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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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2
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Dinges GF, Zyhowski WP, Lucci A, Friend J, Szczecinski NS. Mechanical modeling of mechanosensitive insect strain sensors as a tool to investigate exoskeletal interfaces. BIOINSPIRATION & BIOMIMETICS 2024; 19:026012. [PMID: 38211340 DOI: 10.1088/1748-3190/ad1db9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/11/2024] [Indexed: 01/13/2024]
Abstract
During walking, sensory information is measured and monitored by sensory organs that can be found on and within various limb segments. Strain can be monitored by insect load sensors, campaniform sensilla (CS), which have components embedded within the exoskeleton. CS vary in eccentricity, size, and orientation, which can affect their sensitivity to specific strains. Directly investigating the mechanical interfaces that these sensors utilize to encode changes in load bears various obstacles, such as modeling of viscoelastic properties. To circumvent the difficulties of modeling and performing biological experiments in small insects, we developed 3-dimensional printed resin models based on high-resolution imaging of CS. Through the utilization of strain gauges and a motorized tensile tester, physiologically plausible strain can be mimicked while investigating the compression and tension forces that CS experience; here, this was performed for a field of femoral CS inDrosophila melanogaster. Different loading scenarios differentially affected CS compression and the likely neuronal activity of these sensors and elucidate population coding of stresses acting on the cuticle.
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Affiliation(s)
- Gesa F Dinges
- Neuro-Mechanical Intelligence Laboratory, Department of Mechanical, Materials, and Aerospace Engineering, West Virginia University, Morgantown, WV, United States of America
| | - William P Zyhowski
- Neuro-Mechanical Intelligence Laboratory, Department of Mechanical, Materials, and Aerospace Engineering, West Virginia University, Morgantown, WV, United States of America
| | - Anastasia Lucci
- Lane Innovation Hub, West Virginia University, Morgantown, WV, United States of America
| | - Jordan Friend
- Lane Innovation Hub, West Virginia University, Morgantown, WV, United States of America
| | - Nicholas S Szczecinski
- Neuro-Mechanical Intelligence Laboratory, Department of Mechanical, Materials, and Aerospace Engineering, West Virginia University, Morgantown, WV, United States of America
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3
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Sutton GP, Szczecinski NS, Quinn RD, Chiel HJ. Phase shift between joint rotation and actuation reflects dominant forces and predicts muscle activation patterns. PNAS NEXUS 2023; 2:pgad298. [PMID: 37822766 PMCID: PMC10563792 DOI: 10.1093/pnasnexus/pgad298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/29/2023] [Indexed: 10/13/2023]
Abstract
During behavior, the work done by actuators on the body can be resisted by the body's inertia, elastic forces, gravity, or viscosity. The dominant forces that resist actuation have major consequences on the control of that behavior. In the literature, features and actuation of locomotion, for example, have been successfully predicted by nondimensional numbers (e.g. Froude number and Reynolds number) that generally express the ratio between two of these forces (gravitational, inertial, elastic, and viscous). However, animals of different sizes or motions at different speeds may not share the same dominant forces within a behavior, making ratios of just two of these forces less useful. Thus, for a broad comparison of behavior across many orders of magnitude of limb length and cycle period, a dimensionless number that includes gravitational, inertial, elastic, and viscous forces is needed. This study proposes a nondimensional number that relates these four forces: the phase shift (ϕ) between the displacement of the limb and the actuator force that moves it. Using allometric scaling laws, ϕ for terrestrial walking is expressed as a function of the limb length and the cycle period at which the limb steps. Scale-dependent values of ϕ are used to explain and predict the electromyographic (EMG) patterns employed by different animals as they walk.
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Affiliation(s)
- G P Sutton
- School of Life Sciences, University of Lincoln, Lincoln LN6 7TS, UK
| | - N S Szczecinski
- Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506-6106, USA
| | - R D Quinn
- Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - H J Chiel
- Department of Biology, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Neuroscience, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
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4
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Nourse WRP, Jackson C, Szczecinski NS, Quinn RD. SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber-Physical Systems. Biomimetics (Basel) 2023; 8:247. [PMID: 37366842 DOI: 10.3390/biomimetics8020247] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/02/2023] [Accepted: 06/09/2023] [Indexed: 06/28/2023] Open
Abstract
One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS). These networks are often developed using cyclic topologies and heterogeneous mixtures of spiking and non-spiking neurons, which is a difficult proposition for existing neural simulation software. Most solutions apply to either one of two extremes, the detailed multi-compartment neural models in small networks, and the large-scale networks of greatly simplified neural models. In this work, we present our open-source Python package SNS-Toolbox, which is capable of simulating hundreds to thousands of spiking and non-spiking neurons in real-time or faster on consumer-grade computer hardware. We describe the neural and synaptic models supported by SNS-Toolbox, and provide performance on multiple software and hardware backends, including GPUs and embedded computing platforms. We also showcase two examples using the software, one for controlling a simulated limb with muscles in the physics simulator Mujoco, and another for a mobile robot using ROS. We hope that the availability of this software will reduce the barrier to entry when designing SNS networks, and will increase the prevalence of SNS networks in the field of robotic control.
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Affiliation(s)
- William R P Nourse
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Clayton Jackson
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Nicholas S Szczecinski
- Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Roger D Quinn
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
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5
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Mangan M, Floreano D, Yasui K, Trimmer BA, Gravish N, Hauert S, Webb B, Manoonpong P, Szczecinski N. A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research. BIOINSPIRATION & BIOMIMETICS 2023; 18:035005. [PMID: 36881919 DOI: 10.1088/1748-3190/acc223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.
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Affiliation(s)
- Michael Mangan
- The University of Sheffield, Mappin St, Sheffield S10 2TN, United Kingdom
| | - Dario Floreano
- Ecole Polytechnique Federale de Lausanne, Laboratory of Intelligent Systems, Station 9, Lausanne CH-1015, Switzerland
| | - Kotaro Yasui
- Tohoku University, Frontier Research Institute for Interdisciplinary Sciences, 6-3 Aramaki aza Aoba, Aoba-ku, Sendai 980-8578, Japan
| | - Barry A Trimmer
- Tufts University, Biology, 200 Boston Av, Boston, MA 02111, United States of America
| | - Nick Gravish
- University of California San Diego, Mechanical and Aerospace Engineering, Building EBU II, La Jolla, CA 92093, United States of America
| | - Sabine Hauert
- University of Bristol, Engineering Mathematics, Bristol BS8 1QU, United Kingdom
| | - Barbara Webb
- University of Edinburgh, School of Informatics, 10 Crichton St, Edinburgh EH8 9AB, United Kingdom
| | - Poramate Manoonpong
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China
- Bio-Inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Wangchan Valley, Rayong 21210, Thailand
| | - Nicholas Szczecinski
- West Virginia University, Mechanical and Aerospace Engineering, Morgantown, WV 26506-6201, United States of America
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Li Y, Li P, Zhang W, Zheng X, Gu Q. New Wine in Old Bottle: Caenorhabditis Elegans in Food Science. FOOD REVIEWS INTERNATIONAL 2023. [DOI: 10.1080/87559129.2023.2172429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- Yonglu Li
- School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, People’s Republic of China
| | - Ping Li
- School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, People’s Republic of China
| | - Weixi Zhang
- Department of Food Science and Nutrition; Zhejiang Key Laboratory for Agro-food Processing; Fuli Institute of Food Science; National Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang University, Hangzhou, People’s Republic of China
| | - Xiaodong Zheng
- Department of Food Science and Nutrition; Zhejiang Key Laboratory for Agro-food Processing; Fuli Institute of Food Science; National Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang University, Hangzhou, People’s Republic of China
| | - Qing Gu
- School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, People’s Republic of China
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Zyhowski WP, Zill SN, Szczecinski NS. Adaptive load feedback robustly signals force dynamics in robotic model of Carausius morosus stepping. Front Neurorobot 2023; 17:1125171. [PMID: 36776993 PMCID: PMC9908954 DOI: 10.3389/fnbot.2023.1125171] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Animals utilize a number of neuronal systems to produce locomotion. One type of sensory organ that contributes in insects is the campaniform sensillum (CS) that measures the load on their legs. Groups of the receptors are found on high stress regions of the leg exoskeleton and they have significant effects in adapting walking behavior. Recording from these sensors in freely moving animals is limited by technical constraints. To better understand the load feedback signaled by CS to the nervous system, we have constructed a dynamically scaled robotic model of the Carausius morosus stick insect middle leg. The leg steps on a treadmill and supports weight during stance to simulate body weight. Strain gauges were mounted in the same positions and orientations as four key CS groups (Groups 3, 4, 6B, and 6A). Continuous data from the strain gauges were processed through a previously published dynamic computational model of CS discharge. Our experiments suggest that under different stepping conditions (e.g., changing "body" weight, phasic load stimuli, slipping foot), the CS sensory discharge robustly signals increases in force, such as at the beginning of stance, and decreases in force, such as at the end of stance or when the foot slips. Such signals would be crucial for an insect or robot to maintain intra- and inter-leg coordination while walking over extreme terrain.
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Affiliation(s)
- William P. Zyhowski
- Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, United States,*Correspondence: William P. Zyhowski,
| | - Sasha N. Zill
- Department of Biomedical Sciences, Marshall University, Huntington, WV, United States
| | - Nicholas S. Szczecinski
- Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, United States
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Guschlbauer C, Hooper SL, Mantziaris C, Schwarz A, Szczecinski NS, Büschges A. Correlation between ranges of leg walking angles and passive rest angles among leg types in stick insects. Curr Biol 2022; 32:2334-2340.e3. [DOI: 10.1016/j.cub.2022.04.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 02/07/2022] [Accepted: 04/06/2022] [Indexed: 12/12/2022]
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9
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Manoonpong P, Patanè L, Xiong X, Brodoline I, Dupeyroux J, Viollet S, Arena P, Serres JR. Insect-Inspired Robots: Bridging Biological and Artificial Systems. SENSORS (BASEL, SWITZERLAND) 2021; 21:7609. [PMID: 34833685 PMCID: PMC8623770 DOI: 10.3390/s21227609] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 12/18/2022]
Abstract
This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.
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Affiliation(s)
- Poramate Manoonpong
- Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230 Odense, Denmark;
- Bio-Inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong 21210, Thailand
| | - Luca Patanè
- Department of Engineering, University of Messina, 98100 Messina, Italy
| | - Xiaofeng Xiong
- Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230 Odense, Denmark;
| | - Ilya Brodoline
- Department of Biorobotics, Aix Marseille University, CNRS, ISM, CEDEX 07, 13284 Marseille, France; (I.B.); (S.V.)
| | - Julien Dupeyroux
- Faculty of Aerospace Engineering, Delft University of Technology, 52600 Delft, The Netherlands;
| | - Stéphane Viollet
- Department of Biorobotics, Aix Marseille University, CNRS, ISM, CEDEX 07, 13284 Marseille, France; (I.B.); (S.V.)
| | - Paolo Arena
- Department of Electrical, Electronic and Computer Engineering, University of Catania, 95131 Catania, Italy
| | - Julien R. Serres
- Department of Biorobotics, Aix Marseille University, CNRS, ISM, CEDEX 07, 13284 Marseille, France; (I.B.); (S.V.)
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Goldsmith CA, Quinn RD, Szczecinski NS. Investigating the role of low level reinforcement reflex loops in insect locomotion. BIOINSPIRATION & BIOMIMETICS 2021; 16:065008. [PMID: 34547724 DOI: 10.1088/1748-3190/ac28ea] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Insects are highly capable walkers, but many questions remain regarding how the insect nervous system controls locomotion. One particular question is how information is communicated between the 'lower level' ventral nerve cord (VNC) and the 'higher level' head ganglia to facilitate control. In this work, we seek to explore this question by investigating how systems traditionally described as 'positive feedback' may initiate and maintain stepping in the VNC with limited information exchanged between lower and higher level centers. We focus on the 'reflex reversal' of the stick insect femur-tibia joint between a resistance reflex (RR) and an active reaction in response to joint flexion, as well as the activation of populations of descending dorsal median unpaired (desDUM) neurons from limb strain as our primary reflex loops. We present the development of a neuromechanical model of the stick insect (Carausius morosus) femur-tibia (FTi) and coxa-trochanter joint control networks 'in-the-loop' with a physical robotic limb. The control network generates motor commands for the robotic limb, whose motion and forces generate sensory feedback for the network. We based our network architecture on the anatomy of the non-spiking interneuron joint control network that controls the FTi joint, extrapolated network connectivity based on known muscle responses, and previously developed mechanisms to produce 'sideways stepping'. Previous studies hypothesized that RR is enacted by selective inhibition of sensory afferents from the femoral chordotonal organ, but no study has tested this hypothesis with a model of an intact limb. We found that inhibiting the network's flexion position and velocity afferents generated a reflex reversal in the robot limb's FTi joint. We also explored the intact network's ability to sustain steady locomotion on our test limb. Our results suggested that the reflex reversal and limb strain reinforcement mechanisms are both necessary but individually insufficient to produce and maintain rhythmic stepping in the limb, which can be initiated or halted by brief, transient descending signals. Removing portions of this feedback loop or creating a large enough disruption can halt stepping independent of the higher-level centers. We conclude by discussing why the nervous system might control motor output in this manner, as well as how to apply these findings to generalized nervous system understanding and improved robotic control.
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Affiliation(s)
- C A Goldsmith
- West Virginia University, One Waterfront Place, Morgantown, WV 26506, United States of America
| | - R D Quinn
- Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States of America
| | - N S Szczecinski
- West Virginia University, One Waterfront Place, Morgantown, WV 26506, United States of America
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11
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Karashchuk P, Rupp KL, Dickinson ES, Walling-Bell S, Sanders E, Azim E, Brunton BW, Tuthill JC. Anipose: A toolkit for robust markerless 3D pose estimation. Cell Rep 2021; 36:109730. [PMID: 34592148 PMCID: PMC8498918 DOI: 10.1016/j.celrep.2021.109730] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/15/2021] [Accepted: 08/27/2021] [Indexed: 01/12/2023] Open
Abstract
Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals move in 3D. Here, we introduce Anipose, an open-source toolkit for robust markerless 3D pose estimation. Anipose is built on the 2D tracking method DeepLabCut, so users can expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on a calibration board as well as mice, flies, and humans. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. To help users get started with 3D tracking, we provide tutorials and documentation at http://anipose.org/.
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Affiliation(s)
- Pierre Karashchuk
- Neuroscience Graduate Program, University of Washington, Seattle, WA, USA
| | - Katie L. Rupp
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Evyn S. Dickinson
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sarah Walling-Bell
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Elischa Sanders
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Eiman Azim
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bingni W. Brunton
- Department of Biology, University of Washington, Seattle, WA, USA,Senior author,Correspondence: (B.W.B.), (J.C.T.)
| | - John C. Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA,Senior author,Lead contact,Correspondence: (B.W.B.), (J.C.T.)
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12
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Szczecinski NS, Dallmann CJ, Quinn RD, Zill SN. A computational model of insect campaniform sensilla predicts encoding of forces during walking. BIOINSPIRATION & BIOMIMETICS 2021; 16:065001. [PMID: 34384067 DOI: 10.1088/1748-3190/ac1ced] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/12/2021] [Indexed: 06/13/2023]
Abstract
Control of forces is essential in both animals and walking machines. Insects measure forces as strains in their exoskeletons via campaniform sensilla (CS). Deformations of cuticular caps embedded in the exoskeleton excite afferents that project to the central nervous system. CS afferent firing frequency (i.e. 'discharge') is highly dynamic, correlating with the rate of change of the force. Discharges adapt over time to tonic forces and exhibit hysteresis during cyclic loading.In this study we characterized a phenomenological model that predicts CS discharge, in which discharge is proportional to the instantaneous stimulus force relative to an adaptive variable. In contrast to previous studies of sensory adaptation, our model (1) is nonlinear and (2) reproduces the characteristic power-law adaptation with first order dynamics only (i.e. no 'fractional derivatives' are required to explain dynamics). We solve the response of the system analytically in multiple cases and use these solutions to derive the dynamics of the adaptive variable. We show that the model can reproduce responses of insect CS to many different force stimuli after being tuned to reproduce only one response, suggesting that the model captures the underlying dynamics of the system. We show that adaptation to tonic forces, rate-sensitivity, and hysteresis are different manifestations of the same underlying mechanism: the adaptive variable. We tune the model to replicate the dynamics of three different CS groups from two insects (cockroach and stick insect), demonstrating that it is generalizable. We also invert the model to estimate the stimulus force given the discharge recording from the animal. We discuss the adaptive neural and mechanical processes that the model may mimic and the model's use for understanding the role of load feedback in insect motor control. A preliminary model and results were previously published in the proceedings of the Conference on Biohybrid and Biomimetic Systems.
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Affiliation(s)
- Nicholas S Szczecinski
- Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26505, United States of America
| | - Chris J Dallmann
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, United States of America
| | - Roger D Quinn
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, United States of America
| | - Sasha N Zill
- Department of Biomedical Sciences, Joan C Edwards School of Medicine, Marshall University, Huntington, WV 25755, United States of America
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13
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Dallmann CJ, Karashchuk P, Brunton BW, Tuthill JC. A leg to stand on: computational models of proprioception. CURRENT OPINION IN PHYSIOLOGY 2021; 22:100426. [PMID: 34595361 PMCID: PMC8478261 DOI: 10.1016/j.cophys.2021.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Dexterous motor control requires feedback from proprioceptors, internal mechanosensory neurons that sense the body's position and movement. An outstanding question in neuroscience is how diverse proprioceptive feedback signals contribute to flexible motor control. Genetic tools now enable targeted recording and perturbation of proprioceptive neurons in behaving animals; however, these experiments can be challenging to interpret, due to the tight coupling of proprioception and motor control. Here, we argue that understanding the role of proprioceptive feedback in controlling behavior will be aided by the development of multiscale models of sensorimotor loops. We review current phenomenological and structural models for proprioceptor encoding and discuss how they may be integrated with existing models of posture, movement, and body state estimation.
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Affiliation(s)
- Chris J Dallmann
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Pierre Karashchuk
- Neuroscience Graduate Program, University of Washington, Seattle, WA, USA
| | - Bingni W Brunton
- Department of Biology, University of Washington, Seattle, WA, USA
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
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14
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Modeling the Dynamic Sensory Discharges of Insect Campaniform Sensilla. BIOMIMETIC AND BIOHYBRID SYSTEMS 2020. [DOI: 10.1007/978-3-030-64313-3_33] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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