1
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Lesser E, Azevedo AW, Phelps JS, Elabbady L, Cook A, Sakeena Syed D, Mark B, Kuroda S, Sustar A, Moussa A, Dallmann CJ, Agrawal S, Lee SYJ, Pratt B, Skutt-Kakaria K, Gerhard S, Lu R, Kemnitz N, Lee K, Halageri A, Castro M, Ih D, Gager J, Tammam M, Dorkenwald S, Collman F, Schneider-Mizell C, Brittain D, Jordan CS, Macrina T, Dickinson M, Lee WCA, Tuthill JC. Synaptic architecture of leg and wing premotor control networks in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.30.542725. [PMID: 37398440 PMCID: PMC10312524 DOI: 10.1101/2023.05.30.542725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. MN activity is coordinated by complex premotor networks that allow individual muscles to contribute to many different behaviors. Here, we use connectomics to analyze the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. In contrast, wing premotor networks lack proportional synaptic connectivity, which may allow wing steering muscles to be recruited with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.
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Affiliation(s)
- Ellen Lesser
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Anthony W. Azevedo
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Jasper S. Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Leila Elabbady
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Andrew Cook
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | | | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Sumiya Kuroda
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Anthony Moussa
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Chris J. Dallmann
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Su-Yee J. Lee
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Brandon Pratt
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | | | - Stephan Gerhard
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- UniDesign Solutions LLC, Switzerland
| | | | | | - Kisuk Lee
- Zetta AI, LLC, USA
- Princeton Neuroscience Institute, Princeton University, NJ, USA
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, NJ, USA
- Computer Science Department, Princeton University, NJ, USA
| | | | | | | | - Chris S. Jordan
- Princeton Neuroscience Institute, Princeton University, NJ, USA
| | | | | | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, MA, USA
| | - John C. Tuthill
- Department of Physiology and Biophysics, University of Washington, WA, USA
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2
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Karashchuk L, Li JS(L, Chou GM, Walling-Bell S, Brunton SL, Tuthill JC, Brunton BW. Sensorimotor delays constrain robust locomotion in a 3D kinematic model of fly walking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.589965. [PMID: 38712226 PMCID: PMC11071299 DOI: 10.1101/2024.04.18.589965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Walking animals must maintain stability in the presence of external perturbations, despite significant temporal delays in neural signaling and muscle actuation. Here, we develop a 3D kinematic model with a layered control architecture to investigate how sensorimotor delays constrain robustness of walking behavior in the fruit fly, Drosophila. Motivated by the anatomical architecture of insect locomotor control circuits, our model consists of three component layers: a neural network that generates realistic 3D joint kinematics for each leg, an optimal controller that executes the joint kinematics while accounting for delays, and an inter-leg coordinator. The model generates realistic simulated walking that matches real fly walking kinematics and sustains walking even when subjected to unexpected perturbations, generalizing beyond its training data. However, we found that the model's robustness to perturbations deteriorates when sensorimotor delay parameters exceed the physiological range. These results suggest that fly sensorimotor control circuits operate close to the temporal limit at which they can detect and respond to external perturbations. More broadly, we show how a modular, layered model architecture can be used to investigate physiological constraints on animal behavior.
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Affiliation(s)
- Lili Karashchuk
- Neuroscience Graduate Program, University of Washington, Seattle
| | - Jing Shuang (Lisa) Li
- Dept of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
| | - Grant M. Chou
- Dept of Physiology & Biophysics, University of Washington, Seattle
| | | | | | - John C. Tuthill
- Dept of Physiology & Biophysics, University of Washington, Seattle
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3
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Westerlaken M. Digital twins and the digital logics of biodiversity. SOCIAL STUDIES OF SCIENCE 2024:3063127241236809. [PMID: 38511604 DOI: 10.1177/03063127241236809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Biodiversity is a multidimensional concept that can be understood and measured in many different ways. However, the next generation of digital technologies for biodiversity monitoring currently being funded and developed fail to engage its multidimensional and relational aspects. Based on empirical data from interviews, a conference visit, online meetings, webinars, and project reports, this study articulates four digital logics that structure how biodiversity becomes monitored and understood within recent technological developments. The four digital logics illustrate how intensified practices of capturing, connecting, simulating, and computing produce particular techno-scientific formats for creating biodiversity knowledge. While ongoing projects advance technological development in areas of automation, prediction, and the creation of large-scale species databases, their developmental processes structurally limit the future of biodiversity technology. To better address the complex challenges of the global biodiversity crisis, it is crucial to develop digital technologies and practices that can engage with a wider range of perspectives and understandings of relational and multidimensional approaches to biodiversity.
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4
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Arreguit J, Ramalingasetty ST, Ijspeert A. FARMS: Framework for Animal and Robot Modeling and Simulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.25.559130. [PMID: 38293071 PMCID: PMC10827226 DOI: 10.1101/2023.09.25.559130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The study of animal locomotion and neuromechanical control offers valuable insights for advancing research in neuroscience, biomechanics, and robotics. We have developed FARMS (Framework for Animal and Robot Modeling and Simulation), an open-source, interdisciplinary framework, designed to facilitate access to neuromechanical simulations for modeling, simulation, and analysis of animal locomotion and bio-inspired robotic systems. By providing an accessible and user-friendly platform, FARMS aims to lower the barriers for researchers to explore the complex interactions between the nervous system, musculoskeletal structures, and their environment. Integrating the MuJoCo physics engine in a modular manner, FARMS enables realistic simulations and fosters collaboration among neuroscientists, biologists, and roboticists. FARMS has already been extensively used to study locomotion in animals such as mice, drosophila, fish, salamanders, and centipedes, serving as a platform to investigate the role of central pattern generators and sensory feedback. This article provides an overview of the FARMS framework, discusses its interdisciplinary approach, showcases its versatility through specific case studies, and highlights its effectiveness in advancing our understanding of locomotion. In particular, we show how we used FARMS to study amphibious locomotion by presenting experimental demonstrations across morphologies and environments based on neural controllers with central pattern generators and sensory feedback circuits models. Overall, the goal of FARMS is to contribute to a deeper understanding of animal locomotion, the development of innovative bio-inspired robotic systems, and promote accessibility in neuromechanical research.
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Affiliation(s)
- Jonathan Arreguit
- BioRob, School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Shravan Tata Ramalingasetty
- BioRob, School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Neurobiology and Anatomy, College of Medicine, Drexel University, Philadelphia, USA
| | - Auke Ijspeert
- BioRob, School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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5
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Lindsay GW. Grounding neuroscience in behavioral changes using artificial neural networks. Curr Opin Neurobiol 2024; 84:102816. [PMID: 38052111 DOI: 10.1016/j.conb.2023.102816] [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: 02/02/2023] [Revised: 09/15/2023] [Accepted: 11/05/2023] [Indexed: 12/07/2023]
Abstract
Connecting neural activity to function is a common aim in neuroscience. How to define and conceptualize function, however, can vary. Here I focus on grounding this goal in the specific question of how a given change in behavior is produced by a change in neural circuits or activity. Artificial neural network models offer a particularly fruitful format for tackling such questions because they use neural mechanisms to perform complex transformations and produce appropriate behavior. Therefore, they can be a means of causally testing the extent to which a neural change can be responsible for an experimentally observed behavioral change. Furthermore, because the field of interpretability in artificial intelligence has similar aims, neuroscientists can look to interpretability methods for new ways of identifying neural features that drive performance and behaviors.
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Affiliation(s)
- Grace W Lindsay
- Department of Psychology and Center for Data Science, New York University, USA.
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6
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An L, Ren J, Yu T, Hai T, Jia Y, Liu Y. Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL. Nat Commun 2023; 14:7727. [PMID: 38001106 PMCID: PMC10673844 DOI: 10.1038/s41467-023-43483-w] [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: 10/06/2022] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Understandings of the three-dimensional social behaviors of freely moving large-size mammals are valuable for both agriculture and life science, yet challenging due to occlusions in close interactions. Although existing animal pose estimation methods captured keypoint trajectories, they ignored deformable surfaces which contained geometric information essential for social interaction prediction and for dealing with the occlusions. In this study, we develop a Multi-Animal Mesh Model Alignment (MAMMAL) system based on an articulated surface mesh model. Our self-designed MAMMAL algorithms automatically enable us to align multi-view images into our mesh model and to capture 3D surface motions of multiple animals, which display better performance upon severe occlusions compared to traditional triangulation and allow complex social analysis. By utilizing MAMMAL, we are able to quantitatively analyze the locomotion, postures, animal-scene interactions, social interactions, as well as detailed tail motions of pigs. Furthermore, experiments on mouse and Beagle dogs demonstrate the generalizability of MAMMAL across different environments and mammal species.
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Affiliation(s)
- Liang An
- Department of Automation, Tsinghua University, Beijing, China
| | - Jilong Ren
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Beijing Farm Animal Research Center, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Tao Yu
- Department of Automation, Tsinghua University, Beijing, China
- Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China
| | - Tang Hai
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
- Beijing Farm Animal Research Center, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
| | - Yichang Jia
- School of Medicine, Tsinghua University, Beijing, China.
- IDG/McGovern Institute for Brain Research at Tsinghua, Beijing, China.
- Tsinghua Laboratory of Brain and Intelligence, Beijing, China.
| | - Yebin Liu
- Department of Automation, Tsinghua University, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
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7
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Maya R, Lerner N, Ben-Dov O, Pons A, Beatus T. A hull reconstruction-reprojection method for pose estimation of free-flying fruit flies. J Exp Biol 2023; 226:jeb245853. [PMID: 37795876 PMCID: PMC10629692 DOI: 10.1242/jeb.245853] [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: 04/02/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023]
Abstract
Understanding the mechanisms of insect flight requires high-quality data of free-flight kinematics, e.g. for comparative studies or genetic screens. Although recent improvements in high-speed videography allow us to acquire large amounts of free-flight data, a significant bottleneck is automatically extracting accurate body and wing kinematics. Here, we present an experimental system and a hull reconstruction-reprojection algorithm for measuring the flight kinematics of fruit flies. The experimental system can automatically record hundreds of flight events per day. Our algorithm resolves a significant portion of the occlusions in this system by a reconstruction-reprojection scheme that integrates information from all cameras. Wing and body kinematics, including wing deformation, are then extracted from the hulls of the wing boundaries and body. This model-free method is fully automatic, accurate and open source, and can be readily adjusted for different camera configurations or insect species.
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Affiliation(s)
- Roni Maya
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Center of Bioengineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Noam Lerner
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Center of Bioengineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Omri Ben-Dov
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Center of Bioengineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Arion Pons
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Center of Bioengineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Tsevi Beatus
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Center of Bioengineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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8
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Cruz TL, Chiappe ME. Multilevel visuomotor control of locomotion in Drosophila. Curr Opin Neurobiol 2023; 82:102774. [PMID: 37651855 DOI: 10.1016/j.conb.2023.102774] [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: 04/03/2023] [Revised: 07/26/2023] [Accepted: 08/01/2023] [Indexed: 09/02/2023]
Abstract
Vision is critical for the control of locomotion, but the underlying neural mechanisms by which visuomotor circuits contribute to the movement of the body through space are yet not well understood. Locomotion engages multiple control systems, forming distinct interacting "control levels" driven by the activity of distributed and overlapping circuits. Therefore, a comprehensive understanding of the mechanisms underlying locomotion control requires the consideration of all control levels and their necessary coordination. Due to their small size and the wide availability of experimental tools, Drosophila has become an important model system to study this coordination. Traditionally, insect locomotion has been divided into studying either the biomechanics and local control of limbs, or navigation and course control. However, recent developments in tracking techniques, and physiological and genetic tools in Drosophila have prompted researchers to examine multilevel control coordination in flight and walking.
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Affiliation(s)
- Tomás L Cruz
- Champalimaud Research, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal
| | - M Eugenia Chiappe
- Champalimaud Research, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal.
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9
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Dallmann CJ, Dickerson BH, Simpson JH, Wyart C, Jayaram K. Mechanosensory Control of Locomotion in Animals and Robots: Moving Forward. Integr Comp Biol 2023; 63:450-463. [PMID: 37279901 PMCID: PMC10445419 DOI: 10.1093/icb/icad057] [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: 02/28/2023] [Revised: 05/10/2023] [Accepted: 05/24/2023] [Indexed: 06/08/2023] Open
Abstract
While animals swim, crawl, walk, and fly with apparent ease, building robots capable of robust locomotion remains a significant challenge. In this review, we draw attention to mechanosensation-the sensing of mechanical forces generated within and outside the body-as a key sense that enables robust locomotion in animals. We discuss differences between mechanosensation in animals and current robots with respect to (1) the encoding properties and distribution of mechanosensors and (2) the integration and regulation of mechanosensory feedback. We argue that robotics would benefit greatly from a detailed understanding of these aspects in animals. To that end, we highlight promising experimental and engineering approaches to study mechanosensation, emphasizing the mutual benefits for biologists and engineers that emerge from moving forward together.
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Affiliation(s)
- Chris J Dallmann
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Bradley H Dickerson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Julie H Simpson
- Department of Molecular, Cellular, and Developmental Biology and Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Claire Wyart
- Institut du Cerveau et de la Moelle épinière (ICM), Sorbonne Université, Paris 75005, France
| | - Kaushik Jayaram
- Paul M Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO 80309, USA
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10
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Ramdya P, Ijspeert AJ. The neuromechanics of animal locomotion: From biology to robotics and back. Sci Robot 2023; 8:eadg0279. [PMID: 37256966 DOI: 10.1126/scirobotics.adg0279] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/05/2023] [Indexed: 06/02/2023]
Abstract
Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating animals. These include the use of high-level commands to control low-level central pattern generator-like controllers, which, in turn, are informed by sensory feedback. Reciprocally, neuroscience has benefited from tools and intuitions in robotics to reveal how embodiment, physical interactions with the environment, and sensory feedback help sculpt animal behavior. We illustrate and discuss exemplar studies of this dialog between robotics and neuroscience. We also reveal how the increasing biorealism of simulations and robots is driving these two disciplines together, forging an integrative science of autonomous behavioral control with many exciting future opportunities.
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Affiliation(s)
- Pavan Ramdya
- Neuroengineering Laboratory, Brain Mind Institute and Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Auke Jan Ijspeert
- Biorobotics Laboratory, Institute of Bioengineering, EPFL, Lausanne, Switzerland
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11
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Zhou KC, Harfouche M, Cooke CL, Park J, Konda PC, Kreiss L, Kim K, Jönsson J, Doman T, Reamey P, Saliu V, Cook CB, Zheng M, Bechtel JP, Bègue A, McCarroll M, Bagwell J, Horstmeyer G, Bagnat M, Horstmeyer R. Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second. NATURE PHOTONICS 2023; 17:442-450. [PMID: 37808252 PMCID: PMC10552607 DOI: 10.1038/s41566-023-01171-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/03/2023] [Indexed: 10/10/2023]
Abstract
Wide field of view microscopy that can resolve 3D information at high speed and spatial resolution is highly desirable for studying the behaviour of freely moving model organisms. However, it is challenging to design an optical instrument that optimises all these properties simultaneously. Existing techniques typically require the acquisition of sequential image snapshots to observe large areas or measure 3D information, thus compromising on speed and throughput. Here, we present 3D-RAPID, a computational microscope based on a synchronized array of 54 cameras that can capture high-speed 3D topographic videos over an area of 135 cm2, achieving up to 230 frames per second at spatiotemporal throughputs exceeding 5 gigapixels per second. 3D-RAPID employs a 3D reconstruction algorithm that, for each synchronized snapshot, fuses all 54 images into a composite that includes a co-registered 3D height map. The self-supervised 3D reconstruction algorithm trains a neural network to map raw photometric images to 3D topography using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. The resulting reconstruction process is thus robust to generalization errors and scales to arbitrarily long videos from arbitrarily sized camera arrays. We demonstrate the broad applicability of 3D-RAPID with collections of several freely behaving organisms, including ants, fruit flies, and zebrafish larvae.
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Affiliation(s)
- Kevin C. Zhou
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
- Current affiliation: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Mark Harfouche
- Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
| | - Colin L. Cooke
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Jaehee Park
- Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
| | - Pavan C. Konda
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Lucas Kreiss
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Kanghyun Kim
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Joakim Jönsson
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Thomas Doman
- Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
| | - Paul Reamey
- Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
| | - Veton Saliu
- Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
| | - Clare B. Cook
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
| | - Maxwell Zheng
- Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
| | | | - Aurélien Bègue
- Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
| | - Matthew McCarroll
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
| | - Jennifer Bagwell
- Department of Cell Biology, Duke University, Durham, NC 27710, USA
| | | | - Michel Bagnat
- Department of Cell Biology, Duke University, Durham, NC 27710, USA
| | - Roarke Horstmeyer
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Ramona Optics Inc., 1000 W Main St., Durham, NC 27701, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
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12
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Floegel M, Kasper J, Perrier P, Kell CA. How the conception of control influences our understanding of actions. Nat Rev Neurosci 2023; 24:313-329. [PMID: 36997716 DOI: 10.1038/s41583-023-00691-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/28/2023] [Indexed: 04/01/2023]
Abstract
Wilful movement requires neural control. Commonly, neural computations are thought to generate motor commands that bring the musculoskeletal system - that is, the plant - from its current physical state into a desired physical state. The current state can be estimated from past motor commands and from sensory information. Modelling movement on the basis of this concept of plant control strives to explain behaviour by identifying the computational principles for control signals that can reproduce the observed features of movements. From an alternative perspective, movements emerge in a dynamically coupled agent-environment system from the pursuit of subjective perceptual goals. Modelling movement on the basis of this concept of perceptual control aims to identify the controlled percepts and their coupling rules that can give rise to the observed characteristics of behaviour. In this Perspective, we discuss a broad spectrum of approaches to modelling human motor control and their notions of control signals, internal models, handling of sensory feedback delays and learning. We focus on the influence that the plant control and the perceptual control perspective may have on decisions when modelling empirical data, which may in turn shape our understanding of actions.
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Affiliation(s)
- Mareike Floegel
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Johannes Kasper
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Pascal Perrier
- Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, Grenoble, France
| | - Christian A Kell
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany.
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13
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Chen CL, Aymanns F, Minegishi R, Matsuda VDV, Talabot N, Günel S, Dickson BJ, Ramdya P. Ascending neurons convey behavioral state to integrative sensory and action selection brain regions. Nat Neurosci 2023; 26:682-695. [PMID: 36959417 PMCID: PMC10076225 DOI: 10.1038/s41593-023-01281-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 02/14/2023] [Indexed: 03/25/2023]
Abstract
Knowing one's own behavioral state has long been theorized as critical for contextualizing dynamic sensory cues and identifying appropriate future behaviors. Ascending neurons (ANs) in the motor system that project to the brain are well positioned to provide such behavioral state signals. However, what ANs encode and where they convey these signals remains largely unknown. Here, through large-scale functional imaging in behaving animals and morphological quantification, we report the behavioral encoding and brain targeting of hundreds of genetically identifiable ANs in the adult fly, Drosophila melanogaster. We reveal that ANs encode behavioral states, specifically conveying self-motion to the anterior ventrolateral protocerebrum, an integrative sensory hub, as well as discrete actions to the gnathal ganglia, a locus for action selection. Additionally, AN projection patterns within the motor system are predictive of their encoding. Thus, ascending populations are well poised to inform distinct brain hubs of self-motion and ongoing behaviors and may provide an important substrate for computations that are required for adaptive behavior.
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Affiliation(s)
- Chin-Lin Chen
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Florian Aymanns
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Ryo Minegishi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Victor D V Matsuda
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Nicolas Talabot
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
- Computer Vision Laboratory, EPFL, Lausanne, Switzerland
| | - Semih Günel
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland
- Computer Vision Laboratory, EPFL, Lausanne, Switzerland
| | - Barry J Dickson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Pavan Ramdya
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland.
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Singh SH, van Breugel F, Rao RPN, Brunton BW. Emergent behaviour and neural dynamics in artificial agents tracking odour plumes. NAT MACH INTELL 2023; 5:58-70. [PMID: 37886259 PMCID: PMC10601839 DOI: 10.1038/s42256-022-00599-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 12/01/2022] [Indexed: 01/26/2023]
Abstract
Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have been studied experimentally. Here we take a complementary in silico approach to develop an integrated understanding of their behaviour and neural computations. Specifically, we train artificial recurrent neural network agents using deep reinforcement learning to locate the source of simulated odour plumes that mimic features of plumes in a turbulent flow. Interestingly, the agents' emergent behaviours resemble those of flying insects, and the recurrent neural networks learn to compute task-relevant variables with distinct dynamic structures in population activity. Our analyses put forward a testable behavioural hypothesis for tracking plumes in changing wind direction, and we provide key intuitions for memory requirements and neural dynamics in odour plume tracking.
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15
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Cregg JM, Mirdamadi JL, Fortunato C, Okorokova EV, Kuper C, Nayeem R, Byun AJ, Avraham C, Buonocore A, Winner TS, Mildren RL. Highlights from the 31st Annual Meeting of the Society for the Neural Control of Movement. J Neurophysiol 2023; 129:220-234. [PMID: 36541602 PMCID: PMC9844973 DOI: 10.1152/jn.00500.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Jared M Cregg
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jasmine L Mirdamadi
- Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Cátia Fortunato
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | | | - Clara Kuper
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
- School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Rashida Nayeem
- Department of Electrical Engineering, Northeastern University, Boston, Massachusetts
| | - Andrew J Byun
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
| | - Chen Avraham
- Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beersheva, Israel
| | - Antimo Buonocore
- Werner Reichardt Centre for Integrative Neuroscience, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Department of Educational, Psychological and Communication Sciences, Suor Orsola Benincasa University, Naples, Italy
| | - Taniel S Winner
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
| | - Robyn L Mildren
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland
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16
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Kalaichelvan K, Kausar N, Kousar S, Karaca Y, Pamucar D, Salman MA. Economic Order Quantity Model-Based Optimized Fuzzy Nonlinear Dynamic Mathematical Schemes. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3881265. [PMID: 37377747 PMCID: PMC10292942 DOI: 10.1155/2022/3881265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 08/05/2023]
Abstract
Fuzzy mathematics-informed methods are beneficial in cases when observations display uncertainty and volatility since it is of vital importance to make predictions about the future considering the stages of interpreting, planning, and strategy building. It is possible to realize this aim through accurate, reliable, and realistic data and information analysis, emerging from past to present time. The principal expenditures are treated as fuzzy numbers in this article, which includes a blurry categorial prototype with pattern-diverse stipulation and collapse with salvation worth. Multiple parameters such as a shortage, ordering, and degrading cost are not fixed in nature due to uncertainty in the marketplace. Obtaining an accurate estimate of such expenditures is challenging. Accordingly, in this research, we develop an adaptive and integrative economic order quantity model with a fuzzy method and present an appropriate structure to manage such uncertain parameters, boosting the inventory system's exactness, and computing efficiency. The major goal of the study was to assess a set of changes to the company current inventory processes that allowed an achievement in its inventory costs optimization and system development in optimizing inventory costs for better control and monitoring. The approach of graded mean integration is used to determine the most efficient actual solution. The evidence-based model is illustrated with the help of appropriate numerical and sensitivity analysis through the related visual graphical depictions. The proposed method in our study aims at investigating the economic order quantity (EOQ), as the optimal order quantity, which is significant in inventory management to minimize the total costs related to ordering, receiving, and holding inventory in the dynamic domains with nonlinear features of the complex dynamic and nonlinear systems as well as structures.
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Affiliation(s)
- Kalaiarasi Kalaichelvan
- PG and Research Department of Mathematics, Cauvery College for Women (Affiliated to Bharathidasan University), Tiruchirappalli 620018, Tamil Nadu, India
| | - Nasreen Kausar
- Department of Mathematics, Yildiz Technical University, Faculty of Arts and Science, Esenler 34210, Istanbul, Turkey
| | - Sajida Kousar
- Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan
| | - Yeliz Karaca
- University of Massachusetts Medical School (UMASS), Worcester, MA 01655, USA
| | - Dragan Pamucar
- Department of Logistics, University of Defence in Belgrade, Belgrade, Serbia
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17
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Aymanns F, Chen CL, Ramdya P. Descending neuron population dynamics during odor-evoked and spontaneous limb-dependent behaviors. eLife 2022; 11:81527. [PMID: 36286408 PMCID: PMC9605690 DOI: 10.7554/elife.81527] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 10/13/2022] [Indexed: 11/21/2022] Open
Abstract
Deciphering how the brain regulates motor circuits to control complex behaviors is an important, long-standing challenge in neuroscience. In the fly, Drosophila melanogaster, this is coordinated by a population of ~ 1100 descending neurons (DNs). Activating only a few DNs is known to be sufficient to drive complex behaviors like walking and grooming. However, what additional role the larger population of DNs plays during natural behaviors remains largely unknown. For example, they may modulate core behavioral commands or comprise parallel pathways that are engaged depending on sensory context. We evaluated these possibilities by recording populations of nearly 100 DNs in individual tethered flies while they generated limb-dependent behaviors, including walking and grooming. We found that the largest fraction of recorded DNs encode walking while fewer are active during head grooming and resting. A large fraction of walk-encoding DNs encode turning and far fewer weakly encode speed. Although odor context does not determine which behavior-encoding DNs are recruited, a few DNs encode odors rather than behaviors. Lastly, we illustrate how one can identify individual neurons from DN population recordings by using their spatial, functional, and morphological properties. These results set the stage for a comprehensive, population-level understanding of how the brain’s descending signals regulate complex motor actions.
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Affiliation(s)
- Florian Aymanns
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFLLausanneSwitzerland
| | - Chin-Lin Chen
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFLLausanneSwitzerland
| | - Pavan Ramdya
- Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFLLausanneSwitzerland
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