a brain region.

KEYWORDS:

cell type; computation; ganglion cell; horizontal cell; inhibition; interneuron; model; neuronal circuit; non-linear neural processing; retina

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