1
|
Aldarondo D, Merel J, Marshall JD, Hasenclever L, Klibaite U, Gellis A, Tassa Y, Wayne G, Botvinick M, Ölveczky BP. A virtual rodent predicts the structure of neural activity across behaviours. Nature 2024:10.1038/s41586-024-07633-4. [PMID: 38862024 DOI: 10.1038/s41586-024-07633-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/30/2024] [Indexed: 06/13/2024]
Abstract
Animals have exquisite control of their bodies, allowing them to perform a diverse range of behaviours. How such control is implemented by the brain, however, remains unclear. Advancing our understanding requires models that can relate principles of control to the structure of neural activity in behaving animals. Here, to facilitate this, we built a 'virtual rodent', in which an artificial neural network actuates a biomechanically realistic model of the rat1 in a physics simulator2. We used deep reinforcement learning3-5 to train the virtual agent to imitate the behaviour of freely moving rats, thus allowing us to compare neural activity recorded in real rats to the network activity of a virtual rodent mimicking their behaviour. We found that neural activity in the sensorimotor striatum and motor cortex was better predicted by the virtual rodent's network activity than by any features of the real rat's movements, consistent with both regions implementing inverse dynamics6. Furthermore, the network's latent variability predicted the structure of neural variability across behaviours and afforded robustness in a way consistent with the minimal intervention principle of optimal feedback control7. These results demonstrate how physical simulation of biomechanically realistic virtual animals can help interpret the structure of neural activity across behaviour and relate it to theoretical principles of motor control.
Collapse
Affiliation(s)
- Diego Aldarondo
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
- Fauna Robotics, New York, NY, USA.
| | - Josh Merel
- DeepMind, Google, London, UK
- Fauna Robotics, New York, NY, USA
| | - Jesse D Marshall
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Reality Labs, Meta, New York, NY, USA
| | | | - Ugne Klibaite
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Amanda Gellis
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | | | | | - Matthew Botvinick
- DeepMind, Google, London, UK
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Bence P Ölveczky
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA.
| |
Collapse
|
2
|
Ijspeert AJ, Daley MA. Integration of feedforward and feedback control in the neuromechanics of vertebrate locomotion: a review of experimental, simulation and robotic studies. J Exp Biol 2023; 226:jeb245784. [PMID: 37565347 DOI: 10.1242/jeb.245784] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Animal locomotion is the result of complex and multi-layered interactions between the nervous system, the musculo-skeletal system and the environment. Decoding the underlying mechanisms requires an integrative approach. Comparative experimental biology has allowed researchers to study the underlying components and some of their interactions across diverse animals. These studies have shown that locomotor neural circuits are distributed in the spinal cord, the midbrain and higher brain regions in vertebrates. The spinal cord plays a key role in locomotor control because it contains central pattern generators (CPGs) - systems of coupled neuronal oscillators that provide coordinated rhythmic control of muscle activation that can be viewed as feedforward controllers - and multiple reflex loops that provide feedback mechanisms. These circuits are activated and modulated by descending pathways from the brain. The relative contributions of CPGs, feedback loops and descending modulation, and how these vary between species and locomotor conditions, remain poorly understood. Robots and neuromechanical simulations can complement experimental approaches by testing specific hypotheses and performing what-if scenarios. This Review will give an overview of key knowledge gained from comparative vertebrate experiments, and insights obtained from neuromechanical simulations and robotic approaches. We suggest that the roles of CPGs, feedback loops and descending modulation vary among animals depending on body size, intrinsic mechanical stability, time required to reach locomotor maturity and speed effects. We also hypothesize that distal joints rely more on feedback control compared with proximal joints. Finally, we highlight important opportunities to address fundamental biological questions through continued collaboration between experimentalists and engineers.
Collapse
Affiliation(s)
- Auke J Ijspeert
- BioRobotics Laboratory, EPFL - Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Monica A Daley
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA
| |
Collapse
|
3
|
Dubuc R, Cabelguen JM, Ryczko D. Locomotor pattern generation and descending control: a historical perspective. J Neurophysiol 2023; 130:401-416. [PMID: 37465884 DOI: 10.1152/jn.00204.2023] [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: 05/19/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 07/20/2023] Open
Abstract
The ability to generate and control locomotor movements depends on complex interactions between many areas of the nervous system, the musculoskeletal system, and the environment. How the nervous system manages to accomplish this task has been the subject of investigation for more than a century. In vertebrates, locomotion is generated by neural networks located in the spinal cord referred to as central pattern generators. Descending inputs from the brain stem initiate, maintain, and stop locomotion as well as control speed and direction. Sensory inputs adapt locomotor programs to the environmental conditions. This review presents a comparative and historical overview of some of the neural mechanisms underlying the control of locomotion in vertebrates. We have put an emphasis on spinal mechanisms and descending control.
Collapse
Affiliation(s)
- Réjean Dubuc
- Groupe de Recherche en Activité Physique Adaptée, Département des Sciences de l'Activité Physique, Université du Québec à Montréal, Montreal, Quebec, Canada
- Groupe de Recherche sur le Système Nerveux Central, Département de Neurosciences, Université de Montréal, Montreal, Quebec, Canada
| | - Jean-Marie Cabelguen
- Institut National de la Santé et de la Recherche Médicale (INSERM) U 1215-Neurocentre Magendie, Université de Bordeaux, Bordeaux Cedex, France
| | - Dimitri Ryczko
- Département de Pharmacologie-Physiologie, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Centre de recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada
- Neurosciences Sherbrooke, Sherbrooke, Quebec, Canada
- Institut de Pharmacologie de Sherbrooke, Sherbrooke, Quebec, Canada
| |
Collapse
|
4
|
Hou J, Chai H, Li Y, Xin Y, Chen W. A heuristic control framework for heavy‐duty hexapod robot over complex terrain. IET CYBER-SYSTEMS AND ROBOTICS 2022. [DOI: 10.1049/csy2.12064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Jinmian Hou
- School of Control Science and Engineering Shandong University Jinan China
- Engineering Research Center of Intelligent Unmanned System Ministry of Education Shandong University Jinan China
| | - Hui Chai
- School of Control Science and Engineering Shandong University Jinan China
- Engineering Research Center of Intelligent Unmanned System Ministry of Education Shandong University Jinan China
| | - Yibin Li
- School of Control Science and Engineering Shandong University Jinan China
- Engineering Research Center of Intelligent Unmanned System Ministry of Education Shandong University Jinan China
| | - Yaxian Xin
- School of Control Science and Engineering Shandong University Jinan China
- Engineering Research Center of Intelligent Unmanned System Ministry of Education Shandong University Jinan China
| | - Wei Chen
- School of Control Science and Engineering Shandong University Jinan China
- Engineering Research Center of Intelligent Unmanned System Ministry of Education Shandong University Jinan China
| |
Collapse
|
5
|
Yan Z, Yang H, Zhang W, Gong Q, Lin F, Zhang Y. Bionic Fish Trajectory Tracking Based on a CPG and Model Predictive Control. J INTELL ROBOT SYST 2022. [DOI: 10.1007/s10846-022-01644-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
6
|
Chen Y, Grezmak JE, Graf NM, Daltorio KA. Sideways crab-walking is faster and more efficient than forward walking for a hexapod robot. BIOINSPIRATION & BIOMIMETICS 2022; 17:046001. [PMID: 35439747 DOI: 10.1088/1748-3190/ac6847] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
Articulated legs enable the selection of robot gaits, including walking in different directions such as forward or sideways. For longer distances, the best gaits might maximize velocity or minimize the cost of transport (COT). While animals often have morphology suited to walking either forward (like insects) or sideways (like crabs), hexapod robots often default to forward walking. In this paper, we compare forward walking with crab-like sideways walking. To do this, a simple gait design method is introduced for determining forward and sideways gaits with equivalent body heights and step heights. Specifically, the frequency and stride lengths are tuned within reasonable constraints to find gaits that represent a robot's performance potential in terms of speed and energy cost. Experiments are performed in both dynamic simulation in Webots and a laboratory environment with our 18 degree-of-freedom hexapod robot, Sebastian. With the common three joint leg design, the results show that sideways walking is overall better (75% greater walking speed and 40% lower COT). The performance of sideways walking was better on both hard floors and granular media (dry play sand). This supports development of future crab-like walking robots for future applications. In future work, this approach may be used to develop nominal gaits without extensive optimization, and to explore whether the advantages of sideways walking persist for other hexapod designs.
Collapse
Affiliation(s)
- Yang Chen
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, United States of America
| | - John E Grezmak
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, United States of America
| | - Nicole M Graf
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, United States of America
| | - Kathryn A Daltorio
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, United States of America
| |
Collapse
|
7
|
Owaki D, Manoonpong P, Ayali A. Editorial: Biological and Robotic Inter-Limb Coordination. Front Robot AI 2022; 9:875493. [PMID: 35391940 PMCID: PMC8981463 DOI: 10.3389/frobt.2022.875493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 02/22/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Dai Owaki
- Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan
- *Correspondence: Dai Owaki,
| | - Poramate Manoonpong
- Embodied AI and Neurorobotics Lab., SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark
- Bio-inspired Robotics and Neural Engineering Lab., School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Amir Ayali
- School of Zoology, Faculty of Life Sciences, and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
8
|
Course Control of a Manta Robot Based on Amplitude and Phase Differences. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2022. [DOI: 10.3390/jmse10020285] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Due to external interference, such as waves, the success of underwater missions depends on the turning performance of the vehicle. Manta rays use two broad pectoral fins for propulsion, which provide better anti-interference ability and turning performance. Inspired by biological yaw modes, we use the phase difference between the pectoral fins to realize fast course adjustment and the amplitude difference to realize precise adjustment. We design a bionic robot with pectoral fins and use phase oscillators to realize rhythmic motion. An expected phase difference transition equation is introduced to realize a fast and smooth transition of the output, and the parameters are adjusted online. We combine the phase difference and amplitude difference yaw modes to realize closed-loop course control. Through course interference and adjustment experiments, it is verified that the combined mode is more effective than a single mode. Finally, a rectangular trajectory swimming experiment demonstrates continuous mobility of the robot under the combined mode.
Collapse
|
9
|
Abstract
When animals walk overground, mechanical stimuli activate various receptors located in muscles, joints, and skin. Afferents from these mechanoreceptors project to neuronal networks controlling locomotion in the spinal cord and brain. The dynamic interactions between the control systems at different levels of the neuraxis ensure that locomotion adjusts to its environment and meets task demands. In this article, we describe and discuss the essential contribution of somatosensory feedback to locomotion. We start with a discussion of how biomechanical properties of the body affect somatosensory feedback. We follow with the different types of mechanoreceptors and somatosensory afferents and their activity during locomotion. We then describe central projections to locomotor networks and the modulation of somatosensory feedback during locomotion and its mechanisms. We then discuss experimental approaches and animal models used to investigate the control of locomotion by somatosensory feedback before providing an overview of the different functional roles of somatosensory feedback for locomotion. Lastly, we briefly describe the role of somatosensory feedback in the recovery of locomotion after neurological injury. We highlight the fact that somatosensory feedback is an essential component of a highly integrated system for locomotor control. © 2021 American Physiological Society. Compr Physiol 11:1-71, 2021.
Collapse
Affiliation(s)
- Alain Frigon
- Department of Pharmacology-Physiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Quebec, Canada
| | - Turgay Akay
- Department of Medical Neuroscience, Atlantic Mobility Action Project, Brain Repair Center, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Boris I Prilutsky
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| |
Collapse
|
10
|
Srisuchinnawong A, Homchanthanakul J, Manoonpong P. NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems. Front Neural Circuits 2021; 15:743101. [PMID: 35027885 PMCID: PMC8751631 DOI: 10.3389/fncir.2021.743101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 11/29/2021] [Indexed: 11/13/2022] Open
Abstract
Understanding the real-time dynamical mechanisms of neural systems remains a significant issue, preventing the development of efficient neural technology and user trust. This is because the mechanisms, involving various neural spatial-temporal ingredients [i.e., neural structure (NS), neural dynamics (ND), neural plasticity (NP), and neural memory (NM)], are too complex to interpret and analyze altogether. While advanced tools have been developed using explainable artificial intelligence (XAI), node-link diagram, topography map, and other visualization techniques, they still fail to monitor and visualize all of these neural ingredients online. Accordingly, we propose here for the first time "NeuroVis," real-time neural spatial-temporal information measurement and visualization, as a method/tool to measure temporal neural activities and their propagation throughout the network. By using this neural information along with the connection strength and plasticity, NeuroVis can visualize the NS, ND, NM, and NP via i) spatial 2D position and connection, ii) temporal color gradient, iii) connection thickness, and iv) temporal luminous intensity and change of connection thickness, respectively. This study presents three use cases of NeuroVis to evaluate its performance: i) function approximation using a modular neural network with recurrent and feedforward topologies together with supervised learning, ii) robot locomotion control and learning using the same modular network with reinforcement learning, and iii) robot locomotion control and adaptation using another larger-scale adaptive modular neural network. The use cases demonstrate how NeuroVis tracks and analyzes all neural ingredients of various (embodied) neural systems in real-time under the robot operating system (ROS) framework. To this end, it will offer the opportunity to better understand embodied dynamic neural information processes, boost efficient neural technology development, and enhance user trust.
Collapse
Affiliation(s)
- Arthicha Srisuchinnawong
- Bio-inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
- Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark
| | - Jettanan Homchanthanakul
- Bio-inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Poramate Manoonpong
- Bio-inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
- Embodied Artificial Intelligence and Neurorobotics Laboratory, SDU Biorobotics, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark
| |
Collapse
|
11
|
Iyer AA, Briggman KL. Amphibian behavioral diversity offers insights into evolutionary neurobiology. Curr Opin Neurobiol 2021; 71:19-28. [PMID: 34481981 DOI: 10.1016/j.conb.2021.07.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/16/2021] [Accepted: 07/27/2021] [Indexed: 11/18/2022]
Abstract
Recent studies have served to emphasize the unique placement of amphibians, composed of more than 8000 species, in the evolution of the brain. We provide an overview of the three amphibian orders and their respective ecologies, behaviors, and brain anatomy. Studies have probed the origins of independently evolved parental care strategies in frogs and the biophysical principles driving species-specific differences in courtship vocalization patterns. Amphibians are also important models for studying the central control of movement, especially in the context of the vertebrate origin of limb-based locomotion. By highlighting the versatility of amphibians, we hope to see a further adoption of anurans, urodeles, and gymnophionans as model systems for the evolution and neural basis of behavior across vertebrates.
Collapse
Affiliation(s)
- Aditya A Iyer
- Center of Advanced European Studies and Research (Caesar), Ludwig-Erhard-Allee 2, Bonn, Germany
| | - Kevin L Briggman
- Center of Advanced European Studies and Research (Caesar), Ludwig-Erhard-Allee 2, Bonn, Germany.
| |
Collapse
|