1
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Song H, Lee MG, Kim G, Kim DH, Kim G, Park W, Rhee H, In JH, Kim KM. Fully Memristive Elementary Motion Detectors for a Maneuver Prediction. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2309708. [PMID: 38251443 DOI: 10.1002/adma.202309708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/09/2024] [Indexed: 01/23/2024]
Abstract
Insects can efficiently perform object motion detection via a specialized neural circuit, called an elementary motion detector (EMD). In contrast, conventional machine vision systems require significant computational resources for dynamic motion processing. Here, a fully memristive EMD (M-EMD) is presented that implements the Hassenstein-Reichardt (HR) correlator, a biological model of the EMD. The M-EMD consists of a simple Wye (Y) configuration, including a static resistor, a dynamic memristor, and a Mott memristor. The resistor and dynamic memristor introduce different signal delays, enabling spatio-temporal signal integration in the subsequent Mott memristor, resulting in a direction-selective response. In addition, a neuromorphic system is developed employing the M-EMDs to predict a lane-changing maneuver by vehicles on the road. The system achieved a high accuracy (> 87%) in predicting future lane-changing maneuvers on the Next Generation Simulation (NGSIM) dataset while reducing the computational cost by 92.9% compared to the conventional neuromorphic system without the M-EMD, suggesting its strong potential for edge-level computing.
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Affiliation(s)
- Hanchan Song
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Min Gu Lee
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Gwangmin Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Do Hoon Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Geunyoung Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Woojoon Park
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Hakseung Rhee
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jae Hyun In
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Kyung Min Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
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2
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Pang MM, Chen F, Xie M, Druckmann S, Clandinin TR, Yang HH. A recurrent neural circuit in Drosophila deblurs visual inputs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.19.590352. [PMID: 38712245 PMCID: PMC11071408 DOI: 10.1101/2024.04.19.590352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
A critical goal of vision is to detect changes in light intensity, even when these changes are blurred by the spatial resolution of the eye and the motion of the animal. Here we describe a recurrent neural circuit in Drosophila that compensates for blur and thereby selectively enhances the perceived contrast of moving edges. Using in vivo , two-photon voltage imaging, we measured the temporal response properties of L1 and L2, two cell types that receive direct synaptic input from photoreceptors. These neurons have biphasic responses to brief flashes of light, a hallmark of cells that encode changes in stimulus intensity. However, the second phase was often much larger than the first, creating an unusual temporal filter. Genetic dissection revealed that recurrent neural circuitry strongly shapes the second phase of the response, informing the structure of a dynamical model. By applying this model to moving natural images, we demonstrate that rather than veridically representing stimulus changes, this temporal processing strategy systematically enhances them, amplifying and sharpening responses. Comparing the measured responses of L2 to model predictions across both artificial and natural stimuli revealed that L2 tunes its properties as the model predicts in order to deblur images. Since this strategy is tunable to behavioral context, generalizable to any time-varying sensory input, and implementable with a common circuit motif, we propose that it could be broadly used to selectively enhance sharp and salient changes.
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3
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Prech S, Groschner LN, Borst A. An open platform for visual stimulation of insects. PLoS One 2024; 19:e0301999. [PMID: 38635686 PMCID: PMC11025907 DOI: 10.1371/journal.pone.0301999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/26/2024] [Indexed: 04/20/2024] Open
Abstract
To study how the nervous system processes visual information, experimenters must record neural activity while delivering visual stimuli in a controlled fashion. In animals with a nearly panoramic field of view, such as flies, precise stimulation of the entire visual field is challenging. We describe a projector-based device for stimulation of the insect visual system under a microscope. The device is based on a bowl-shaped screen that provides a wide and nearly distortion-free field of view. It is compact, cheap, easy to assemble, and easy to operate using the included open-source software for stimulus generation. We validate the virtual reality system technically and demonstrate its capabilities in a series of experiments at two levels: the cellular, by measuring the membrane potential responses of visual interneurons; and the organismal, by recording optomotor and fixation behavior of Drosophila melanogaster in tethered flight. Our experiments reveal the importance of stimulating the visual system of an insect with a wide field of view, and we provide a simple solution to do so.
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Affiliation(s)
- Stefan Prech
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Lukas N. Groschner
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Gottfried Schatz Research Center, Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria
| | - Alexander Borst
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
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4
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Jernigan CM, Freiwald WA, Sheehan MJ. Neural correlates of individual facial recognition in a social wasp. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.11.589095. [PMID: 38659842 PMCID: PMC11042187 DOI: 10.1101/2024.04.11.589095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Individual recognition is critical for social behavior across species. Whether recognition is mediated by circuits specialized for social information processing has been a matter of debate. Here we examine the neurobiological underpinning of individual visual facial recognition in Polistes fuscatus paper wasps. Front-facing images of conspecific wasps broadly increase activity across many brain regions relative to other stimuli. Notably, we identify a localized subpopulation of neurons in the protocerebrum which show specialized selectivity for front-facing wasp images, which we term wasp cells. These wasp cells encode information regarding the facial patterns, with ensemble activity correlating with facial identity. Wasp cells are strikingly analogous to face cells in primates, indicating that specialized circuits are likely an adaptive feature of neural architecture to support visual recognition.
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Affiliation(s)
- Christopher M. Jernigan
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University; Ithaca, NY, 14853, USA
| | - Winrich A. Freiwald
- Laboratory of Neural Systems, The Rockefeller University, New York, NY 10065, USA
| | - Michael J. Sheehan
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University; Ithaca, NY, 14853, USA
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5
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Lin A, Yang R, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Bates AS, Eckstein N, Funke J, Jefferis GSXE, Murthy M. Network Statistics of the Whole-Brain Connectome of Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.29.551086. [PMID: 37547019 PMCID: PMC10402125 DOI: 10.1101/2023.07.29.551086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.
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Affiliation(s)
- Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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6
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Schoepe T, Janotte E, Milde MB, Bertrand OJN, Egelhaaf M, Chicca E. Finding the gap: neuromorphic motion-vision in dense environments. Nat Commun 2024; 15:817. [PMID: 38280859 PMCID: PMC10821932 DOI: 10.1038/s41467-024-45063-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 01/15/2024] [Indexed: 01/29/2024] Open
Abstract
Animals have evolved mechanisms to travel safely and efficiently within different habitats. On a journey in dense terrains animals avoid collisions and cross narrow passages while controlling an overall course. Multiple hypotheses target how animals solve challenges faced during such travel. Here we show that a single mechanism enables safe and efficient travel. We developed a robot inspired by insects. It has remarkable capabilities to travel in dense terrain, avoiding collisions, crossing gaps and selecting safe passages. These capabilities are accomplished by a neuromorphic network steering the robot toward regions of low apparent motion. Our system leverages knowledge about vision processing and obstacle avoidance in insects. Our results demonstrate how insects might safely travel through diverse habitats. We anticipate our system to be a working hypothesis to study insects' travels in dense terrains. Furthermore, it illustrates that we can design novel hardware systems by understanding the underlying mechanisms driving behaviour.
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Affiliation(s)
- Thorben Schoepe
- Peter Grünberg Institut 15, Forschungszentrum Jülich, Aachen, Germany.
- Faculty of Technology and Cognitive Interaction Technology Center of Excellence (CITEC), Bielefeld University, Bielefeld, Germany.
- Bio-Inspired Circuits and Systems (BICS) Lab. Zernike Institute for Advanced Materials (Zernike Inst Adv Mat), University of Groningen, Groningen, Netherlands.
- CogniGron (Groningen Cognitive Systems and Materials Center), University of Groningen, Groningen, Netherlands.
| | - Ella Janotte
- Event Driven Perception for Robotics, Italian Institute of Technology, iCub facility, Genoa, Italy
| | - Moritz B Milde
- International Centre for Neuromorphic Systems, MARCS Institute, Western Sydney University, Penrith, Australia
| | | | - Martin Egelhaaf
- Neurobiology, Faculty of Biology, Bielefeld University, Bielefeld, Germany
| | - Elisabetta Chicca
- Faculty of Technology and Cognitive Interaction Technology Center of Excellence (CITEC), Bielefeld University, Bielefeld, Germany
- Bio-Inspired Circuits and Systems (BICS) Lab. Zernike Institute for Advanced Materials (Zernike Inst Adv Mat), University of Groningen, Groningen, Netherlands
- CogniGron (Groningen Cognitive Systems and Materials Center), University of Groningen, Groningen, Netherlands
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7
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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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8
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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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9
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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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10
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Zhu L, Mangan M, Webb B. Neuromorphic sequence learning with an event camera on routes through vegetation. Sci Robot 2023; 8:eadg3679. [PMID: 37756384 DOI: 10.1126/scirobotics.adg3679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 08/29/2023] [Indexed: 09/29/2023]
Abstract
For many robotics applications, it is desirable to have relatively low-power and efficient onboard solutions. We took inspiration from insects, such as ants, that are capable of learning and following routes in complex natural environments using relatively constrained sensory and neural systems. Such capabilities are particularly relevant to applications such as agricultural robotics, where visual navigation through dense vegetation remains a challenging task. In this scenario, a route is likely to have high self-similarity and be subject to changing lighting conditions and motion over uneven terrain, and the effects of wind on leaves increase the variability of the input. We used a bioinspired event camera on a terrestrial robot to collect visual sequences along routes in natural outdoor environments and applied a neural algorithm for spatiotemporal memory that is closely based on a known neural circuit in the insect brain. We show that this method is plausible to support route recognition for visual navigation and more robust than SeqSLAM when evaluated on repeated runs on the same route or routes with small lateral offsets. By encoding memory in a spiking neural network running on a neuromorphic computer, our model can evaluate visual familiarity in real time from event camera footage.
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Affiliation(s)
- Le Zhu
- School of Informatics, University of Edinburgh, EH8 9AB Edinburgh, UK
| | - Michael Mangan
- Sheffield Robotics, Department of Computer Science, University of Sheffield, S1 4DP Sheffield, UK
| | - Barbara Webb
- School of Informatics, University of Edinburgh, EH8 9AB Edinburgh, UK
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11
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Fu Q. Motion perception based on ON/OFF channels: A survey. Neural Netw 2023; 165:1-18. [PMID: 37263088 DOI: 10.1016/j.neunet.2023.05.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 04/02/2023] [Accepted: 05/17/2023] [Indexed: 06/03/2023]
Abstract
Motion perception is an essential ability for animals and artificially intelligent systems interacting effectively, safely with surrounding objects and environments. Biological visual systems, that have naturally evolved over hundreds-million years, are quite efficient and robust for motion perception, whereas artificial vision systems are far from such capability. This paper argues that the gap can be significantly reduced by formulation of ON/OFF channels in motion perception models encoding luminance increment (ON) and decrement (OFF) responses within receptive field, separately. Such signal-bifurcating structure has been found in neural systems of many animal species articulating early motion is split and processed in segregated pathways. However, the corresponding biological substrates, and the necessity for artificial vision systems have never been elucidated together, leaving concerns on uniqueness and advantages of ON/OFF channels upon building dynamic vision systems to address real world challenges. This paper highlights the importance of ON/OFF channels in motion perception through surveying current progress covering both neuroscience and computationally modelling works with applications. Compared to related literature, this paper for the first time provides insights into implementation of different selectivity to directional motion of looming, translating, and small-sized target movement based on ON/OFF channels in keeping with soundness and robustness of biological principles. Existing challenges and future trends of such bio-plausible computational structure for visual perception in connection with hotspots of machine learning, advanced vision sensors like event-driven camera finally are discussed.
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Affiliation(s)
- Qinbing Fu
- Machine Life and Intelligence Research Centre, School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China.
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12
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Tanaka R, Zhou B, Agrochao M, Badwan BA, Au B, Matos NCB, Clark DA. Neural mechanisms to incorporate visual counterevidence in self motion estimation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.04.522814. [PMID: 36711843 PMCID: PMC9881891 DOI: 10.1101/2023.01.04.522814] [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: 01/08/2023]
Abstract
In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can confuse the movement of external objects with genuine self motion. Here, we show that stationary patterns on the retina, which constitute negative evidence against self rotation, are used by the fruit fly Drosophila to suppress inappropriate stabilizing rotational behavior. In silico experiments show that artificial neural networks optimized to distinguish self and world motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's motion- and optic flow-detectors. Our results exemplify how the compact brain of the fly incorporates negative evidence to improve heading stability, exploiting geometrical constraints of the visual world.
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Affiliation(s)
- Ryosuke Tanaka
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
- Present Address: Institute of Neuroscience, Technical University of Munich, Munich 80802, Germany
| | - Baohua Zhou
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06511, USA
| | - Margarida Agrochao
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA
| | - Bara A. Badwan
- School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA
| | - Braedyn Au
- Department of Physics, Yale University, New Haven, CT 06511, USA
| | - Natalia C. B. Matos
- 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
- Wu Tsai Institute, Yale University, New Haven, CT 06511, USA
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13
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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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14
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Pirogova N, Borst A. Contrast normalization affects response time-course of visual interneurons. PLoS One 2023; 18:e0285686. [PMID: 37294743 PMCID: PMC10256145 DOI: 10.1371/journal.pone.0285686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 04/28/2023] [Indexed: 06/11/2023] Open
Abstract
In natural environments, light intensities and visual contrasts vary widely, yet neurons have a limited response range for encoding them. Neurons accomplish that by flexibly adjusting their dynamic range to the statistics of the environment via contrast normalization. The effect of contrast normalization is usually measured as a reduction of neural signal amplitudes, but whether it influences response dynamics is unknown. Here, we show that contrast normalization in visual interneurons of Drosophila melanogaster not only suppresses the amplitude but also alters the dynamics of responses when a dynamic surround is present. We present a simple model that qualitatively reproduces the simultaneous effect of the visual surround on the response amplitude and temporal dynamics by altering the cells' input resistance and, thus, their membrane time constant. In conclusion, single-cell filtering properties as derived from artificial stimulus protocols like white-noise stimulation cannot be transferred one-to-one to predict responses under natural conditions.
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Affiliation(s)
- Nadezhda Pirogova
- Department Circuits-Computation-Models, Max Planck Institute for Biological Intelligence, Planegg, Martinsried, Germany
- Graduate School of Systemic Neurosciences, LMU Munich, Planegg, Martinsried, Germany
| | - Alexander Borst
- Department Circuits-Computation-Models, Max Planck Institute for Biological Intelligence, Planegg, Martinsried, Germany
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15
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Currier TA, Pang MM, Clandinin TR. Visual processing in the fly, from photoreceptors to behavior. Genetics 2023; 224:iyad064. [PMID: 37128740 PMCID: PMC10213501 DOI: 10.1093/genetics/iyad064] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/22/2023] [Indexed: 05/03/2023] Open
Abstract
Originally a genetic model organism, the experimental use of Drosophila melanogaster has grown to include quantitative behavioral analyses, sophisticated perturbations of neuronal function, and detailed sensory physiology. A highlight of these developments can be seen in the context of vision, where pioneering studies have uncovered fundamental and generalizable principles of sensory processing. Here we begin with an overview of vision-guided behaviors and common methods for probing visual circuits. We then outline the anatomy and physiology of brain regions involved in visual processing, beginning at the sensory periphery and ending with descending motor control. Areas of focus include contrast and motion detection in the optic lobe, circuits for visual feature selectivity, computations in support of spatial navigation, and contextual associative learning. Finally, we look to the future of fly visual neuroscience and discuss promising topics for further study.
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Affiliation(s)
- Timothy A Currier
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michelle M Pang
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
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16
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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: 0] [Impact Index Per Article: 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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17
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Zhao Y, Ke S, Cheng G, Lv X, Chang J, Zhou W. Direction Selectivity of TmY Neurites in Drosophila. Neurosci Bull 2023; 39:759-773. [PMID: 36399278 PMCID: PMC10169962 DOI: 10.1007/s12264-022-00966-y] [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: 04/29/2022] [Accepted: 06/29/2022] [Indexed: 11/19/2022] Open
Abstract
The perception of motion is an important function of vision. Neural wiring diagrams for extracting directional information have been obtained by connectome reconstruction. Direction selectivity in Drosophila is thought to originate in T4/T5 neurons through integrating inputs with different temporal filtering properties. Through genetic screening based on synaptic distribution, we isolated a new type of TmY neuron, termed TmY-ds, that form reciprocal synaptic connections with T4/T5 neurons. Its neurites responded to grating motion along the four cardinal directions and showed a variety of direction selectivity. Intriguingly, its direction selectivity originated from temporal filtering neurons rather than T4/T5. Genetic silencing and activation experiments showed that TmY-ds neurons are functionally upstream of T4/T5. Our results suggest that direction selectivity is generated in a tripartite circuit formed among these three neurons-temporal filtering, TmY-ds, and T4/T5 neurons, in which TmY-ds plays a role in the enhancement of direction selectivity in T4/T5 neurons.
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Affiliation(s)
- Yinyin Zhao
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Shanshan Ke
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Guo Cheng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xiaohua Lv
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jin Chang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Wei Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
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18
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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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19
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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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20
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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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21
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Kim H, Park H, Lee J, Kim AJ. A visuomotor circuit for evasive flight turns in Drosophila. Curr Biol 2023; 33:321-335.e6. [PMID: 36603587 DOI: 10.1016/j.cub.2022.12.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/14/2022] [Accepted: 12/07/2022] [Indexed: 01/06/2023]
Abstract
Visual systems extract multiple features from a scene using parallel neural circuits. Ultimately, the separate neural signals must come together to coherently influence action. Here, we characterize a circuit in Drosophila that integrates multiple visual features related to imminent threats to drive evasive locomotor turns. We identified, using genetic perturbation methods, a pair of visual projection neurons (LPLC2) and descending neurons (DNp06) that underlie evasive flight turns in response to laterally moving or approaching visual objects. Using two-photon calcium imaging or whole-cell patch clamping, we show that these cells indeed respond to both translating and approaching visual patterns. Furthermore, by measuring visual responses of LPLC2 neurons after genetically silencing presynaptic motion-sensing neurons, we show that their visual properties emerge by integrating multiple visual features across two early visual structures: the lobula and the lobula plate. This study highlights a clear example of how distinct visual signals converge on a single class of visual neurons and then activate premotor neurons to drive action, revealing a concise visuomotor pathway for evasive flight maneuvers in Drosophila.
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Affiliation(s)
- Hyosun Kim
- Department of Artificial Intelligence, Hanyang University, Seoul 04763, South Korea
| | - Hayun Park
- Department of Electronic Engineering, Hanyang University, Seoul 04763, South Korea
| | - Joowon Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, South Korea
| | - Anmo J Kim
- Department of Artificial Intelligence, Hanyang University, Seoul 04763, South Korea; Department of Electronic Engineering, Hanyang University, Seoul 04763, South Korea; Department of Biomedical Engineering, Hanyang University, Seoul 04763, South Korea.
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22
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Skelton PSM, Finn A, Brinkworth RSA. Contrast independent biologically inspired translational optic flow estimation. BIOLOGICAL CYBERNETICS 2022; 116:635-660. [PMID: 36303043 PMCID: PMC9691503 DOI: 10.1007/s00422-022-00948-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
The visual systems of insects are relatively simple compared to humans. However, they enable navigation through complex environments where insects perform exceptional levels of obstacle avoidance. Biology uses two separable modes of optic flow to achieve this: rapid gaze fixation (rotational motion known as saccades); and the inter-saccadic translational motion. While the fundamental process of insect optic flow has been known since the 1950's, so too has its dependence on contrast. The surrounding visual pathways used to overcome environmental dependencies are less well known. Previous work has shown promise for low-speed rotational motion estimation, but a gap remained in the estimation of translational motion, in particular the estimation of the time to impact. To consistently estimate the time to impact during inter-saccadic translatory motion, the fundamental limitation of contrast dependence must be overcome. By adapting an elaborated rotational velocity estimator from literature to work for translational motion, this paper proposes a novel algorithm for overcoming the contrast dependence of time to impact estimation using nonlinear spatio-temporal feedforward filtering. By applying bioinspired processes, approximately 15 points per decade of statistical discrimination were achieved when estimating the time to impact to a target across 360 background, distance, and velocity combinations: a 17-fold increase over the fundamental process. These results show the contrast dependence of time to impact estimation can be overcome in a biologically plausible manner. This, combined with previous results for low-speed rotational motion estimation, allows for contrast invariant computational models designed on the principles found in the biological visual system, paving the way for future visually guided systems.
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Affiliation(s)
- Phillip S. M. Skelton
- Centre for Defence Engineering Research and Training, College of Science and Engineering, Flinders University, 1284 South Road, Tonsley, South Australia 5042 Australia
| | - Anthony Finn
- Science, Technology, Engineering, and Mathematics, University of South Australia, 1 Mawson Lakes Boulevard, Mawson Lakes, South Australia 5095 Australia
| | - Russell S. A. Brinkworth
- Centre for Defence Engineering Research and Training, College of Science and Engineering, Flinders University, 1284 South Road, Tonsley, South Australia 5042 Australia
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23
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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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24
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Hayashi M, Kazawa T, Tsunoda H, Kanzaki R. The Understanding of ON-Edge Motion Detection Through the Simulation Based on the Connectome of Drosophila’s Optic Lobe. JOURNAL OF ROBOTICS AND MECHATRONICS 2022. [DOI: 10.20965/jrm.2022.p0795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The optic lobe of the fly is one of the prominent model systems for the neural mechanism of the motion detection. How a fly who lives under various visual situations of the nature processes the information from at most a few thousands of ommatidia in their neural circuit for the detection of moving objects is not exactly clear though many computational models of the fly optic lobe as a moving objects detector were suggested. Here we attempted to elucidate the mechanisms of ON-edge motion detection by a simulation approach based on the TEM connectome of Drosophila. Our simulation model of the optic lobe with the NEURON simulator that covers the full scale of ommatidia, reproduced the characteristics of the receptor neurons, lamina monopolar neurons, and T4 cells in the lobula. The contribution of each neuron can be estimated by changing synaptic connection strengths in the simulation and measuring the response to the motion stimulus. Those show the paradelle pathway provide motion detection in the fly optic lobe has more robustness and is more sophisticated than a simple combination of HR and BL systems.
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25
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Chen PJ, Li Y, Lee CH. Calcium Imaging of Neural Activity in Fly Photoreceptors. Cold Spring Harb Protoc 2022; 2022:Pdb.top107800. [PMID: 35641092 DOI: 10.1101/pdb.top107800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Functional imaging methodologies allow researchers to simultaneously monitor the neural activities of all single neurons in a population, and this ability has led to great advances in neuroscience research. Taking advantage of a genetically tractable model organism, functional imaging in Drosophila provides opportunities to probe scientific questions that were previously unanswerable by electrophysiological recordings. Here, we introduce comprehensive protocols for two-photon calcium imaging in fly visual neurons. We also discuss some challenges in applying optical imaging techniques to study visual systems and consider the best practices for making comparisons between different neuron groups.
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Affiliation(s)
- Pei-Ju Chen
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei 11529, Taiwan, Republic of China
| | - Yan Li
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei 11529, Taiwan, Republic of China
| | - Chi-Hon Lee
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei 11529, Taiwan, Republic of China
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26
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Ammer G, Vieira RM, Fendl S, Borst A. Anatomical distribution and functional roles of electrical synapses in Drosophila. Curr Biol 2022; 32:2022-2036.e4. [DOI: 10.1016/j.cub.2022.03.040] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 02/16/2022] [Accepted: 03/14/2022] [Indexed: 10/18/2022]
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27
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Groschner LN, Malis JG, Zuidinga B, Borst A. A biophysical account of multiplication by a single neuron. Nature 2022; 603:119-123. [PMID: 35197635 PMCID: PMC8891015 DOI: 10.1038/s41586-022-04428-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 01/14/2022] [Indexed: 12/19/2022]
Abstract
Nonlinear, multiplication-like operations carried out by individual nerve cells greatly enhance the computational power of a neural system1-3, but our understanding of their biophysical implementation is scant. Here we pursue this problem in the Drosophila melanogaster ON motion vision circuit4,5, in which we record the membrane potentials of direction-selective T4 neurons and of their columnar input elements6,7 in response to visual and pharmacological stimuli in vivo. Our electrophysiological measurements and conductance-based simulations provide evidence for a passive supralinear interaction between two distinct types of synapse on T4 dendrites. We show that this multiplication-like nonlinearity arises from the coincidence of cholinergic excitation and release from glutamatergic inhibition. The latter depends on the expression of the glutamate-gated chloride channel GluClα8,9 in T4 neurons, which sharpens the directional tuning of the cells and shapes the optomotor behaviour of the animals. Interacting pairs of shunting inhibitory and excitatory synapses have long been postulated as an analogue approximation of a multiplication, which is integral to theories of motion detection10,11, sound localization12 and sensorimotor control13.
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Affiliation(s)
| | | | - Birte Zuidinga
- Max Planck Institute of Neurobiology, Martinsried, Germany
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28
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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: 7] [Impact Index Per Article: 2.3] [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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29
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Nagel K. Motion vision: Pinning down motion computation in an ever-changing circuit. Curr Biol 2021; 31:R1523-R1525. [PMID: 34875241 DOI: 10.1016/j.cub.2021.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A new electrophysiological study of the Drosophila visual system, recording from columnar inputs to motion-detecting neurons, has provided new insights into the computations that underlie motion vision.
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Affiliation(s)
- Katherine Nagel
- Neuroscience Institute, NYU School of Medicine, 435 E. 30(th) Street, Room 1102, New York, NY 10016, USA.
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30
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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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31
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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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32
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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: 5] [Impact Index Per Article: 1.7] [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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33
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Li Y, Chen PJ, Lin TY, Ting CY, Muthuirulan P, Pursley R, Ilić M, Pirih P, Drews MS, Menon KP, Zinn KG, Pohida T, Borst A, Lee CH. Neural mechanism of spatio-chromatic opponency in the Drosophila amacrine neurons. Curr Biol 2021; 31:3040-3052.e9. [PMID: 34033749 DOI: 10.1016/j.cub.2021.04.068] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 12/18/2022]
Abstract
Visual animals detect spatial variations of light intensity and wavelength composition. Opponent coding is a common strategy for reducing information redundancy. Neurons equipped with both spatial and spectral opponency have been identified in vertebrates but not yet in insects. The Drosophila amacrine neuron Dm8 was recently reported to show color opponency. Here, we demonstrate Dm8 exhibits spatio-chromatic opponency. Antagonistic convergence of the direct input from the UV-sensing R7s and indirect input from the broadband receptors R1-R6 through Tm3 and Mi1 is sufficient to confer Dm8's UV/Vis (ultraviolet/visible light) opponency. Using high resolution monochromatic stimuli, we show the pale and yellow subtypes of Dm8s, inheriting retinal mosaic characteristics, have distinct spectral tuning properties. Using 2D white-noise stimulus and reverse correlation analysis, we found that the UV receptive field (RF) of Dm8 has a center-inhibition/surround-excitation structure. In the absence of UV-sensing R7 inputs, the polarity of the RF is inverted owing to the excitatory input from the broadband photoreceptors R1-R6. Using a new synGRASP method based on endogenous neurotransmitter receptors, we show that neighboring Dm8s form mutual inhibitory connections mediated by the glutamate-gated chloride channel GluClα, which is essential for both Dm8's spatial opponency and animals' phototactic behavior. Our study shows spatio-chromatic opponency could arise in the early visual stage, suggesting a common information processing strategy in both invertebrates and vertebrates.
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Affiliation(s)
- Yan Li
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan, Republic of China
| | - Pei-Ju Chen
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan, Republic of China
| | - Tzu-Yang Lin
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan, Republic of China
| | - Chun-Yuan Ting
- Section on Neuronal Connectivity, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| | - Pushpanathan Muthuirulan
- Section on Neuronal Connectivity, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| | - Randall Pursley
- Signal Processing and Instrumentation Section, Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892, USA
| | - Marko Ilić
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Primož Pirih
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Michael S Drews
- Department Circuits-Computation-Models, Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany
| | - Kaushiki P Menon
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Kai G Zinn
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Thomas Pohida
- Signal Processing and Instrumentation Section, Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892, USA
| | - Alexander Borst
- Department Circuits-Computation-Models, Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany
| | - Chi-Hon Lee
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan, Republic of China.
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34
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Pagni M, Haikala V, Oberhauser V, Meyer PB, Reiff DF, Schnaitmann C. Interaction of “chromatic” and “achromatic” circuits in Drosophila color opponent processing. Curr Biol 2021; 31:1687-1698.e4. [DOI: 10.1016/j.cub.2021.01.105] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 01/22/2021] [Accepted: 01/28/2021] [Indexed: 02/07/2023]
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35
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Fendl S, Vieira RM, Borst A. Conditional protein tagging methods reveal highly specific subcellular distribution of ion channels in motion-sensing neurons. eLife 2020; 9:62953. [PMID: 33079061 PMCID: PMC7655108 DOI: 10.7554/elife.62953] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 10/14/2020] [Indexed: 11/25/2022] Open
Abstract
Neurotransmitter receptors and ion channels shape the biophysical properties of neurons, from the sign of the response mediated by neurotransmitter receptors to the dynamics shaped by voltage-gated ion channels. Therefore, knowing the localizations and types of receptors and channels present in neurons is fundamental to our understanding of neural computation. Here, we developed two approaches to visualize the subcellular localization of specific proteins in Drosophila: The flippase-dependent expression of GFP-tagged receptor subunits in single neurons and ‘FlpTag’, a versatile new tool for the conditional labelling of endogenous proteins. Using these methods, we investigated the subcellular distribution of the receptors GluClα, Rdl, and Dα7 and the ion channels para and Ih in motion-sensing T4/T5 neurons of the Drosophila visual system. We discovered a strictly segregated subcellular distribution of these proteins and a sequential spatial arrangement of glutamate, acetylcholine, and GABA receptors along the dendrite that matched the previously reported EM-reconstructed synapse distributions.
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Affiliation(s)
- Sandra Fendl
- Max Planck Institute of Neurobiology, Martinsried, Germany.,Graduate School of Systemic Neurosciences, LMU Munich, Martinsried, Germany
| | | | - Alexander Borst
- Max Planck Institute of Neurobiology, Martinsried, Germany.,Graduate School of Systemic Neurosciences, LMU Munich, Martinsried, Germany
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36
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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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37
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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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38
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39
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Städele C, Keleş MF, Mongeau JM, Frye MA. Non-canonical Receptive Field Properties and Neuromodulation of Feature-Detecting Neurons in Flies. Curr Biol 2020; 30:2508-2519.e6. [PMID: 32442460 PMCID: PMC7343589 DOI: 10.1016/j.cub.2020.04.069] [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: 01/17/2020] [Revised: 03/10/2020] [Accepted: 04/24/2020] [Indexed: 10/24/2022]
Abstract
Several fundamental aspects of motion vision circuitry are prevalent across flies and mice. Both taxa segregate ON and OFF signals. For any given spatial pattern, motion detectors in both taxa are tuned to speed, selective for one of four cardinal directions, and modulated by catecholamine neurotransmitters. These similarities represent conserved, canonical properties of the functional circuits and computational algorithms for motion vision. Less is known about feature detectors, including how receptive field properties differ from the motion pathway or whether they are under neuromodulatory control to impart functional plasticity for the detection of salient objects from a moving background. Here, we investigated 19 types of putative feature selective lobula columnar (LC) neurons in the optic lobe of the fruit fly Drosophila melanogaster to characterize divergent properties of feature selection. We identified LC12 and LC15 as feature detectors. LC15 encodes moving bars, whereas LC12 is selective for the motion of discrete objects, mostly independent of size. Neither is selective for contrast polarity, speed, or direction, highlighting key differences in the underlying algorithms for feature detection and motion vision. We show that the onset of background motion suppresses object responses by LC12 and LC15. Surprisingly, the application of octopamine, which is released during flight, reverses the suppressive influence of background motion, rendering both LCs able to track moving objects superimposed against background motion. Our results provide a comparative framework for the function and modulation of feature detectors and new insights into the underlying neuronal mechanisms involved in visual feature detection.
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Affiliation(s)
- Carola Städele
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095-7239, USA
| | - Mehmet F Keleş
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095-7239, USA
| | - Jean-Michel Mongeau
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095-7239, USA
| | - Mark A Frye
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095-7239, USA.
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40
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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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41
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Schuetzenberger A, Borst A. Seeing Natural Images through the Eye of a Fly with Remote Focusing Two-Photon Microscopy. iScience 2020; 23:101170. [PMID: 32502966 PMCID: PMC7270611 DOI: 10.1016/j.isci.2020.101170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/02/2020] [Accepted: 05/12/2020] [Indexed: 11/30/2022] Open
Abstract
Visual systems of many animals, including the fruit fly Drosophila, represent the surrounding space as 2D maps, formed by populations of neurons. Advanced genetic tools make the fly visual system especially well accessible. However, in typical in vivo preparations for two-photon calcium imaging, relatively few neurons can be recorded at the same time. Here, we present an extension to a conventional two-photon microscope, based on remote focusing, which enables real-time rotation of the imaging plane, and thus flexible alignment to cellular structures, without resolution or speed trade-off. We simultaneously record from over 100 neighboring cells spanning the 2D retinotopic map. We characterize its representation of moving natural images, which we find is comparable to noise predictions. Our method increases throughput 10-fold and allows us to visualize a significant fraction of the fly's visual field. Furthermore, our system can be applied in general for a more flexible investigation of neural circuits.
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Affiliation(s)
- Anna Schuetzenberger
- Department Circuits - Computation - Models, Max-Planck-Institute of Neurobiology, 82152 Planegg, Germany; Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, 82152 Planegg, Germany.
| | - Alexander Borst
- Department Circuits - Computation - Models, Max-Planck-Institute of Neurobiology, 82152 Planegg, Germany; Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, 82152 Planegg, Germany.
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42
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Hörmann N, Schilling T, Ali AH, Serbe E, Mayer C, Borst A, Pujol-Martí J. A combinatorial code of transcription factors specifies subtypes of visual motion-sensing neurons in Drosophila. Development 2020; 147:223179. [PMID: 32238425 PMCID: PMC7240302 DOI: 10.1242/dev.186296] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 03/20/2020] [Indexed: 12/21/2022]
Abstract
Direction-selective T4/T5 neurons exist in four subtypes, each tuned to visual motion along one of the four cardinal directions. Along with their directional tuning, neurons of each T4/T5 subtype orient their dendrites and project their axons in a subtype-specific manner. Directional tuning, thus, appears strictly linked to morphology in T4/T5 neurons. How the four T4/T5 subtypes acquire their distinct morphologies during development remains largely unknown. Here, we investigated when and how the dendrites of the four T4/T5 subtypes acquire their specific orientations, and profiled the transcriptomes of all T4/T5 neurons during this process. This revealed a simple and stable combinatorial code of transcription factors defining the four T4/T5 subtypes during their development. Changing the combination of transcription factors of specific T4/T5 subtypes resulted in predictable and complete conversions of subtype-specific properties, i.e. dendrite orientation and matching axon projection pattern. Therefore, a combinatorial code of transcription factors coordinates the development of dendrite and axon morphologies to generate anatomical specializations that differentiate subtypes of T4/T5 motion-sensing neurons. Summary: Morphological and transcriptomic analyses allowed the identification of a combinatorial code of transcription factors that controls the development of subtype-specific morphologies in motion-detecting neurons of the Drosophila visual system.
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Affiliation(s)
- Nikolai Hörmann
- Department of Circuits - Computation - Models, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany
| | - Tabea Schilling
- Department of Circuits - Computation - Models, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany
| | - Aicha Haji Ali
- Department of Circuits - Computation - Models, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany
| | - Etienne Serbe
- Department of Circuits - Computation - Models, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany
| | - Christian Mayer
- Laboratory of Neurogenomics, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany
| | - Alexander Borst
- Department of Circuits - Computation - Models, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany
| | - Jesús Pujol-Martí
- Department of Circuits - Computation - Models, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany
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43
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Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex. Neuron 2020; 106:388-403.e18. [DOI: 10.1016/j.neuron.2020.01.040] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 10/17/2019] [Accepted: 01/27/2020] [Indexed: 01/08/2023]
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44
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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: 20] [Impact Index Per Article: 5.0] [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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45
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Davis FP, Nern A, Picard S, Reiser MB, Rubin GM, Eddy SR, Henry GL. A genetic, genomic, and computational resource for exploring neural circuit function. eLife 2020; 9:e50901. [PMID: 31939737 PMCID: PMC7034979 DOI: 10.7554/elife.50901] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 01/14/2020] [Indexed: 12/11/2022] Open
Abstract
The anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. Here, we characterize gene expression patterns in distinct neural cell types of the Drosophila visual system using genetic lines to access individual cell types, the TAPIN-seq method to measure their transcriptomes, and a probabilistic method to interpret these measurements. We used these tools to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types, available at http://www.opticlobe.com. Combining these transcriptomes with recently reported connectomes helps characterize how information is transmitted and processed across a range of scales, from individual synapses to circuit pathways. We describe examples that include identifying neurotransmitters, including cases of apparent co-release, generating functional hypotheses based on receptor expression, as well as identifying strong commonalities between different cell types.
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Affiliation(s)
- Fred P Davis
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Molecular Immunology and Inflammation BranchNational Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of HealthBethesdaUnited States
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Serge Picard
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Michael B Reiser
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Sean R Eddy
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Howard Hughes Medical Institute and Department of Molecular and Cellular BiologyHarvard UniversityCambridgeUnited States
- John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeUnited States
| | - Gilbert L Henry
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
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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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Gür B, Sporar K, Lopez-Behling A, Silies M. Distinct expression of potassium channels regulates visual response properties of lamina neurons in Drosophila melanogaster. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2019; 206:273-287. [PMID: 31823004 DOI: 10.1007/s00359-019-01385-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 10/23/2019] [Accepted: 11/21/2019] [Indexed: 01/11/2023]
Abstract
The computational organization of sensory systems depends on the diversification of individual cell types with distinct signal-processing capabilities. The Drosophila visual system, for instance, splits information into channels with different temporal properties directly downstream of photoreceptors in the first-order interneurons of the OFF pathway, L2 and L3. However, the biophysical mechanisms that determine this specialization are largely unknown. Here, we show that the voltage-gated Ka channels Shaker and Shal contribute to the response properties of the major OFF pathway input L2. L3 calcium response kinetics postsynaptic to photoreceptors resemble the sustained calcium signals of photoreceptors, whereas L2 neurons decay transiently. Based on a cell-type-specific RNA-seq data set and endogenous protein tagging, we identified Shaker and Shal as the primary candidates to shape L2 responses. Using in vivo two-photon imaging of L2 calcium signals in combination with pharmacological and genetic perturbations of these Ka channels, we show that the wild-type Shaker and Shal function is to enhance L2 responses and cell-autonomously sharpen L2 kinetics. Our results reveal a role for Ka channels in determining the signal-processing characteristics of a specific cell type in the visual system.
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Affiliation(s)
- Burak Gür
- Institute of Developmental Biology and Neurobiology, Johannes Gutenberg-Universität Mainz, 55128, Mainz, Germany
- European Neuroscience Institute Göttingen a Joint Initiative of the University Medical Center Göttingen, and the Max Planck Society, 37077, Göttingen, Germany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of Göttingen, Göttingen, Germany
| | - Katja Sporar
- Institute of Developmental Biology and Neurobiology, Johannes Gutenberg-Universität Mainz, 55128, Mainz, Germany
- European Neuroscience Institute Göttingen a Joint Initiative of the University Medical Center Göttingen, and the Max Planck Society, 37077, Göttingen, Germany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of Göttingen, Göttingen, Germany
| | - Anne Lopez-Behling
- European Neuroscience Institute Göttingen a Joint Initiative of the University Medical Center Göttingen, and the Max Planck Society, 37077, Göttingen, Germany
| | - Marion Silies
- Institute of Developmental Biology and Neurobiology, Johannes Gutenberg-Universität Mainz, 55128, Mainz, Germany.
- European Neuroscience Institute Göttingen a Joint Initiative of the University Medical Center Göttingen, and the Max Planck Society, 37077, Göttingen, Germany.
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48
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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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Neuromodulation of insect motion vision. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2019; 206:125-137. [DOI: 10.1007/s00359-019-01383-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 11/11/2019] [Accepted: 11/19/2019] [Indexed: 10/25/2022]
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50
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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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