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Li Y, Webster-Wood VA, Gill JP, Sutton GP, Chiel HJ, Quinn RD. A computational neural model that incorporates both intrinsic dynamics and sensory feedback in the Aplysia feeding network. BIOLOGICAL CYBERNETICS 2024:10.1007/s00422-024-00991-2. [PMID: 38769189 DOI: 10.1007/s00422-024-00991-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 05/03/2024] [Indexed: 05/22/2024]
Abstract
Studying the nervous system underlying animal motor control can shed light on how animals can adapt flexibly to a changing environment. We focus on the neural basis of feeding control in Aplysia californica. Using the Synthetic Nervous System framework, we developed a model of Aplysia feeding neural circuitry that balances neurophysiological plausibility and computational complexity. The circuitry includes neurons, synapses, and feedback pathways identified in existing literature. We organized the neurons into three layers and five subnetworks according to their functional roles. Simulation results demonstrate that the circuitry model can capture the intrinsic dynamics at neuronal and network levels. When combined with a simplified peripheral biomechanical model, it is sufficient to mediate three animal-like feeding behaviors (biting, swallowing, and rejection). The kinematic, dynamic, and neural responses of the model also share similar features with animal data. These results emphasize the functional roles of sensory feedback during feeding.
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Affiliation(s)
- Yanjun Li
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
| | - Victoria A Webster-Wood
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
| | - Jeffrey P Gill
- Department of Biology, Case Western Reserve University, 2080 Adelbert Road, Cleveland, OH, 44106, USA
| | - Gregory P Sutton
- Department of Life Sciences, University of Lincoln, Brayford Pool, Lincoln, Lincolnshire, LN6 7TS, UK
| | - Hillel J Chiel
- Department of Biology, Case Western Reserve University, 2080 Adelbert Road, Cleveland, OH, 44106, USA
- Department of Neurosciences, Case Western Reserve University, 2080 Adelbert Road, Cleveland, OH, 44106, USA
- Department of Biomedical Engineering, Case Western Reserve University, 2080 Adelbert Road, Cleveland, OH, 44106, USA
| | - Roger D Quinn
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
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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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Abstract
This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems.
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4
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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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5
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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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Szczecinski NS, Quinn RD, Hunt AJ. Extending the Functional Subnetwork Approach to a Generalized Linear Integrate-and-Fire Neuron Model. Front Neurorobot 2020; 14:577804. [PMID: 33281592 PMCID: PMC7691602 DOI: 10.3389/fnbot.2020.577804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/08/2020] [Indexed: 11/24/2022] Open
Abstract
Engineering neural networks to perform specific tasks often represents a monumental challenge in determining network architecture and parameter values. In this work, we extend our previously-developed method for tuning networks of non-spiking neurons, the “Functional subnetwork approach” (FSA), to the tuning of networks composed of spiking neurons. This extension enables the direct assembly and tuning of networks of spiking neurons and synapses based on the network's intended function, without the use of global optimization or machine learning. To extend the FSA, we show that the dynamics of a generalized linear integrate and fire (GLIF) neuron model have fundamental similarities to those of a non-spiking leaky integrator neuron model. We derive analytical expressions that show functional parallels between: (1) A spiking neuron's steady-state spiking frequency and a non-spiking neuron's steady-state voltage in response to an applied current; (2) a spiking neuron's transient spiking frequency and a non-spiking neuron's transient voltage in response to an applied current; and (3) a spiking synapse's average conductance during steady spiking and a non-spiking synapse's conductance. The models become more similar as additional spiking neurons are added to each population “node” in the network. We apply the FSA to model a neuromuscular reflex pathway two different ways: Via non-spiking components and then via spiking components. These results provide a concrete example of how a single non-spiking neuron may model the average spiking frequency of a population of spiking neurons. The resulting model also demonstrates that by using the FSA, models can be constructed that incorporate both spiking and non-spiking units. This work facilitates the construction of large networks of spiking neurons and synapses that perform specific functions, for example, those implemented with neuromorphic computing hardware, by providing an analytical method for directly tuning their parameters without time-consuming optimization or learning.
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Affiliation(s)
- Nicholas S Szczecinski
- Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, United States
| | - Roger D Quinn
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Alexander J Hunt
- Department of Mechanical and Materials Engineering, Portland State University, Portland, OR, United States
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Webster-Wood VA, Gill JP, Thomas PJ, Chiel HJ. Control for multifunctionality: bioinspired control based on feeding in Aplysia californica. BIOLOGICAL CYBERNETICS 2020; 114:557-588. [PMID: 33301053 PMCID: PMC8543386 DOI: 10.1007/s00422-020-00851-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source of bioinspiration for robotic controllers. However, many existing approaches to modeling biological neural networks rely on computationally expensive models and tend to focus solely on the nervous system, often neglecting the biomechanics of the periphery. As a consequence, while these models are excellent tools for neuroscience, they fail to predict functional behavior in real time, which is a critical capability for robotic control. To meet the need for real-time multifunctional control, we have developed a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. Using this approach, we present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors (biting, swallowing, and rejection), demonstrates behavioral switching in response to external sensory cues, and incorporates both known neural connectivity and a simple bioinspired mechanical model of the feeding apparatus. We demonstrate that the model can be used for formulating testable hypotheses and discuss the implications of this approach for robotic control and neuroscience.
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Affiliation(s)
- Victoria A Webster-Wood
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
- McGowan Institute for Regenerative Medicine, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
| | - Jeffrey P Gill
- Department of Biology, Case Western Reserve University, 2080 Adelbert Road, Cleveland, OH, 44106-7080, USA
| | - Peter J Thomas
- Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-4901, USA
- Department of Biology, Department of Cognitive Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-4901, USA
- Department of Electrical Computer and Systems Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-4901, USA
| | - Hillel J Chiel
- Department of Biology, Case Western Reserve University, 2080 Adelbert Road, Cleveland, OH, 44106-7080, USA
- Department of Neurosciences, Case Western Reserve University, 2080 Adelbert Road, Cleveland, OH, 44106-7080, USA
- Department of Biomedical Engineering, Case Western Reserve University, 2080 Adelbert Road, Cleveland, OH, 44106-7080, USA
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8
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Goldsmith CA, Szczecinski NS, Quinn RD. Neurodynamic modeling of the fruit fly Drosophila melanogaster. BIOINSPIRATION & BIOMIMETICS 2020; 15:065003. [PMID: 32924978 DOI: 10.1088/1748-3190/ab9e52] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This manuscript describes neuromechanical modeling of the fruit fly Drosophila melanogaster in the form of a hexapod robot, Drosophibot, and an accompanying dynamic simulation. Drosophibot is a testbed for real-time dynamical neural controllers modeled after the anatomy and function of the insect nervous system. As such, Drosophibot has been designed to capture features of the animal's biomechanics in order to better test the neural controllers. These features include: dynamically scaling the robot to match the fruit fly by designing its joint elasticity and movement speed; a biomimetic actuator control scheme that converts neural activity into motion in the same way as observed in insects; biomimetic sensing, including proprioception from all leg joints and strain sensing from all leg segments; and passively compliant tarsi that mimic the animal's passive compliance to the walking substrate. We incorporated these features into a dynamical simulation of Drosophibot, and demonstrate that its actuators and sensors perform in an animal-like way. We used this simulation to test a neural walking controller based on anatomical and behavioral data from insects. Finally, we describe Drosophibot's hardware and show that the animal-like features of the simulation transfer to the physical robot.
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Affiliation(s)
- C A Goldsmith
- Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States of America
| | - N S Szczecinski
- Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States of America
| | - R D Quinn
- Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States of America
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Lodi M, Shilnikov AL, Storace M. Design Principles for Central Pattern Generators With Preset Rhythms. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:3658-3669. [PMID: 31722491 DOI: 10.1109/tnnls.2019.2945637] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This article is concerned with the design of synthetic central pattern generators (CPGs). Biological CPGs are neural circuits that determine a variety of rhythmic activities, including locomotion, in animals. A synthetic CPG is a network of dynamical elements (here called cells) properly coupled by various synapses to emulate rhythms produced by a biological CPG. We focus on CPGs for locomotion of quadrupeds and present our design approach, based on the principles of nonlinear dynamics, bifurcation theory, and parameter optimization. This approach lets us design the synthetic CPG with a set of desired rhythms and switch between them as the parameter representing the control actions from the brain is varied. The developed four-cell CPG can produce four distinct gaits: walk, trot, gallop, and bound, similar to the mouse locomotion. The robustness and adaptability of the network design principles are verified using different cell and synapse models.
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Schilling M, Cruse H. Decentralized control of insect walking: A simple neural network explains a wide range of behavioral and neurophysiological results. PLoS Comput Biol 2020; 16:e1007804. [PMID: 32339162 PMCID: PMC7205325 DOI: 10.1371/journal.pcbi.1007804] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 05/07/2020] [Accepted: 03/19/2020] [Indexed: 01/02/2023] Open
Abstract
Controlling the six legs of an insect walking in an unpredictable environment is a challenging task, as many degrees of freedom have to be coordinated. Solutions proposed to deal with this task are usually based on the highly influential concept that (sensory-modulated) central pattern generators (CPG) are required to control the rhythmic movements of walking legs. Here, we investigate a different view. To this end, we introduce a sensor based controller operating on artificial neurons, being applied to a (simulated) insectoid robot required to exploit the "loop through the world" allowing for simplification of neural computation. We show that such a decentralized solution leads to adaptive behavior when facing uncertain environments which we demonstrate for a broad range of behaviors never dealt with in a single system by earlier approaches. This includes the ability to produce footfall patterns such as velocity dependent "tripod", "tetrapod", "pentapod" as well as various stable intermediate patterns as observed in stick insects and in Drosophila. These patterns are found to be stable against disturbances and when starting from various leg configurations. Our neuronal architecture easily allows for starting or interrupting a walk, all being difficult for CPG controlled solutions. Furthermore, negotiation of curves and walking on a treadmill with various treatments of individual legs is possible as well as backward walking and performing short steps. This approach can as well account for the neurophysiological results usually interpreted to support the idea that CPGs form the basis of walking, although our approach is not relying on explicit CPG-like structures. Application of CPGs may however be required for very fast walking. Our neuronal structure allows to pinpoint specific neurons known from various insect studies. Interestingly, specific common properties observed in both insects and crustaceans suggest a significance of our controller beyond the realm of insects.
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Affiliation(s)
- Malte Schilling
- Cluster of Excellence Cognitive Interactive Technology (CITEC), Bielefeld University, Bielefeld, Germany
| | - Holk Cruse
- Cluster of Excellence Cognitive Interactive Technology (CITEC), Bielefeld University, Bielefeld, Germany
- Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany
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11
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Naris M, Szczecinski NS, Quinn RD. A neuromechanical model exploring the role of the common inhibitor motor neuron in insect locomotion. BIOLOGICAL CYBERNETICS 2020; 114:23-41. [PMID: 31788747 DOI: 10.1007/s00422-019-00811-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 11/18/2019] [Indexed: 06/10/2023]
Abstract
In this work, we analyze a simplified, dynamical, closed-loop, neuromechanical simulation of insect joint control. We are specifically interested in two elements: (1) how slow muscle fibers may serve as temporal integrators of sensory feedback and (2) the role of common inhibitory (CI) motor neurons in resetting this integration when the commanded position changes, particularly during steady-state walking. Despite the simplicity of the model, we show that slow muscle fibers increase the accuracy of limb positioning, even for motions much shorter than the relaxation time of the fiber; this increase in accuracy is due to the slow dynamics of the fibers; the CI motor neuron plays a critical role in accelerating muscle relaxation when the limb moves to a new position; as in the animal, this architecture enables the control of the stance phase speed, independent of swing phase amplitude or duration, by changing the gain of sensory feedback to the stance phase muscles. We discuss how this relates to other models, and how it could be applied to robotic control.
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Affiliation(s)
- Mantas Naris
- Bio-Inspired Perception and Robotics Laboratory, University of Colorado Boulder, UCB 427 1111 Engineering Drive, Boulder, CO, 80309, USA.
| | - Nicholas S Szczecinski
- Biologically Inspired Robotics Laboratory, Case Western Reserve University, Glennan 418 10900 Euclid Avenue, Cleveland, OH, 44106, USA
| | - Roger D Quinn
- Biologically Inspired Robotics Laboratory, Case Western Reserve University, Glennan 418 10900 Euclid Avenue, Cleveland, OH, 44106, USA
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12
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Pickard SC, Quinn RD, Szczecinski NS. A dynamical model exploring sensory integration in the insect central complex substructures. BIOINSPIRATION & BIOMIMETICS 2020; 15:026003. [PMID: 31726442 DOI: 10.1088/1748-3190/ab57b6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
It is imperative that an animal has the ability to contextually integrate received sensory information to formulate appropriate behavioral responses. Determining a body heading based on a multitude of ego-motion cues and visual landmarks is an example of such a task that requires this context dependent integration. The work presented here simulates a sensory integrator in the insect brain called the central complex (CX). Based on the architecture of the CX, we assembled a dynamical neural simulation of two structures called the protocerebral bridge (PB) and the ellipsoid body (EB). Using non-spiking neuronal dynamics, our simulation was able to recreate in vivo neuronal behavior such as correlating body rotation direction and speed to activity bumps within the EB as well as updating the believed heading with quick secondary system updates. With this model, we performed sensitivity analysis of certain neuronal parameters as a possible means to control multi-system gains during sensory integration. We found that modulation of synapses in the memory network and EB inhibition are two possible mechanisms in which a sensory system could affect the memory stability and gain of another input, respectively. This model serves as an exploration in network design for integrating simultaneous idiothetic and allothetic cues in the task of body tracking and determining contextually dependent behavioral outputs.
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Affiliation(s)
- S C Pickard
- Author to whom any correspondence should be addressed
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13
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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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14
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Dürr V, Arena PP, Cruse H, Dallmann CJ, Drimus A, Hoinville T, Krause T, Mátéfi-Tempfli S, Paskarbeit J, Patanè L, Schäffersmann M, Schilling M, Schmitz J, Strauss R, Theunissen L, Vitanza A, Schneider A. Integrative Biomimetics of Autonomous Hexapedal Locomotion. Front Neurorobot 2019; 13:88. [PMID: 31708765 PMCID: PMC6819508 DOI: 10.3389/fnbot.2019.00088] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 10/07/2019] [Indexed: 01/31/2023] Open
Abstract
Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots—ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model—we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size.
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Affiliation(s)
- Volker Dürr
- Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany.,Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Paolo P Arena
- DIEEI: Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, Catania, Italy
| | - Holk Cruse
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Chris J Dallmann
- Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany.,Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Alin Drimus
- Mads Clausen Institute, University of Southern Denmark, Sønderborg, Denmark
| | - Thierry Hoinville
- Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany.,Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Tammo Krause
- Institut für Entwicklungsbiologie und Neurobiologie, Johannes Gutenberg-Universität, Mainz, Germany
| | | | - Jan Paskarbeit
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Luca Patanè
- DIEEI: Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, Catania, Italy
| | - Mattias Schäffersmann
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Malte Schilling
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Josef Schmitz
- Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany.,Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Roland Strauss
- Institut für Entwicklungsbiologie und Neurobiologie, Johannes Gutenberg-Universität, Mainz, Germany
| | - Leslie Theunissen
- Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany.,Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany
| | - Alessandra Vitanza
- DIEEI: Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, Catania, Italy
| | - Axel Schneider
- Cognitive Interaction Technology: Center of Excellence, Bielefeld University, Bielefeld, Germany.,Institute of System Dynamics and Mechatronics, Bielefeld University of Applied Sciences, Bielefeld, Germany
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Szczecinski NS, Quinn RD. Leg-local neural mechanisms for searching and learning enhance robotic locomotion. BIOLOGICAL CYBERNETICS 2018; 112:99-112. [PMID: 28782078 DOI: 10.1007/s00422-017-0726-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Accepted: 07/30/2017] [Indexed: 06/07/2023]
Abstract
Adapting motor output based on environmental forces is critical for successful locomotion in the real world. Arthropods use at least two neural mechanisms to adjust muscle activation while walking based on detected forces. Mechanism 1 uses negative feedback of leg depressor force to ensure that each stance leg supports an appropriate amount of the body's weight. Mechanism 2 encourages searching for ground contact if the leg supports no body weight. We expand the neural controller for MantisBot, a robot based upon a praying mantis, to include these mechanisms by incorporating leg-local memory and command neurons, as observed in arthropods. We present results from MantisBot transitioning between searching and stepping, mimicking data from animals as reported in the literature.
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Abstract
The purpose of this work is to better understand how animals control locomotion. This knowledge can then be applied to neuromechanical design to produce more capable and adaptable robot locomotion. To test hypotheses about animal motor control, we model animals and their nervous systems with dynamical simulations, which we call synthetic nervous systems (SNS). However, one major challenge is picking parameter values that produce the intended dynamics. This paper presents a design process that solves this problem without the need for global optimization. We test this method by selecting parameter values for SimRoach2, a dynamical model of a cockroach. Each leg joint is actuated by an antagonistic pair of Hill muscles. A distributed SNS was designed based on pathways known to exist in insects, as well as hypothetical pathways that produced insect-like motion. Each joint’s controller was designed to function as a proportional-integral (PI) feedback loop and tuned with numerical optimization. Once tuned, SimRoach2 walks through a simulated environment, with several cockroach-like features. A model with such reliable low-level performance is necessary to investigate more sophisticated locomotion patterns in the future.
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