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      Seeing Natural Images through the Eye of a Fly with Remote Focusing Two-Photon Microscopy

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          Summary

          Visual systems of many animals, including the fruit fly Drosophila, represent the surrounding space as 2D maps, formed by populations of neurons. Advanced genetic tools make the fly visual system especially well accessible. However, in typical in vivo preparations for two-photon calcium imaging, relatively few neurons can be recorded at the same time. Here, we present an extension to a conventional two-photon microscope, based on remote focusing, which enables real-time rotation of the imaging plane, and thus flexible alignment to cellular structures, without resolution or speed trade-off. We simultaneously record from over 100 neighboring cells spanning the 2D retinotopic map. We characterize its representation of moving natural images, which we find is comparable to noise predictions. Our method increases throughput 10-fold and allows us to visualize a significant fraction of the fly's visual field. Furthermore, our system can be applied in general for a more flexible investigation of neural circuits.

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          Highlights

          • •

            A remote-focusing two-photon module for real-time rotation of the imaging plane

          • •

            Simultaneously recording >100 cells of the 2D retinotopic map in Drosophila

          • •

            Calcium imaging of L2 and Mi1 shows their dynamic representation of natural images

          • •

            Linear properties of L2 and Mi1 largely predict dynamic natural scene responses

          Abstract

          Optical Imaging; Sensory Neuroscience; Methodology in Biological Sciences

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          Most cited references48

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          Spatio-temporal correlations and visual signalling in a complete neuronal population.

          Jonathan W. Pillow, Jonathon Shlens, Liam Paninski … (2008)
          Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20% more information about the visual scene than decoding under the assumption of independence, and preserves 40% more visual information than optimal linear decoding. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.
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            Design principles of insect and vertebrate visual systems.

            Joshua Sanes, S Lawrence Zipursky (2010)
            A century ago, Cajal noted striking similarities between the neural circuits that underlie vision in vertebrates and flies. Over the past few decades, structural and functional studies have provided strong support for Cajal's view. In parallel, genetic studies have revealed some common molecular mechanisms controlling development of vertebrate and fly visual systems and suggested that they share a common evolutionary origin. Here, we review these shared features, focusing on the first several layers-retina, optic tectum (superior colliculus), and lateral geniculate nucleus in vertebrates; and retina, lamina, and medulla in fly. We argue that vertebrate and fly visual circuits utilize common design principles and that taking advantage of this phylogenetic conservation will speed progress in elucidating both functional strategies and developmental mechanisms, as has already occurred in other areas of neurobiology ranging from electrical signaling and synaptic plasticity to neurogenesis and axon guidance. Copyright 2010 Elsevier Inc. All rights reserved.
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              Independent component filters of natural images compared with simple cells in primary visual cortex.

              J. H. van Hateren, A. van der Schaaf (1998)
              Properties of the receptive fields of simple cells in macaque cortex were compared with properties of independent component filters generated by independent component analysis (ICA) on a large set of natural images. Histograms of spatial frequency bandwidth, orientation tuning bandwidth, aspect ratio and length of the receptive fields match well. This indicates that simple cells are well tuned to the expected statistics of natural stimuli. There is no match, however, in calculated and measured distributions for the peak of the spatial frequency response: the filters produced by ICA do not vary their spatial scale as much as simple cells do, but are fixed to scales close to the finest ones allowed by the sampling lattice. Possible ways to resolve this discrepancy are discussed.
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                Author and article information

                Contributors
                Anna Schuetzenberger
                Alexander Borst
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 17 May 2020
                Publication date Collection: 26 June 2020
                Publication date (Electronic): 17 May 2020
                Volume: 23
                Issue: 6
                Electronic Location Identifier: 101170
                Affiliations
                [1 ]Department Circuits - Computation - Models, Max-Planck-Institute of Neurobiology, 82152 Planegg, Germany
                [2 ]Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, 82152 Planegg, Germany
                Author notes
                [∗ ]Corresponding author schuetzenberger@ 123456neuro.mpg.de
                [∗∗ ]Corresponding author borst@ 123456neuro.mpg.de
                [3]

                Lead Contact

                Article
                Publisher ID: S2589-0042(20)30355-2 Publisher ID: 101170
                DOI: 10.1016/j.isci.2020.101170
                PMC ID: 7270611
                PubMed ID: 32502966
                SO-VID: 6fa0fe44-6aad-4b39-8e2f-1bf5ee55a301
                Copyright © © 2020 The Author(s)
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 18 February 2020
                Date revision received : 2 April 2020
                Date accepted : 12 May 2020
                Categories
                Subject: Article

                Keywords: optical imaging,sensory neuroscience,methodology in biological sciences
                Data availability:
                Keywords: optical imaging, sensory neuroscience, methodology in biological sciences

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