1
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Ammer G, Serbe-Kamp E, Mauss AS, Richter FG, Fendl S, Borst A. Multilevel visual motion opponency in Drosophila. Nat Neurosci 2023; 26:1894-1905. [PMID: 37783895 PMCID: PMC10620086 DOI: 10.1038/s41593-023-01443-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 08/30/2023] [Indexed: 10/04/2023]
Abstract
Inhibitory interactions between opponent neuronal pathways constitute a common circuit motif across brain areas and species. However, in most cases, synaptic wiring and biophysical, cellular and network mechanisms generating opponency are unknown. Here, we combine optogenetics, voltage and calcium imaging, connectomics, electrophysiology and modeling to reveal multilevel opponent inhibition in the fly visual system. We uncover a circuit architecture in which a single cell type implements direction-selective, motion-opponent inhibition at all three network levels. This inhibition, mediated by GluClα receptors, is balanced with excitation in strength, despite tenfold fewer synapses. The different opponent network levels constitute a nested, hierarchical structure operating at increasing spatiotemporal scales. Electrophysiology and modeling suggest that distributing this computation over consecutive network levels counteracts a reduction in gain, which would result from integrating large opposing conductances at a single instance. We propose that this neural architecture provides resilience to noise while enabling high selectivity for relevant sensory information.
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Affiliation(s)
- Georg Ammer
- Max Planck Institute for Biological Intelligence, Martinsried, Germany.
| | - Etienne Serbe-Kamp
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Ludwig Maximilian University of Munich, Munich, Germany
| | - Alex S Mauss
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Florian G Richter
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Sandra Fendl
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Alexander Borst
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
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2
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Chen J, Gish CM, Fransen JW, Salazar-Gatzimas E, Clark DA, Borghuis BG. Direct comparison reveals algorithmic similarities in fly and mouse visual motion detection. iScience 2023; 26:107928. [PMID: 37810236 PMCID: PMC10550730 DOI: 10.1016/j.isci.2023.107928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/07/2023] [Accepted: 09/12/2023] [Indexed: 10/10/2023] Open
Abstract
Evolution has equipped vertebrates and invertebrates with neural circuits that selectively encode visual motion. While similarities in the computations performed by these circuits in mouse and fruit fly have been noted, direct experimental comparisons have been lacking. Because molecular mechanisms and neuronal morphology in the two species are distinct, we directly compared motion encoding in these two species at the algorithmic level, using matched stimuli and focusing on a pair of analogous neurons, the mouse ON starburst amacrine cell (ON SAC) and Drosophila T4 neurons. We find that the cells share similar spatiotemporal receptive field structures, sensitivity to spatiotemporal correlations, and tuning to sinusoidal drifting gratings, but differ in their responses to apparent motion stimuli. Both neuron types showed a response to summed sinusoids that deviates from models for motion processing in these cells, underscoring the similarities in their processing and identifying response features that remain to be explained.
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Affiliation(s)
- Juyue Chen
- Interdepartmental Neurosciences Program, Yale University, New Haven, CT 06511, USA
| | - Caitlin M Gish
- Department of Physics, Yale University, New Haven, CT 06511, USA
| | - James W Fransen
- Department of Anatomical Sciences and Neurobiology, University of Louisville, Louisville, KY 40202, USA
| | | | - Damon A Clark
- Interdepartmental Neurosciences Program, Yale University, New Haven, CT 06511, USA
- Department of Physics, Yale University, New Haven, CT 06511, USA
- Department of Molecular, Cellular, Developmental Biology, Yale University, New Haven, CT 06511, USA
- Department of Neuroscience, Yale University, New Haven, CT 06511, USA
| | - Bart G Borghuis
- Department of Anatomical Sciences and Neurobiology, University of Louisville, Louisville, KY 40202, USA
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3
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Mano O, Choi M, Tanaka R, Creamer MS, Matos NCB, Shomar JW, Badwan BA, Clandinin TR, Clark DA. Long-timescale anti-directional rotation in Drosophila optomotor behavior. eLife 2023; 12:e86076. [PMID: 37751469 PMCID: PMC10522332 DOI: 10.7554/elife.86076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 09/12/2023] [Indexed: 09/28/2023] Open
Abstract
Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied Drosophila melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such 'anti-directional turning' is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses.
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Affiliation(s)
- Omer Mano
- Department of Molecular, Cellular, and Developmental Biology, Yale UniversityNew HavenUnited States
| | - Minseung Choi
- Department of Neurobiology, Stanford UniversityStanfordUnited States
| | - Ryosuke Tanaka
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
| | - Matthew S Creamer
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
| | - Natalia CB Matos
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
| | - Joseph W Shomar
- Department of Physics, Yale UniversityNew HavenUnited States
| | - Bara A Badwan
- Department of Chemical Engineering, Yale UniversityNew HavenUnited States
| | | | - Damon A Clark
- Department of Molecular, Cellular, and Developmental Biology, Yale UniversityNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
- Department of Physics, Yale UniversityNew HavenUnited States
- Department of Neuroscience, Yale UniversityNew HavenUnited States
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4
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Chiappe ME. Circuits for self-motion estimation and walking control in Drosophila. Curr Opin Neurobiol 2023; 81:102748. [PMID: 37453230 DOI: 10.1016/j.conb.2023.102748] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/11/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023]
Abstract
The brain's evolution and operation are inextricably linked to animal movement, and critical functions, such as motor control, spatial perception, and navigation, rely on precise knowledge of body movement. Such internal estimates of self-motion emerge from the integration of mechanosensory and visual feedback with motor-related signals. Thus, this internal representation likely depends on the activity of circuits distributed across the central nervous system. However, the circuits responsible for self-motion estimation, and the exact mechanisms by which motor-sensory coordination occurs within these circuits remain poorly understood. Recent technological advances have positioned Drosophila melanogaster as an advantageous model for investigating the emergence, maintenance, and utilization of self-motion representations during naturalistic walking behaviors. In this review, I will illustrate how the adult fly is providing insights into the fundamental problems of self-motion computations and walking control, which have relevance for all animals.
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Affiliation(s)
- M Eugenia Chiappe
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal.
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5
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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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6
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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: 13] [Impact Index Per Article: 13.0] [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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7
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Braun A, Borst A, Meier M. Disynaptic inhibition shapes tuning of OFF-motion detectors in Drosophila. Curr Biol 2023:S0960-9822(23)00601-2. [PMID: 37236181 DOI: 10.1016/j.cub.2023.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 04/02/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023]
Abstract
The circuitry underlying the detection of visual motion in Drosophila melanogaster is one of the best studied networks in neuroscience. Lately, electron microscopy reconstructions, algorithmic models, and functional studies have proposed a common motif for the cellular circuitry of an elementary motion detector based on both supralinear enhancement for preferred direction and sublinear suppression for null-direction motion. In T5 cells, however, all columnar input neurons (Tm1, Tm2, Tm4, and Tm9) are excitatory. So, how is null-direction suppression realized there? Using two-photon calcium imaging in combination with thermogenetics, optogenetics, apoptotics, and pharmacology, we discovered that it is via CT1, the GABAergic large-field amacrine cell, where the different processes have previously been shown to act in an electrically isolated way. Within each column, CT1 receives excitatory input from Tm9 and Tm1 and provides the sign-inverted, now inhibitory input signal onto T5. Ablating CT1 or knocking down GABA-receptor subunit Rdl significantly broadened the directional tuning of T5 cells. It thus appears that the signal of Tm1 and Tm9 is used both as an excitatory input for preferred direction enhancement and, through a sign inversion within the Tm1/Tm9-CT1 microcircuit, as an inhibitory input for null-direction suppression.
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Affiliation(s)
- Amalia Braun
- Max Planck Institute for Biological Intelligence, Department of Circuits - Computation - Models, Am Klopferspitz 18, 82152 Martinsried, Germany.
| | - Alexander Borst
- Max Planck Institute for Biological Intelligence, Department of Circuits - Computation - Models, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Matthias Meier
- Max Planck Institute for Biological Intelligence, Department of Circuits - Computation - Models, Am Klopferspitz 18, 82152 Martinsried, Germany.
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8
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Wu Z, Guo A. Bioinspired figure-ground discrimination via visual motion smoothing. PLoS Comput Biol 2023; 19:e1011077. [PMID: 37083880 PMCID: PMC10155969 DOI: 10.1371/journal.pcbi.1011077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/03/2023] [Accepted: 04/04/2023] [Indexed: 04/22/2023] Open
Abstract
Flies detect and track moving targets among visual clutter, and this process mainly relies on visual motion. Visual motion is analyzed or computed with the pathway from the retina to T4/T5 cells. The computation of local directional motion was formulated as an elementary movement detector (EMD) model more than half a century ago. Solving target detection or figure-ground discrimination problems can be equivalent to extracting boundaries between a target and the background based on the motion discontinuities in the output of a retinotopic array of EMDs. Individual EMDs cannot measure true velocities, however, due to their sensitivity to pattern properties such as luminance contrast and spatial frequency content. It remains unclear how local directional motion signals are further integrated to enable figure-ground discrimination. Here, we present a computational model inspired by fly motion vision. Simulations suggest that the heavily fluctuating output of an EMD array is naturally surmounted by a lobula network, which is hypothesized to be downstream of the local motion detectors and have parallel pathways with distinct directional selectivity. The lobula network carries out a spatiotemporal smoothing operation for visual motion, especially across time, enabling the segmentation of moving figures from the background. The model qualitatively reproduces experimental observations in the visually evoked response characteristics of one type of lobula columnar (LC) cell. The model is further shown to be robust to natural scene variability. Our results suggest that the lobula is involved in local motion-based target detection.
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Affiliation(s)
- Zhihua Wu
- School of Life Sciences, Shanghai University, Shanghai, China
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Aike Guo
- School of Life Sciences, Shanghai University, Shanghai, China
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Zhuhai, Guangdong, China
- University of Chinese Academy of Sciences, Beijing, China
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9
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Mano O, Choi M, Tanaka R, Creamer MS, Matos NC, Shomar J, Badwan BA, Clandinin TR, Clark DA. Long timescale anti-directional rotation in Drosophila optomotor behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.06.523055. [PMID: 36711627 PMCID: PMC9882005 DOI: 10.1101/2023.01.06.523055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied D. melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such "anti-directional turning" is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses.
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Affiliation(s)
- Omer Mano
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06511, USA
| | - Minseung Choi
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Ryosuke Tanaka
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
| | - Matthew S. Creamer
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
| | - Natalia C.B. Matos
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
| | - Joseph Shomar
- Department of Physics, Yale University, New Haven, CT 06511, USA
| | - Bara A. Badwan
- Department of Chemical Engineering, Yale University, New Haven, CT 06511, USA
| | | | - Damon A. Clark
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06511, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
- Department of Physics, Yale University, New Haven, CT 06511, USA
- Department of Neuroscience, Yale University, New Haven, CT 06511, USA
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10
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Mishra A, Serbe-Kamp E, Borst A, Haag J. Voltage to Calcium Transformation Enhances Direction Selectivity in Drosophila T4 Neurons. J Neurosci 2023; 43:2497-2514. [PMID: 36849417 PMCID: PMC10082464 DOI: 10.1523/jneurosci.2297-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/03/2023] [Accepted: 02/08/2023] [Indexed: 03/01/2023] Open
Abstract
An important step in neural information processing is the transformation of membrane voltage into calcium signals leading to transmitter release. However, the effect of voltage to calcium transformation on neural responses to different sensory stimuli is not well understood. Here, we use in vivo two-photon imaging of genetically encoded voltage and calcium indicators, ArcLight and GCaMP6f, respectively, to measure responses in direction-selective T4 neurons of female Drosophila Comparison between ArcLight and GCaMP6f signals reveals calcium signals to have a significantly higher direction selectivity compared with voltage signals. Using these recordings, we build a model which transforms T4 voltage responses into calcium responses. Using a cascade of thresholding, temporal filtering and a stationary nonlinearity, the model reproduces experimentally measured calcium responses across different visual stimuli. These findings provide a mechanistic underpinning of the voltage to calcium transformation and show how this processing step, in addition to synaptic mechanisms on the dendrites of T4 cells, enhances direction selectivity in the output signal of T4 neurons. Measuring the directional tuning of postsynaptic vertical system (VS)-cells with inputs from other cells blocked, we found that, indeed, it matches the one of the calcium signal in presynaptic T4 cells.SIGNIFICANCE STATEMENT The transformation of voltage to calcium influx is an important step in the signaling cascade within a nerve cell. While this process has been intensely studied in the context of transmitter release mechanism, its consequences for information transmission and neural computation are unclear. Here, we measured both membrane voltage and cytosolic calcium levels in direction-selective cells of Drosophila in response to a large set of visual stimuli. We found direction selectivity in the calcium signal to be significantly enhanced compared with membrane voltage through a nonlinear transformation of voltage to calcium. Our findings highlight the importance of an additional step in the signaling cascade for information processing within single nerve cells.
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Affiliation(s)
- Abhishek Mishra
- Max Planck Institute for Biological Intelligence, 82152 Martinsried, Germany
- Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, 82152 Martinsried, Germany
| | - Etienne Serbe-Kamp
- Max Planck Institute for Biological Intelligence, 82152 Martinsried, Germany
| | - Alexander Borst
- Max Planck Institute for Biological Intelligence, 82152 Martinsried, Germany
- Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, 82152 Martinsried, Germany
| | - Juergen Haag
- Max Planck Institute for Biological Intelligence, 82152 Martinsried, Germany
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11
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Turner MH, Krieger A, Pang MM, Clandinin TR. Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila. eLife 2022; 11:e82587. [PMID: 36300621 PMCID: PMC9651947 DOI: 10.7554/elife.82587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/25/2022] [Indexed: 01/07/2023] Open
Abstract
Natural vision is dynamic: as an animal moves, its visual input changes dramatically. How can the visual system reliably extract local features from an input dominated by self-generated signals? In Drosophila, diverse local visual features are represented by a group of projection neurons with distinct tuning properties. Here, we describe a connectome-based volumetric imaging strategy to measure visually evoked neural activity across this population. We show that local visual features are jointly represented across the population, and a shared gain factor improves trial-to-trial coding fidelity. A subset of these neurons, tuned to small objects, is modulated by two independent signals associated with self-movement, a motor-related signal, and a visual motion signal associated with rotation of the animal. These two inputs adjust the sensitivity of these feature detectors across the locomotor cycle, selectively reducing their gain during saccades and restoring it during intersaccadic intervals. This work reveals a strategy for reliable feature detection during locomotion.
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Affiliation(s)
- Maxwell H Turner
- Department of Neurobiology, Stanford UniversityStanfordUnited States
| | - Avery Krieger
- Department of Neurobiology, Stanford UniversityStanfordUnited States
| | - Michelle M Pang
- Department of Neurobiology, Stanford UniversityStanfordUnited States
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12
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Gonzalez-Suarez AD, Zavatone-Veth JA, Chen J, Matulis CA, Badwan BA, Clark DA. Excitatory and inhibitory neural dynamics jointly tune motion detection. Curr Biol 2022; 32:3659-3675.e8. [PMID: 35868321 PMCID: PMC9474608 DOI: 10.1016/j.cub.2022.06.075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 05/03/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022]
Abstract
Neurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. Different motion detection circuits have different velocity sensitivity, but it remains untested how the response dynamics of individual cell types drive this tuning. Here, we sped up or slowed down specific neuron types in Drosophila's motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neuron types upstream of motion detectors increased their sensitivity to fast or slow visual motion, exposing distinct roles for excitatory and inhibitory dynamics in tuning directional signals, including a role for the amacrine cell CT1. A circuit model constrained by functional data and anatomy qualitatively reproduced the observed tuning changes. Overall, these results reveal how excitatory and inhibitory dynamics together tune a canonical circuit computation.
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Affiliation(s)
| | - Jacob A Zavatone-Veth
- Department of Physics, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Juyue Chen
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
| | | | - Bara A Badwan
- School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA
| | - Damon A Clark
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA; Department of Physics, Yale University, New Haven, CT 06511, USA; Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06511, USA; Department of Neuroscience, Yale University, New Haven, CT 06511, USA.
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13
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Henning M, Ramos-Traslosheros G, Gür B, Silies M. Populations of local direction-selective cells encode global motion patterns generated by self-motion. SCIENCE ADVANCES 2022; 8:eabi7112. [PMID: 35044821 PMCID: PMC8769539 DOI: 10.1126/sciadv.abi7112] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Self-motion generates visual patterns on the eye that are important for navigation. These optic flow patterns are encoded by the population of local direction–selective cells in the mouse retina, whereas in flies, local direction–selective T4/T5 cells are thought to be uniformly tuned. How complex global motion patterns can be computed downstream is unclear. We show that the population of T4/T5 cells in Drosophila encodes global motion patterns. Whereas the mouse retina encodes four types of optic flow, the fly visual system encodes six. This matches the larger number of degrees of freedom and the increased complexity of translational and rotational motion patterns during flight. The four uniformly tuned T4/T5 subtypes described previously represent a local subset of the population. Thus, a population code for global motion patterns appears to be a general coding principle of visual systems that matches local motion responses to modes of the animal’s movement.
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Affiliation(s)
- Miriam Henning
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University Mainz, Mainz 55128, Germany
- Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) and International Max Planck Research School (IMPRS) for Neurosciences at the University of Göttingen, Göttingen 37077, Germany
| | - Giordano Ramos-Traslosheros
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University Mainz, Mainz 55128, Germany
- Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) and International Max Planck Research School (IMPRS) for Neurosciences at the University of Göttingen, Göttingen 37077, Germany
| | - Burak Gür
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University Mainz, Mainz 55128, Germany
- Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) and International Max Planck Research School (IMPRS) for Neurosciences at the University of Göttingen, Göttingen 37077, Germany
| | - Marion Silies
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University Mainz, Mainz 55128, Germany
- Corresponding author.
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14
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Srivastava P, de Rosenroll G, Matsumoto A, Michaels T, Turple Z, Jain V, Sethuramanujam S, Murphy-Baum BL, Yonehara K, Awatramani GB. Spatiotemporal properties of glutamate input support direction selectivity in the dendrites of retinal starburst amacrine cells. eLife 2022; 11:81533. [PMID: 36346388 PMCID: PMC9674338 DOI: 10.7554/elife.81533] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022] Open
Abstract
The asymmetric summation of kinetically distinct glutamate inputs across the dendrites of retinal 'starburst' amacrine cells is one of the several mechanisms that have been proposed to underlie their direction-selective properties, but experimentally verifying input kinetics has been a challenge. Here, we used two-photon glutamate sensor (iGluSnFR) imaging to directly measure the input kinetics across individual starburst dendrites. We found that signals measured from proximal dendrites were relatively sustained compared to those measured from distal dendrites. These differences were observed across a range of stimulus sizes and appeared to be shaped mainly by excitatory rather than inhibitory network interactions. Temporal deconvolution analysis suggests that the steady-state vesicle release rate was ~3 times larger at proximal sites compared to distal sites. Using a connectomics-inspired computational model, we demonstrate that input kinetics play an important role in shaping direction selectivity at low stimulus velocities. Taken together, these results provide direct support for the 'space-time wiring' model for direction selectivity.
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Affiliation(s)
| | | | - Akihiro Matsumoto
- Danish Research Institute of Translational Neuroscience, Nordic-EMBL Partnership for Molecular Medicine, Department of Biomedicine, Aarhus UniversityAarhusDenmark
| | - Tracy Michaels
- Department of Biology, University of VictoriaVictoriaCanada
| | - Zachary Turple
- Department of Biology, University of VictoriaVictoriaCanada
| | - Varsha Jain
- Department of Biology, University of VictoriaVictoriaCanada
| | | | | | - Keisuke Yonehara
- Danish Research Institute of Translational Neuroscience, Nordic-EMBL Partnership for Molecular Medicine, Department of Biomedicine, Aarhus UniversityAarhusDenmark
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15
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Kohn JR, Portes JP, Christenson MP, Abbott LF, Behnia R. Flexible filtering by neural inputs supports motion computation across states and stimuli. Curr Biol 2021; 31:5249-5260.e5. [PMID: 34670114 DOI: 10.1016/j.cub.2021.09.061] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/10/2021] [Accepted: 09/22/2021] [Indexed: 01/05/2023]
Abstract
Sensory systems flexibly adapt their processing properties across a wide range of environmental and behavioral conditions. Such variable processing complicates attempts to extract a mechanistic understanding of sensory computations. This is evident in the highly constrained, canonical Drosophila motion detection circuit, where the core computation underlying direction selectivity is still debated despite extensive studies. Here we measured the filtering properties of neural inputs to the OFF motion-detecting T5 cell in Drosophila. We report state- and stimulus-dependent changes in the shape of these signals, which become more biphasic under specific conditions. Summing these inputs within the framework of a connectomic-constrained model of the circuit demonstrates that these shapes are sufficient to explain T5 responses to various motion stimuli. Thus, our stimulus- and state-dependent measurements reconcile motion computation with the anatomy of the circuit. These findings provide a clear example of how a basic circuit supports flexible sensory computation.
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Affiliation(s)
- Jessica R Kohn
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Jacob P Portes
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Matthias P Christenson
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - L F Abbott
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Rudy Behnia
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA.
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16
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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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17
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Mano O, Creamer MS, Badwan BA, Clark DA. Predicting individual neuron responses with anatomically constrained task optimization. Curr Biol 2021; 31:4062-4075.e4. [PMID: 34324832 DOI: 10.1016/j.cub.2021.06.090] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/24/2021] [Accepted: 06/29/2021] [Indexed: 01/28/2023]
Abstract
Artificial neural networks trained to solve sensory tasks can develop statistical representations that match those in biological circuits. However, it remains unclear whether they can reproduce properties of individual neurons. Here, we investigated how artificial networks predict individual neuron properties in the visual motion circuits of the fruit fly Drosophila. We trained anatomically constrained networks to predict movement in natural scenes, solving the same inference problem as fly motion detectors. Units in the artificial networks adopted many properties of analogous individual neurons, even though they were not explicitly trained to match these properties. Among these properties was the split into ON and OFF motion detectors, which is not predicted by classical motion detection models. The match between model and neurons was closest when models were trained to be robust to noise. These results demonstrate how anatomical, task, and noise constraints can explain properties of individual neurons in a small neural network.
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Affiliation(s)
- Omer Mano
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Department of Neuroscience, Yale University, New Haven, CT 06511, USA
| | - Matthew S Creamer
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
| | - Bara A Badwan
- School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA
| | - Damon A Clark
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Department of Neuroscience, Yale University, New Haven, CT 06511, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA; Department of Physics, Yale University, New Haven, CT 06511, USA.
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18
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Ramos-Traslosheros G, Silies M. The physiological basis for contrast opponency in motion computation in Drosophila. Nat Commun 2021; 12:4987. [PMID: 34404776 PMCID: PMC8371135 DOI: 10.1038/s41467-021-24986-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 07/07/2021] [Indexed: 12/02/2022] Open
Abstract
In Drosophila, direction-selective neurons implement a mechanism of motion computation similar to cortical neurons, using contrast-opponent receptive fields with ON and OFF subfields. It is not clear how the presynaptic circuitry of direction-selective neurons in the OFF pathway supports this computation if all major inputs are OFF-rectified neurons. Here, we reveal the biological substrate for motion computation in the OFF pathway. Three interneurons, Tm2, Tm9 and CT1, provide information about ON stimuli to the OFF direction-selective neuron T5 across its receptive field, supporting a contrast-opponent receptive field organization. Consistent with its prominent role in motion detection, variability in Tm9 receptive field properties transfers to T5, and calcium decrements in Tm9 in response to ON stimuli persist across behavioral states, while spatial tuning is sharpened by active behavior. Together, our work shows how a key neuronal computation is implemented by its constituent neuronal circuit elements to ensure direction selectivity.
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Affiliation(s)
- Giordano Ramos-Traslosheros
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University Mainz, Mainz, Germany
- International Max Planck Research School Neuroscienes and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of Göttingen, Göttingen, Germany
| | - Marion Silies
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University Mainz, Mainz, Germany.
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19
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Agrochao M, Tanaka R, Salazar-Gatzimas E, Clark DA. Mechanism for analogous illusory motion perception in flies and humans. Proc Natl Acad Sci U S A 2020; 117:23044-23053. [PMID: 32839324 PMCID: PMC7502748 DOI: 10.1073/pnas.2002937117] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Visual motion detection is one of the most important computations performed by visual circuits. Yet, we perceive vivid illusory motion in stationary, periodic luminance gradients that contain no true motion. This illusion is shared by diverse vertebrate species, but theories proposed to explain this illusion have remained difficult to test. Here, we demonstrate that in the fruit fly Drosophila, the illusory motion percept is generated by unbalanced contributions of direction-selective neurons' responses to stationary edges. First, we found that flies, like humans, perceive sustained motion in the stationary gradients. The percept was abolished when the elementary motion detector neurons T4 and T5 were silenced. In vivo calcium imaging revealed that T4 and T5 neurons encode the location and polarity of stationary edges. Furthermore, our proposed mechanistic model allowed us to predictably manipulate both the magnitude and direction of the fly's illusory percept by selectively silencing either T4 or T5 neurons. Interestingly, human brains possess the same mechanistic ingredients that drive our model in flies. When we adapted human observers to moving light edges or dark edges, we could manipulate the magnitude and direction of their percepts as well, suggesting that mechanisms similar to the fly's may also underlie this illusion in humans. By taking a comparative approach that exploits Drosophila neurogenetics, our results provide a causal, mechanistic account for a long-known visual illusion. These results argue that this illusion arises from architectures for motion detection that are shared across phyla.
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Affiliation(s)
- Margarida Agrochao
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511
| | - Ryosuke Tanaka
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511
| | | | - Damon A Clark
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511;
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511
- Department of Physics, Yale University, New Haven, CT 06511
- Department of Neuroscience, Yale University, New Haven, CT 06511
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20
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Zavatone-Veth JA, Badwan BA, Clark DA. A minimal synaptic model for direction selective neurons in Drosophila. J Vis 2020; 20:2. [PMID: 32040161 PMCID: PMC7343402 DOI: 10.1167/jov.20.2.2] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Visual motion estimation is a canonical neural computation. In Drosophila, recent advances have identified anatomic and functional circuitry underlying direction-selective computations. Models with varying levels of abstraction have been proposed to explain specific experimental results but have rarely been compared across experiments. Here we use the wealth of available anatomical and physiological data to construct a minimal, biophysically inspired synaptic model for Drosophila’s first-order direction-selective T4 cells. We show how this model relates mathematically to classical models of motion detection, including the Hassenstein-Reichardt correlator model. We used numerical simulation to test how well this synaptic model could reproduce measurements of T4 cells across many datasets and stimulus modalities. These comparisons include responses to sinusoid gratings, to apparent motion stimuli, to stochastic stimuli, and to natural scenes. Without fine-tuning this model, it sufficed to reproduce many, but not all, response properties of T4 cells. Since this model is flexible and based on straightforward biophysical properties, it provides an extensible framework for developing a mechanistic understanding of T4 neural response properties. Moreover, it can be used to assess the sufficiency of simple biophysical mechanisms to describe features of the direction-selective computation and identify where our understanding must be improved.
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21
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22
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Roy A, Osik JJ, Meschede-Krasa B, Alford WT, Leman DP, Van Hooser SD. Synaptic and intrinsic mechanisms underlying development of cortical direction selectivity. eLife 2020; 9:e58509. [PMID: 32701059 PMCID: PMC7440916 DOI: 10.7554/elife.58509] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 07/23/2020] [Indexed: 02/02/2023] Open
Abstract
Modifications of synaptic inputs and cell-intrinsic properties both contribute to neuronal plasticity and development. To better understand these mechanisms, we undertook an intracellular analysis of the development of direction selectivity in the ferret visual cortex, which occurs rapidly over a few days after eye opening. We found strong evidence of developmental changes in linear spatiotemporal receptive fields of simple cells, implying alterations in circuit inputs. Further, this receptive field plasticity was accompanied by increases in near-spike-threshold excitability and input-output gain that resulted in dramatically increased spiking responses in the experienced state. Increases in subthreshold membrane responses induced by the receptive field plasticity and the increased input-output spiking gain were both necessary to explain the elevated firing rates in experienced ferrets. These results demonstrate that cortical direction selectivity develops through a combination of plasticity in inputs and in cell-intrinsic properties.
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Affiliation(s)
- Arani Roy
- Department of Biology, Brandeis UniversityWalthamUnited States
- Volen Center for Complex Systems, Brandeis UniversityWalthamUnited States
| | - Jason J Osik
- Department of Biology, Brandeis UniversityWalthamUnited States
- Volen Center for Complex Systems, Brandeis UniversityWalthamUnited States
| | | | - Wesley T Alford
- Department of Biology, Brandeis UniversityWalthamUnited States
| | - Daniel P Leman
- Department of Biology, Brandeis UniversityWalthamUnited States
| | - Stephen D Van Hooser
- Department of Biology, Brandeis UniversityWalthamUnited States
- Volen Center for Complex Systems, Brandeis UniversityWalthamUnited States
- Sloan-Swartz Center for Theoretical Neurobiology Brandeis UniversityWalthamUnited States
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23
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Isaacman-Beck J, Paik KC, Wienecke CFR, Yang HH, Fisher YE, Wang IE, Ishida IG, Maimon G, Wilson RI, Clandinin TR. SPARC enables genetic manipulation of precise proportions of cells. Nat Neurosci 2020; 23:1168-1175. [PMID: 32690967 PMCID: PMC7939234 DOI: 10.1038/s41593-020-0668-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 06/12/2020] [Indexed: 11/17/2022]
Abstract
Many experimental approaches rely on controlling gene expression in select subsets of cells within an individual animal. However, reproducibly targeting transgene expression to specific fractions of a genetically defined cell type is challenging. We developed Sparse Predictive Activity through Recombinase Competition (SPARC), a generalizable toolkit that can express any effector in precise proportions of post-mitotic cells in Drosophila. Using this approach, we demonstrate targeted expression of many effectors in several cell types and apply these tools to calcium imaging of individual neurons and optogenetic manipulation of sparse cell populations in vivo.
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Affiliation(s)
| | - Kristine C Paik
- Department of Neurobiology, Stanford University, Stanford, CA, USA.,Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | | | - Helen H Yang
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Yvette E Fisher
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Irving E Wang
- Department of Neurobiology, Stanford University, Stanford, CA, USA.,Freenome, South San Francisco, CA, USA
| | - Itzel G Ishida
- Laboratory of Integrative Brain Function and Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA
| | - Gaby Maimon
- Laboratory of Integrative Brain Function and Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA
| | - Rachel I Wilson
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
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24
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Tanaka R, Clark DA. Object-Displacement-Sensitive Visual Neurons Drive Freezing in Drosophila. Curr Biol 2020; 30:2532-2550.e8. [PMID: 32442466 PMCID: PMC8716191 DOI: 10.1016/j.cub.2020.04.068] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/22/2020] [Accepted: 04/24/2020] [Indexed: 11/26/2022]
Abstract
Visual systems are often equipped with neurons that detect small moving objects, which may represent prey, predators, or conspecifics. Although the processing properties of those neurons have been studied in diverse organisms, links between the proposed algorithms and animal behaviors or circuit mechanisms remain elusive. Here, we have investigated behavioral function, computational algorithm, and neurochemical mechanisms of an object-selective neuron, LC11, in Drosophila. With genetic silencing and optogenetic activation, we show that LC11 is necessary for a visual object-induced stopping behavior in walking flies, a form of short-term freezing, and its activity can promote stopping. We propose a new quantitative model for small object selectivity based on the physiology and anatomy of LC11 and its inputs. The model accurately reproduces LC11 responses by pooling fast-adapting, tightly size-tuned inputs. Direct visualization of neurotransmitter inputs to LC11 confirmed the model conjectures about upstream processing. Our results demonstrate how adaptation can enhance selectivity for behaviorally relevant, dynamic visual features.
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Affiliation(s)
- Ryosuke Tanaka
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
| | - Damon A Clark
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA; Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06511, USA; Department of Physics, Yale University, New Haven, CT 06511, USA; Department of Neuroscience, Yale University, New Haven, CT 06511, USA.
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25
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Yildizoglu T, Riegler C, Fitzgerald JE, Portugues R. A Neural Representation of Naturalistic Motion-Guided Behavior in the Zebrafish Brain. Curr Biol 2020; 30:2321-2333.e6. [PMID: 32386533 DOI: 10.1016/j.cub.2020.04.043] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 03/13/2020] [Accepted: 04/20/2020] [Indexed: 11/20/2022]
Abstract
All animals must transform ambiguous sensory data into successful behavior. This requires sensory representations that accurately reflect the statistics of natural stimuli and behavior. Multiple studies show that visual motion processing is tuned for accuracy under naturalistic conditions, but the sensorimotor circuits extracting these cues and implementing motion-guided behavior remain unclear. Here we show that the larval zebrafish retina extracts a diversity of naturalistic motion cues, and the retinorecipient pretectum organizes these cues around the elements of behavior. We find that higher-order motion stimuli, gliders, induce optomotor behavior matching expectations from natural scene analyses. We then image activity of retinal ganglion cell terminals and pretectal neurons. The retina exhibits direction-selective responses across glider stimuli, and anatomically clustered pretectal neurons respond with magnitudes matching behavior. Peripheral computations thus reflect natural input statistics, whereas central brain activity precisely codes information needed for behavior. This general principle could organize sensorimotor transformations across animal species.
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Affiliation(s)
- Tugce Yildizoglu
- Max Planck Institute of Neurobiology, Research Group of Sensorimotor Control, Martinsried 82152, Germany
| | - Clemens Riegler
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA; Department of Neurobiology, Faculty of Life Sciences, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria
| | - James E Fitzgerald
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
| | - Ruben Portugues
- Max Planck Institute of Neurobiology, Research Group of Sensorimotor Control, Martinsried 82152, Germany; Institute of Neuroscience, Technical University of Munich, Munich 80802, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich 80802, Germany.
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26
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Matulis CA, Chen J, Gonzalez-Suarez AD, Behnia R, Clark DA. Heterogeneous Temporal Contrast Adaptation in Drosophila Direction-Selective Circuits. Curr Biol 2020; 30:222-236.e6. [PMID: 31928874 PMCID: PMC7003801 DOI: 10.1016/j.cub.2019.11.077] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 11/06/2019] [Accepted: 11/26/2019] [Indexed: 11/23/2022]
Abstract
In visual systems, neurons adapt both to the mean light level and to the range of light levels, or the contrast. Contrast adaptation has been studied extensively, but it remains unclear how it is distributed among neurons in connected circuits, and how early adaptation affects subsequent computations. Here, we investigated temporal contrast adaptation in neurons across Drosophila's visual motion circuitry. Several ON-pathway neurons showed strong adaptation to changes in contrast over time. One of these neurons, Mi1, showed almost complete adaptation on fast timescales, and experiments ruled out several potential mechanisms for its adaptive properties. When contrast adaptation reduced the gain in ON-pathway cells, it was accompanied by decreased motion responses in downstream direction-selective cells. Simulations show that contrast adaptation can substantially improve motion estimates in natural scenes. The benefits are larger for ON-pathway adaptation, which helps explain the heterogeneous distribution of contrast adaptation in these circuits.
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Affiliation(s)
- Catherine A Matulis
- Department of Physics, Yale University, 217 Prospect Street, New Haven, CT 06511, USA
| | - Juyue Chen
- Interdepartmental Neuroscience Program, Yale University, 333 Cedar Street, New Haven, CT 06510, USA
| | | | - Rudy Behnia
- Department of Neuroscience, Columbia University, 3227 Broadway, New York, NY 10027, USA
| | - Damon A Clark
- Department of Physics, Yale University, 217 Prospect Street, New Haven, CT 06511, USA; Interdepartmental Neuroscience Program, Yale University, 333 Cedar Street, New Haven, CT 06510, USA; Department of Molecular, Cellular, and Developmental Biology, Yale University, 260 Whitney Avenue, New Haven, CT 06511, USA; Department of Neuroscience, Yale University, 333 Cedar Street, New Haven, CT 06510, USA.
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27
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Dynamic Signal Compression for Robust Motion Vision in Flies. Curr Biol 2020; 30:209-221.e8. [PMID: 31928873 DOI: 10.1016/j.cub.2019.10.035] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 09/17/2019] [Accepted: 10/18/2019] [Indexed: 12/16/2022]
Abstract
Sensory systems need to reliably extract information from highly variable natural signals. Flies, for instance, use optic flow to guide their course and are remarkably adept at estimating image velocity regardless of image statistics. Current circuit models, however, cannot account for this robustness. Here, we demonstrate that the Drosophila visual system reduces input variability by rapidly adjusting its sensitivity to local contrast conditions. We exhaustively map functional properties of neurons in the motion detection circuit and find that local responses are compressed by surround contrast. The compressive signal is fast, integrates spatially, and derives from neural feedback. Training convolutional neural networks on estimating the velocity of natural stimuli shows that this dynamic signal compression can close the performance gap between model and organism. Overall, our work represents a comprehensive mechanistic account of how neural systems attain the robustness to carry out survival-critical tasks in challenging real-world environments.
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28
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Gruntman E, Romani S, Reiser MB. The computation of directional selectivity in the Drosophila OFF motion pathway. eLife 2019; 8:e50706. [PMID: 31825313 PMCID: PMC6917495 DOI: 10.7554/elife.50706] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 11/30/2019] [Indexed: 01/23/2023] Open
Abstract
In flies, the direction of moving ON and OFF features is computed separately. T4 (ON) and T5 (OFF) are the first neurons in their respective pathways to extract a directionally selective response from their non-selective inputs. Our recent study of T4 found that the integration of offset depolarizing and hyperpolarizing inputs is critical for the generation of directional selectivity. However, T5s lack small-field inhibitory inputs, suggesting they may use a different mechanism. Here we used whole-cell recordings of T5 neurons and found a similar receptive field structure: fast depolarization and persistent, spatially offset hyperpolarization. By assaying pairwise interactions of local stimulation across the receptive field, we found no amplifying responses, only suppressive responses to the non-preferred motion direction. We then evaluated passive, biophysical models and found that a model using direct inhibition, but not the removal of excitation, can accurately predict T5 responses to a range of moving stimuli.
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Affiliation(s)
- Eyal Gruntman
- Janelia Research CampusHoward Hughes Medical InstituteAshburnUnited States
| | - Sandro Romani
- Janelia Research CampusHoward Hughes Medical InstituteAshburnUnited States
| | - Michael B Reiser
- Janelia Research CampusHoward Hughes Medical InstituteAshburnUnited States
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29
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de Andres-Bragado L, Sprecher SG. Mechanisms of vision in the fruit fly. CURRENT OPINION IN INSECT SCIENCE 2019; 36:25-32. [PMID: 31325739 DOI: 10.1016/j.cois.2019.06.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 06/17/2019] [Accepted: 06/21/2019] [Indexed: 06/10/2023]
Abstract
Vision is essential to maximize the efficiency of daily tasks such as feeding, avoiding predators or finding mating partners. An advantageous model is Drosophila melanogaster, since it offers tools that allow genetic and neuronal manipulation with high spatial and temporal resolution, which can be combined with behavioral, anatomical and physiological assays. Recent advances have expanded our knowledge on the neural circuitry underlying such important behaviors as color vision (role of reciprocal inhibition to enhance color signal at the level of the ommatidia); motion vision (motion-detection neurones receive both excitatory and inhibitory input), and sensory processing (role of the central complex in spatial navigation, and in orchestrating the information from other senses and the inner state). Research on synergies between pathways is shaping the field.
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Affiliation(s)
| | - Simon G Sprecher
- Department of Biology, University of Fribourg, Fribourg, Switzerland.
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30
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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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31
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Mano O, Creamer MS, Matulis CA, Salazar-Gatzimas E, Chen J, Zavatone-Veth JA, Clark DA. Using slow frame rate imaging to extract fast receptive fields. Nat Commun 2019; 10:4979. [PMID: 31672963 PMCID: PMC6823504 DOI: 10.1038/s41467-019-12974-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Accepted: 10/11/2019] [Indexed: 11/09/2022] Open
Abstract
In functional imaging, large numbers of neurons are measured during sensory stimulation or behavior. This data can be used to map receptive fields that describe neural associations with stimuli or with behavior. The temporal resolution of these receptive fields has traditionally been limited by image acquisition rates. However, even when acquisitions scan slowly across a population of neurons, individual neurons may be measured at precisely known times. Here, we apply a method that leverages the timing of neural measurements to find receptive fields with temporal resolutions higher than the image acquisition rate. We use this temporal super-resolution method to resolve fast voltage and glutamate responses in visual neurons in Drosophila and to extract calcium receptive fields from cortical neurons in mammals. We provide code to easily apply this method to existing datasets. This method requires no specialized hardware and can be used with any optical indicator of neural activity.
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Affiliation(s)
- Omer Mano
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06511, USA
| | - Matthew S Creamer
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA
| | | | | | - Juyue Chen
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA
| | | | - Damon A Clark
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06511, USA.
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA.
- Department of Physics, Yale University, New Haven, CT, 06511, USA.
- Department of Neuroscience, Yale University, New Haven, CT, 06511, USA.
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Chen J, Mandel HB, Fitzgerald JE, Clark DA. Asymmetric ON-OFF processing of visual motion cancels variability induced by the structure of natural scenes. eLife 2019; 8:e47579. [PMID: 31613221 PMCID: PMC6884396 DOI: 10.7554/elife.47579] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 10/12/2019] [Indexed: 02/05/2023] Open
Abstract
Animals detect motion using a variety of visual cues that reflect regularities in the natural world. Experiments in animals across phyla have shown that motion percepts incorporate both pairwise and triplet spatiotemporal correlations that could theoretically benefit motion computation. However, it remains unclear how visual systems assemble these cues to build accurate motion estimates. Here, we used systematic behavioral measurements of fruit fly motion perception to show how flies combine local pairwise and triplet correlations to reduce variability in motion estimates across natural scenes. By generating synthetic images with statistics controlled by maximum entropy distributions, we show that the triplet correlations are useful only when images have light-dark asymmetries that mimic natural ones. This suggests that asymmetric ON-OFF processing is tuned to the particular statistics of natural scenes. Since all animals encounter the world's light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.
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Affiliation(s)
- Juyue Chen
- Interdepartmental Neuroscience ProgramYale UniversityNew HavenUnited States
| | - Holly B Mandel
- Department of Molecular, Cellular and Developmental BiologyYale UniversityNew HavenUnited States
| | - James E Fitzgerald
- Janelia Research CampusHoward Hughes Medical InstituteAshburnUnited States
| | - Damon A Clark
- Interdepartmental Neuroscience ProgramYale UniversityNew HavenUnited States
- Department of Molecular, Cellular and Developmental BiologyYale UniversityNew HavenUnited States
- Department of PhysicsYale UniversityNew HavenUnited States
- Department of NeuroscienceYale UniversityNew HavenUnited States
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33
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Spatiotemporally Asymmetric Excitation Supports Mammalian Retinal Motion Sensitivity. Curr Biol 2019; 29:3277-3288.e5. [PMID: 31564498 PMCID: PMC6865067 DOI: 10.1016/j.cub.2019.08.048] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Revised: 08/15/2019] [Accepted: 08/20/2019] [Indexed: 11/20/2022]
Abstract
The detection of visual motion is a fundamental function of the visual system. How motion speed and direction are computed together at the cellular level, however, remains largely unknown. Here, we suggest a circuit mechanism by which excitatory inputs to direction-selective ganglion cells in the mouse retina become sensitive to the motion speed and direction of image motion. Electrophysiological, imaging, and connectomic analyses provide evidence that the dendrites of ON direction-selective cells receive spatially offset and asymmetrically filtered glutamatergic inputs along motion-preference axis from asymmetrically wired bipolar and amacrine cell types with distinct release dynamics. A computational model shows that, with this spatiotemporal structure, the input amplitude becomes sensitive to speed and direction by a preferred direction enhancement mechanism. Our results highlight the role of an excitatory mechanism in retinal motion computation by which feature selectivity emerges from non-selective inputs.
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Molina-Obando S, Vargas-Fique JF, Henning M, Gür B, Schladt TM, Akhtar J, Berger TK, Silies M. ON selectivity in the Drosophila visual system is a multisynaptic process involving both glutamatergic and GABAergic inhibition. eLife 2019; 8:e49373. [PMID: 31535971 PMCID: PMC6845231 DOI: 10.7554/elife.49373] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Accepted: 09/18/2019] [Indexed: 01/06/2023] Open
Abstract
Sensory systems sequentially extract increasingly complex features. ON and OFF pathways, for example, encode increases or decreases of a stimulus from a common input. This ON/OFF pathway split is thought to occur at individual synaptic connections through a sign-inverting synapse in one of the pathways. Here, we show that ON selectivity is a multisynaptic process in the Drosophila visual system. A pharmacogenetics approach demonstrates that both glutamatergic inhibition through GluClα and GABAergic inhibition through Rdl mediate ON responses. Although neurons postsynaptic to the glutamatergic ON pathway input L1 lose all responses in GluClα mutants, they are resistant to a cell-type-specific loss of GluClα. This shows that ON selectivity is distributed across multiple synapses, and raises the possibility that cell-type-specific manipulations might reveal similar strategies in other sensory systems. Thus, sensory coding is more distributed than predicted by simple circuit motifs, allowing for robust neural processing.
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Affiliation(s)
- Sebastian Molina-Obando
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
- European Neuroscience Institute Göttingen – A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of GöttingenGöttingenGermany
| | - Juan Felipe Vargas-Fique
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
- European Neuroscience Institute Göttingen – A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of GöttingenGöttingenGermany
| | - Miriam Henning
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
- European Neuroscience Institute Göttingen – A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
| | - Burak Gür
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
- European Neuroscience Institute Göttingen – A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of GöttingenGöttingenGermany
| | - T Moritz Schladt
- Department of Molecular Sensory SystemsCenter of Advanced European Studies and Research (caesar)BonnGermany
| | - Junaid Akhtar
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
| | - Thomas K Berger
- Department of Molecular Sensory SystemsCenter of Advanced European Studies and Research (caesar)BonnGermany
- Institute of Physiology and PathophysiologyPhilipps-Universität MarburgMarburgGermany
| | - Marion Silies
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
- European Neuroscience Institute Göttingen – A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
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Dynamic nonlinearities enable direction opponency in Drosophila elementary motion detectors. Nat Neurosci 2019; 22:1318-1326. [PMID: 31346296 PMCID: PMC6748873 DOI: 10.1038/s41593-019-0443-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 06/03/2019] [Indexed: 12/13/2022]
Abstract
Direction-selective neurons respond to visual motion in a preferred direction. They are direction-opponent if they are also inhibited by motion in the opposite direction. In flies and vertebrates, direction opponency has been observed in second-order direction-selective neurons, which achieve this opponency by subtracting signals from first-order direction-selective cells with opposite directional tunings. Here, we report direction opponency in Drosophila that emerges in first-order direction-selective neurons, the elementary motion detectors T4 and T5. This opponency persists when synaptic output from these cells is blocked, suggesting that it arises from feedforward, not feedback, computations. These observations exclude a broad class of linear-nonlinear models that have been proposed to describe direction-selective computations. However, they are consistent with models that include dynamic nonlinearities. Simulations of opponent models suggest that direction opponency in first-order motion detectors improves motion discriminability by suppressing noise generated by the local structure of natural scenes.
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36
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Creamer MS, Mano O, Tanaka R, Clark DA. A flexible geometry for panoramic visual and optogenetic stimulation during behavior and physiology. J Neurosci Methods 2019; 323:48-55. [PMID: 31103713 DOI: 10.1016/j.jneumeth.2019.05.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 05/11/2019] [Accepted: 05/12/2019] [Indexed: 11/26/2022]
Abstract
BACKGROUND To study visual processing, it is necessary to precisely control visual stimuli while recording neural and behavioral responses. It can be important to present stimuli over a broad area of the visual field, which can be technically difficult. NEW METHOD We present a simple geometry that can be used to display panoramic stimuli. A single digital light projector generates images that are reflected by mirrors onto flat screens that surround an animal. It can be used for behavioral and neurophysiological measurements, so virtually identical stimuli can be presented. Moreover, this geometry permits light from the projector to be used to activate optogenetic tools. RESULTS Using this geometry, we presented panoramic visual stimulation to Drosophila in three paradigms. We presented drifting contrast gratings while recording walking and turning speed. We used the same projector to activate optogenetic channels during visual stimulation. Finally, we used two-photon microscopy to record responses in direction-selective cells to drifting gratings. COMPARISON WITH EXISTING METHOD(S) Existing methods have typically required custom hardware or curved screens, while this method requires only flat back projection screens and a digital light projector. The projector generates images in real time and does not require pre-generated images. Finally, while many setups are large, this geometry occupies a 30 × 20 cm footprint with a 25 cm height. CONCLUSIONS This flexible geometry enables measurements of behavioral and neural responses to panoramic stimuli. This allows moderate throughput behavioral experiments with simultaneous optogenetic manipulation, with easy comparisons between behavior and neural activity using virtually identical stimuli.
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Affiliation(s)
- Matthew S Creamer
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, United States
| | - Omer Mano
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, United States
| | - Ryosuke Tanaka
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, United States
| | - Damon A Clark
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, United States; Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, United States; Department of Physics, Yale University, New Haven, CT, United States; Department of Neuroscience, Yale University, New Haven, CT, United States.
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37
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Extreme Compartmentalization in a Drosophila Amacrine Cell. Curr Biol 2019; 29:1545-1550.e2. [PMID: 31031119 DOI: 10.1016/j.cub.2019.03.070] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 03/08/2019] [Accepted: 03/28/2019] [Indexed: 11/22/2022]
Abstract
A neuron is conventionally regarded as a single processing unit. It receives input from one or several presynaptic cells, transforms these signals, and transmits one output signal to its postsynaptic partners. Exceptions exist: amacrine cells in the mammalian retina [1-3] or interneurons in the locust mesothoracic ganglion [4] are thought to represent many electrically isolated microcircuits within one neuron. An extreme case of such an amacrine cell has recently been described in the Drosophila visual system. This cell, called CT1, reaches into two neuropils of the optic lobe, where it visits each of 700 repetitive columns, thereby covering the whole visual field [5, 6]. Due to its unusual morphology, CT1 has been suspected to perform local computations [6, 7], but this has never been proven. Using 2-photon calcium imaging and visual stimulation, we find highly compartmentalized retinotopic response properties in neighboring terminals of CT1, with each terminal acting as an independent functional unit. Model simulations demonstrate that this extreme case of compartmentalization is at the biophysical limit of neural computation.
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38
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Shinomiya K, Huang G, Lu Z, Parag T, Xu CS, Aniceto R, Ansari N, Cheatham N, Lauchie S, Neace E, Ogundeyi O, Ordish C, Peel D, Shinomiya A, Smith C, Takemura S, Talebi I, Rivlin PK, Nern A, Scheffer LK, Plaza SM, Meinertzhagen IA. Comparisons between the ON- and OFF-edge motion pathways in the Drosophila brain. eLife 2019; 8:40025. [PMID: 30624205 PMCID: PMC6338461 DOI: 10.7554/elife.40025] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 01/02/2019] [Indexed: 02/03/2023] Open
Abstract
Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In Drosophila melanogaster, recently discovered synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest a motion model that is increasingly intricate when compared with the ubiquitous Hassenstein-Reichardt model. By contrast, our knowledge of OFF-pathway (T5) has been incomplete. Here, we present a conclusive and comprehensive connectome that, for the first time, integrates detailed connectivity information for inputs to both the T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. Although the two pathways are probably evolutionarily linked and exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.
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Affiliation(s)
- Kazunori Shinomiya
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Gary Huang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Zhiyuan Lu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.,Department of Psychology and Neuroscience, Dalhousie University, Halifax, Canada
| | - Toufiq Parag
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.,School of Engineering and Applied Sciences, Harvard University, Cambridge, United States
| | - C Shan Xu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Roxanne Aniceto
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Namra Ansari
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Natasha Cheatham
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Shirley Lauchie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Erika Neace
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Omotara Ogundeyi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Christopher Ordish
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - David Peel
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Aya Shinomiya
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Claire Smith
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Satoko Takemura
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Iris Talebi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Patricia K Rivlin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Louis K Scheffer
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Stephen M Plaza
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Ian A Meinertzhagen
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, Canada
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Fu Q, Wang H, Hu C, Yue S. Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review. ARTIFICIAL LIFE 2019; 25:263-311. [PMID: 31397604 DOI: 10.1162/artl_a_00297] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.
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Affiliation(s)
- Qinbing Fu
- Guangzhou University, School of Mechanical and Electrical Engineering; Machine Life and Intelligence Research Centre
- University of Lincoln, Computational Intelligence Lab, School of Computer Science; Lincoln Centre for Autonomous Systems.
| | - Hongxin Wang
- University of Lincoln, Computational Intelligence Lab, School of Computer Science; Lincoln Centre for Autonomous Systems.
| | - Cheng Hu
- Guangzhou University, School of Mechanical and Electrical Engineering; Machine Life and Intelligence Research Centre
- University of Lincoln, Computational Intelligence Lab, School of Computer Science; Lincoln Centre for Autonomous Systems.
| | - Shigang Yue
- Guangzhou University, School of Mechanical and Electrical Engineering; Machine Life and Intelligence Research Centre
- University of Lincoln, Computational Intelligence Lab, School of Computer Science; Lincoln Centre for Autonomous Systems.
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40
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Creamer MS, Mano O, Clark DA. Visual Control of Walking Speed in Drosophila. Neuron 2018; 100:1460-1473.e6. [PMID: 30415994 PMCID: PMC6405217 DOI: 10.1016/j.neuron.2018.10.028] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 08/29/2018] [Accepted: 10/16/2018] [Indexed: 10/27/2022]
Abstract
An animal's self-motion generates optic flow across its retina, and it can use this visual signal to regulate its orientation and speed through the world. While orientation control has been studied extensively in Drosophila and other insects, much less is known about the visual cues and circuits that regulate translational speed. Here, we show that flies regulate walking speed with an algorithm that is tuned to the speed of visual motion, causing them to slow when visual objects are nearby. This regulation does not depend strongly on the spatial structure or the direction of visual stimuli, making it algorithmically distinct from the classic computation that controls orientation. Despite the different algorithms, the visual circuits that regulate walking speed overlap with those that regulate orientation. Taken together, our findings suggest that walking speed is controlled by a hierarchical computation that combines multiple motion detectors with distinct tunings. VIDEO ABSTRACT.
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Affiliation(s)
- Matthew S Creamer
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
| | - Omer Mano
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06511, USA
| | - Damon A Clark
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA; Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06511, USA; Department of Physics, Yale University, New Haven, CT 06511, USA.
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41
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Cyr A, Thériault F, Ross M, Berberian N, Chartier S. Spiking Neurons Integrating Visual Stimuli Orientation and Direction Selectivity in a Robotic Context. Front Neurorobot 2018; 12:75. [PMID: 30524261 PMCID: PMC6256284 DOI: 10.3389/fnbot.2018.00075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Accepted: 10/31/2018] [Indexed: 11/13/2022] Open
Abstract
Visual motion detection is essential for the survival of many species. The phenomenon includes several spatial properties, not fully understood at the level of a neural circuit. This paper proposes a computational model of a visual motion detector that integrates direction and orientation selectivity features. A recent experiment in the Drosophila model highlights that stimulus orientation influences the neural response of direction cells. However, this interaction and the significance at the behavioral level are currently unknown. As such, another objective of this article is to study the effect of merging these two visual processes when contextualized in a neuro-robotic model and an operant conditioning procedure. In this work, the learning task was solved using an artificial spiking neural network, acting as the brain controller for virtual and physical robots, showing a behavior modulation from the integration of both visual processes.
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Affiliation(s)
- André Cyr
- Conec Laboratory, School of Psychology, Ottawa University, Ottawa, ON, Canada
| | - Frédéric Thériault
- Department of Computer Science, Cégep du Vieux Montréal, Montreal, QC, Canada
| | - Matthew Ross
- Conec Laboratory, School of Psychology, Ottawa University, Ottawa, ON, Canada
| | - Nareg Berberian
- Conec Laboratory, School of Psychology, Ottawa University, Ottawa, ON, Canada
| | - Sylvain Chartier
- Conec Laboratory, School of Psychology, Ottawa University, Ottawa, ON, Canada
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42
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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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43
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Barnhart EL, Wang IE, Wei H, Desplan C, Clandinin TR. Sequential Nonlinear Filtering of Local Motion Cues by Global Motion Circuits. Neuron 2018; 100:229-243.e3. [PMID: 30220510 DOI: 10.1016/j.neuron.2018.08.022] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 07/20/2018] [Accepted: 08/17/2018] [Indexed: 11/16/2022]
Abstract
Many animals guide their movements using optic flow, the displacement of stationary objects across the retina caused by self-motion. How do animals selectively synthesize a global motion pattern from its local motion components? To what extent does this feature selectivity rely on circuit mechanisms versus dendritic processing? Here we used in vivo calcium imaging to identify pre- and postsynaptic mechanisms for processing local motion signals in global motion detection circuits in Drosophila. Lobula plate tangential cells (LPTCs) detect global motion by pooling input from local motion detectors, T4/T5 neurons. We show that T4/T5 neurons suppress responses to adjacent local motion signals whereas LPTC dendrites selectively amplify spatiotemporal sequences of local motion signals consistent with preferred global patterns. We propose that sequential nonlinear suppression and amplification operations allow optic flow circuitry to simultaneously prevent saturating responses to local signals while creating selectivity for global motion patterns critical to behavior.
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Affiliation(s)
- Erin L Barnhart
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA; Department of Biology, New York University, New York, NY 10003, USA
| | - Irving E Wang
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Huayi Wei
- Department of Biology, New York University, New York, NY 10003, USA
| | - Claude Desplan
- Department of Biology, New York University, New York, NY 10003, USA.
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA.
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Wienecke CFR, Leong JCS, Clandinin TR. Linear Summation Underlies Direction Selectivity in Drosophila. Neuron 2018; 99:680-688.e4. [PMID: 30057202 DOI: 10.1016/j.neuron.2018.07.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 05/24/2018] [Accepted: 07/02/2018] [Indexed: 11/28/2022]
Abstract
While linear mechanisms lay the foundations of feature selectivity in many brain areas, direction selectivity in the elementary motion detector (EMD) of the fly has become a paradigm of nonlinear neuronal computation. We have bridged this divide by demonstrating that linear spatial summation can generate direction selectivity in the fruit fly Drosophila. Using linear systems analysis and two-photon imaging of a genetically encoded voltage indicator, we measure the emergence of direction-selective (DS) voltage signals in the Drosophila OFF pathway. Our study is a direct, quantitative investigation of the algorithm underlying directional signals, with the striking finding that linear spatial summation is sufficient for the emergence of direction selectivity. A linear stage of the fly EMD strongly resembles similar computations in vertebrate visual cortex, demands a reappraisal of the role of upstream nonlinearities, and implicates the voltage-to-calcium transformation in the refinement of feature selectivity in this system. VIDEO ABSTRACT.
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Affiliation(s)
- Carl F R Wienecke
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Jonathan C S Leong
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA; Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA.
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45
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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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Apitz H, Salecker I. Spatio-temporal relays control layer identity of direction-selective neuron subtypes in Drosophila. Nat Commun 2018; 9:2295. [PMID: 29895891 PMCID: PMC5997761 DOI: 10.1038/s41467-018-04592-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 05/04/2018] [Indexed: 11/09/2022] Open
Abstract
Visual motion detection in sighted animals is essential to guide behavioral actions ensuring their survival. In Drosophila, motion direction is first detected by T4/T5 neurons. Their axons innervate one of the four lobula plate layers. How T4/T5 neurons with layer-specific representation of motion-direction preferences are specified during development is unknown. We show that diffusible Wingless (Wg) between adjacent neuroepithelia induces its own expression to form secondary signaling centers. These activate Decapentaplegic (Dpp) signaling in adjacent lateral tertiary neuroepithelial domains dedicated to producing layer 3/4-specific T4/T5 neurons. T4/T5 neurons derived from the core domain devoid of Dpp signaling adopt the default layer 1/2 fate. Dpp signaling induces the expression of the T-box transcription factor Optomotor-blind (Omb), serving as a relay to postmitotic neurons. Omb-mediated repression of Dachshund transforms layer 1/2- into layer 3/4-specific neurons. Hence, spatio-temporal relay mechanisms, bridging the distances between neuroepithelial domains and their postmitotic progeny, implement T4/T5 neuron-subtype identity.
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Affiliation(s)
- Holger Apitz
- The Francis Crick Institute, Visual Circuit Assembly Laboratory, 1 Midland Road, London, NW1 1AT, UK
| | - Iris Salecker
- The Francis Crick Institute, Visual Circuit Assembly Laboratory, 1 Midland Road, London, NW1 1AT, UK.
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47
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Borst A. A biophysical mechanism for preferred direction enhancement in fly motion vision. PLoS Comput Biol 2018; 14:e1006240. [PMID: 29897917 PMCID: PMC6016951 DOI: 10.1371/journal.pcbi.1006240] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 06/25/2018] [Accepted: 05/29/2018] [Indexed: 11/18/2022] Open
Abstract
Seeing the direction of motion is essential for survival of all sighted animals. Consequently, nerve cells that respond to visual stimuli moving in one but not in the opposite direction, so-called 'direction-selective' neurons, are found abundantly. In general, direction selectivity can arise by either signal amplification for stimuli moving in the cell's preferred direction ('preferred direction enhancement'), signal suppression for stimuli moving along the opposite direction ('null direction suppression'), or a combination of both. While signal suppression can be readily implemented in biophysical terms by a hyperpolarization followed by a rectification corresponding to the nonlinear voltage-dependence of the Calcium channel, the biophysical mechanism for signal amplification has remained unclear so far. Taking inspiration from the fly, I analyze a neural circuit where a direction-selective ON-cell receives inhibitory input from an OFF cell on the preferred side of the dendrite, while excitatory ON-cells contact the dendrite centrally. This way, an ON edge moving along the cell's preferred direction suppresses the inhibitory input, leading to a release from inhibition in the postsynaptic cell. The benefit of such a two-fold signal inversion lies in the resulting increase of the postsynaptic cell's input resistance, amplifying its response to a subsequent excitatory input signal even with a passive dendrite, i.e. without voltage-gated ion channels. A motion detector implementing this mechanism together with null direction suppression shows a high degree of direction selectivity over a large range of temporal frequency, narrow directional tuning, and a large signal-to-noise ratio.
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48
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Development of Concurrent Retinotopic Maps in the Fly Motion Detection Circuit. Cell 2018; 173:485-498.e11. [PMID: 29576455 DOI: 10.1016/j.cell.2018.02.053] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 12/24/2017] [Accepted: 02/21/2018] [Indexed: 11/22/2022]
Abstract
Understanding how complex brain wiring is produced during development is a daunting challenge. In Drosophila, information from 800 retinal ommatidia is processed in distinct brain neuropiles, each subdivided into 800 matching retinotopic columns. The lobula plate comprises four T4 and four T5 neuronal subtypes. T4 neurons respond to bright edge motion, whereas T5 neurons respond to dark edge motion. Each is tuned to motion in one of the four cardinal directions, effectively establishing eight concurrent retinotopic maps to support wide-field motion. We discovered a mode of neurogenesis where two sequential Notch-dependent divisions of either a horizontal or a vertical progenitor produce matching sets of two T4 and two T5 neurons retinotopically coincident with pairwise opposite direction selectivity. We show that retinotopy is an emergent characteristic of this neurogenic program and derives directly from neuronal birth order. Our work illustrates how simple developmental rules can implement complex neural organization.
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Gruntman E, Romani S, Reiser MB. Simple integration of fast excitation and offset, delayed inhibition computes directional selectivity in Drosophila. Nat Neurosci 2018; 21:250-257. [PMID: 29311742 PMCID: PMC5967973 DOI: 10.1038/s41593-017-0046-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 11/06/2017] [Indexed: 02/07/2023]
Abstract
A neuron that extracts directionally selective motion information from upstream signals lacking this selectivity must compare visual responses from spatially offset inputs. Distinguishing among prevailing algorithmic models for this computation requires measuring fast neuronal activity and inhibition. In the Drosophila melanogaster visual system, a fourth-order neuron-T4-is the first cell type in the ON pathway to exhibit directionally selective signals. Here we use in vivo whole-cell recordings of T4 to show that directional selectivity originates from simple integration of spatially offset fast excitatory and slow inhibitory inputs, resulting in a suppression of responses to the nonpreferred motion direction. We constructed a passive, conductance-based model of a T4 cell that accurately predicts the neuron's response to moving stimuli. These results connect the known circuit anatomy of the motion pathway to the algorithmic mechanism by which the direction of motion is computed.
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Affiliation(s)
- Eyal Gruntman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Michael B Reiser
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
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Neural mechanisms underlying sensitivity to reverse-phi motion in the fly. PLoS One 2017; 12:e0189019. [PMID: 29261684 PMCID: PMC5737883 DOI: 10.1371/journal.pone.0189019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 11/18/2017] [Indexed: 01/18/2023] Open
Abstract
Optical illusions provide powerful tools for mapping the algorithms and circuits that underlie visual processing, revealing structure through atypical function. Of particular note in the study of motion detection has been the reverse-phi illusion. When contrast reversals accompany discrete movement, detected direction tends to invert. This occurs across a wide range of organisms, spanning humans and invertebrates. Here, we map an algorithmic account of the phenomenon onto neural circuitry in the fruit fly Drosophila melanogaster. Through targeted silencing experiments in tethered walking flies as well as electrophysiology and calcium imaging, we demonstrate that ON- or OFF-selective local motion detector cells T4 and T5 are sensitive to certain interactions between ON and OFF. A biologically plausible detector model accounts for subtle features of this particular form of illusory motion reversal, like the re-inversion of turning responses occurring at extreme stimulus velocities. In light of comparable circuit architecture in the mammalian retina, we suggest that similar mechanisms may apply even to human psychophysics.
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