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Abstract
How neurons detect the direction of motion is a prime example of neural computation: Motion vision is found in the visual systems of virtually all sighted animals, it is important for survival, and it requires interesting computations with well-defined linear and nonlinear processing steps-yet the whole process is of moderate complexity. The genetic methods available in the fruit fly Drosophila and the charting of a connectome of its visual system have led to rapid progress and unprecedented detail in our understanding of how neurons compute the direction of motion in this organism. The picture that emerged incorporates not only the identity, morphology, and synaptic connectivity of each neuron involved but also its neurotransmitters, its receptors, and their subcellular localization. Together with the neurons' membrane potential responses to visual stimulation, this information provides the basis for a biophysically realistic model of the circuit that computes the direction of visual motion.
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Affiliation(s)
- Alexander Borst
- Max Planck Institute for Biological Intelligence, Martinsried, Germany; ,
| | - Lukas N Groschner
- Max Planck Institute for Biological Intelligence, Martinsried, Germany; ,
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2
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Currier TA, Pang MM, Clandinin TR. Visual processing in the fly, from photoreceptors to behavior. Genetics 2023; 224:iyad064. [PMID: 37128740 PMCID: PMC10213501 DOI: 10.1093/genetics/iyad064] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/22/2023] [Indexed: 05/03/2023] Open
Abstract
Originally a genetic model organism, the experimental use of Drosophila melanogaster has grown to include quantitative behavioral analyses, sophisticated perturbations of neuronal function, and detailed sensory physiology. A highlight of these developments can be seen in the context of vision, where pioneering studies have uncovered fundamental and generalizable principles of sensory processing. Here we begin with an overview of vision-guided behaviors and common methods for probing visual circuits. We then outline the anatomy and physiology of brain regions involved in visual processing, beginning at the sensory periphery and ending with descending motor control. Areas of focus include contrast and motion detection in the optic lobe, circuits for visual feature selectivity, computations in support of spatial navigation, and contextual associative learning. Finally, we look to the future of fly visual neuroscience and discuss promising topics for further study.
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Affiliation(s)
- Timothy A Currier
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michelle M Pang
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
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3
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Gruntman E, Reimers P, Romani S, Reiser MB. Non-preferred contrast responses in the Drosophila motion pathways reveal a receptive field structure that explains a common visual illusion. Curr Biol 2021; 31:5286-5298.e7. [PMID: 34672960 DOI: 10.1016/j.cub.2021.09.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 09/14/2021] [Accepted: 09/24/2021] [Indexed: 10/20/2022]
Abstract
Diverse sensory systems, from audition to thermosensation, feature a separation of inputs into ON (increments) and OFF (decrements) signals. In the Drosophila visual system, separate ON and OFF pathways compute the direction of motion, yet anatomical and functional studies have identified some crosstalk between these channels. We used this well-studied circuit to ask whether the motion computation depends on ON-OFF pathway crosstalk. Using whole-cell electrophysiology, we recorded visual responses of T4 (ON) and T5 (OFF) cells, mapped their composite ON-OFF receptive fields, and found that they share a similar spatiotemporal structure. We fit a biophysical model to these receptive fields that accurately predicts directionally selective T4 and T5 responses to both ON and OFF moving stimuli. This model also provides a detailed mechanistic explanation for the directional preference inversion in response to the prominent reverse-phi illusion. Finally, we used the steering responses of tethered flying flies to validate the model's predicted effects of varying stimulus parameters on the behavioral turning inversion.
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Affiliation(s)
- Eyal Gruntman
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA, USA.
| | - Pablo Reimers
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA, USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA, USA
| | - Michael B Reiser
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA, USA.
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Kirkels LAMH, Zhang W, Duijnhouwer J, van Wezel RJA. Opto-locomotor reflexes of mice to reverse-phi stimuli. J Vis 2020; 20:7. [PMID: 32097483 PMCID: PMC7343431 DOI: 10.1167/jov.20.2.7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
In a reverse-phi stimulus, the contrast luminance of moving dots is reversed each displacement step. Under those conditions, the direction of the moving dots is perceived in the direction opposite of the displacement direction of the dots. In this study, we investigate if mice respond oppositely to phi and reverse-phi stimuli. Mice ran head-fixed on a Styrofoam ball floating on pressurized air at the center of a large dome. We projected random dot patterns that were displaced rightward or leftward, using either a phi or a reverse-phi stimulus. For phi stimuli, changes in direction caused the mice to reflexively compensate and adjust their running direction in the direction of the displaced pattern. We show that for reverse-phi stimuli mice compensate in the direction opposite to the displacement direction of the dots, in accordance with the perceived direction of displacement in humans for reverse-phi stimuli.
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Ketkar MD, Sporar K, Gür B, Ramos-Traslosheros G, Seifert M, Silies M. Luminance Information Is Required for the Accurate Estimation of Contrast in Rapidly Changing Visual Contexts. Curr Biol 2020; 30:657-669.e4. [PMID: 32008904 DOI: 10.1016/j.cub.2019.12.038] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Revised: 11/01/2019] [Accepted: 12/11/2019] [Indexed: 11/28/2022]
Abstract
Visual perception scales with changes in the visual stimulus, or contrast, irrespective of background illumination. However, visual perception is challenged when adaptation is not fast enough to deal with sudden declines in overall illumination, for example, when gaze follows a moving object from bright sunlight into a shaded area. Here, we show that the visual system of the fly employs a solution by propagating a corrective luminance-sensitive signal. We use in vivo 2-photon imaging and behavioral analyses to demonstrate that distinct OFF-pathway inputs encode contrast and luminance. Predictions of contrast-sensitive neuronal responses show that contrast information alone cannot explain behavioral responses in sudden dim light. The luminance-sensitive pathway via the L3 neuron is required for visual processing in such rapidly changing light conditions, ensuring contrast constancy when pure contrast sensitivity underestimates a stimulus. Thus, retaining a peripheral feature, luminance, in visual processing is required for robust behavioral responses.
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Affiliation(s)
- Madhura D Ketkar
- Institute of Developmental Biology and Neurobiology, Johannes Gutenberg-Universität Mainz, Hanns-Dieter-Hüsch-Weg 15, Mainz 55128, Germany; European Neuroscience Institute Göttingen, a Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstr. 5, Göttingen 37077, Germany; International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of Göttingen, Justus-von-Liebig-Weg 11, Göttingen 37077, Germany
| | - Katja Sporar
- Institute of Developmental Biology and Neurobiology, Johannes Gutenberg-Universität Mainz, Hanns-Dieter-Hüsch-Weg 15, Mainz 55128, Germany; European Neuroscience Institute Göttingen, a Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstr. 5, Göttingen 37077, Germany; International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of Göttingen, Justus-von-Liebig-Weg 11, Göttingen 37077, Germany
| | - Burak Gür
- Institute of Developmental Biology and Neurobiology, Johannes Gutenberg-Universität Mainz, Hanns-Dieter-Hüsch-Weg 15, Mainz 55128, Germany; European Neuroscience Institute Göttingen, a Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstr. 5, Göttingen 37077, Germany; International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of Göttingen, Justus-von-Liebig-Weg 11, Göttingen 37077, Germany
| | - Giordano Ramos-Traslosheros
- Institute of Developmental Biology and Neurobiology, Johannes Gutenberg-Universität Mainz, Hanns-Dieter-Hüsch-Weg 15, Mainz 55128, Germany; European Neuroscience Institute Göttingen, a Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstr. 5, Göttingen 37077, Germany; International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of Göttingen, Justus-von-Liebig-Weg 11, Göttingen 37077, Germany
| | - Marvin Seifert
- European Neuroscience Institute Göttingen, a Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstr. 5, Göttingen 37077, Germany
| | - Marion Silies
- Institute of Developmental Biology and Neurobiology, Johannes Gutenberg-Universität Mainz, Hanns-Dieter-Hüsch-Weg 15, Mainz 55128, Germany; European Neuroscience Institute Göttingen, a Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstr. 5, Göttingen 37077, Germany.
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How fly neurons compute the direction of visual motion. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2019; 206:109-124. [PMID: 31691093 PMCID: PMC7069908 DOI: 10.1007/s00359-019-01375-9] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 10/16/2019] [Accepted: 10/23/2019] [Indexed: 10/25/2022]
Abstract
Detecting the direction of image motion is a fundamental component of visual computation, essential for survival of the animal. However, at the level of individual photoreceptors, the direction in which the image is shifting is not explicitly represented. Rather, directional motion information needs to be extracted from the photoreceptor array by comparing the signals of neighboring units over time. The exact nature of this process as implemented in the visual system of the fruit fly Drosophila melanogaster has been studied in great detail, and much progress has recently been made in determining the neural circuits giving rise to directional motion information. The results reveal the following: (1) motion information is computed in parallel ON and OFF pathways. (2) Within each pathway, T4 (ON) and T5 (OFF) cells are the first neurons to represent the direction of motion. Four subtypes of T4 and T5 cells exist, each sensitive to one of the four cardinal directions. (3) The core process of direction selectivity as implemented on the dendrites of T4 and T5 cells comprises both an enhancement of signals for motion along their preferred direction as well as a suppression of signals for motion along the opposite direction. This combined strategy ensures a high degree of direction selectivity right at the first stage where the direction of motion is computed. (4) At the subsequent processing stage, tangential cells spatially integrate direct excitation from ON and OFF-selective T4 and T5 cells and indirect inhibition from bi-stratified LPi cells activated by neighboring T4/T5 terminals, thus generating flow-field-selective responses.
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Salazar-Gatzimas E, Agrochao M, Fitzgerald JE, Clark DA. The Neuronal Basis of an Illusory Motion Percept Is Explained by Decorrelation of Parallel Motion Pathways. Curr Biol 2018; 28:3748-3762.e8. [PMID: 30471993 DOI: 10.1016/j.cub.2018.10.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 09/28/2018] [Accepted: 10/02/2018] [Indexed: 10/27/2022]
Abstract
Both vertebrates and invertebrates perceive illusory motion, known as "reverse-phi," in visual stimuli that contain sequential luminance increments and decrements. However, increment (ON) and decrement (OFF) signals are initially processed by separate visual neurons, and parallel elementary motion detectors downstream respond selectively to the motion of light or dark edges, often termed ON- and OFF-edges. It remains unknown how and where ON and OFF signals combine to generate reverse-phi motion signals. Here, we show that each of Drosophila's elementary motion detectors encodes motion by combining both ON and OFF signals. Their pattern of responses reflects combinations of increments and decrements that co-occur in natural motion, serving to decorrelate their outputs. These results suggest that the general principle of signal decorrelation drives the functional specialization of parallel motion detection channels, including their selectivity for moving light or dark edges.
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Affiliation(s)
- Emilio Salazar-Gatzimas
- Interdepartmental Neuroscience Program, Yale University, 333 Cedar Street, New Haven, CT 06511, USA
| | - Margarida Agrochao
- Department of Molecular Cellular and Developmental Biology, Yale University, 219 Prospect Street, New Haven, CT 06511, USA
| | - James E Fitzgerald
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Damon A Clark
- Interdepartmental Neuroscience Program, Yale University, 333 Cedar Street, New Haven, CT 06511, USA; Department of Molecular Cellular and Developmental Biology, Yale University, 219 Prospect Street, New Haven, CT 06511, USA; Department of Physics, Yale University, New Haven, CT 06511, USA.
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Abstract
Motion in the visual world provides critical information to guide the behavior of sighted animals. Furthermore, as visual motion estimation requires comparisons of signals across inputs and over time, it represents a paradigmatic and generalizable neural computation. Focusing on the Drosophila visual system, where an explosion of technological advances has recently accelerated experimental progress, we review our understanding of how, algorithmically and mechanistically, motion signals are first computed.
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Affiliation(s)
- Helen H Yang
- Department of Neurobiology, Stanford University, Stanford, California 94305, USA; .,Current affiliation: Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA;
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Stanford, California 94305, USA;
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