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Warchoł J, Tetych A, Tomaszewski R, Kowalczyk B, Olchowik G. Virtual Reality-Induced Modification of Vestibulo-Ocular Reflex Gain in Posturography Tests. J Clin Med 2024; 13:2742. [PMID: 38792284 PMCID: PMC11122614 DOI: 10.3390/jcm13102742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/03/2024] [Accepted: 05/05/2024] [Indexed: 05/26/2024] Open
Abstract
Background: The aim of the study was to demonstrate the influence of virtual reality (VR) exposure on postural stability and determine the mechanism of this influence. Methods: Twenty-six male participants aged 21-23 years were included, who underwent postural stability assessment twice before and after a few minute of single VR exposure. The VR projection was a computer-generated simulation of the surrounding scenery. Postural stability was assessed using the Sensory Organization Test (SOT), using Computerized Dynamic Posturography (CDP). Results: The findings indicated that VR exposure affects the visual and vestibular systems. Significant differences (p < 0.05) in results before and after VR exposure were observed in tests on an unstable surface. It was confirmed that VR exposure has a positive influence on postural stability, attributed to an increase in the sensory weight of the vestibular system. Partial evidence suggested that the reduction in vestibulo-ocular reflex (VOR) reinforcement may result in an adaptive shift to the optokinetic reflex (OKR). Conclusions: By modifying the process of environmental perception through artificial sensory simulation, the influence of VR on postural stability has been demonstrated. The validity of this type of research is determined by the effectiveness of VR techniques in the field of vestibular rehabilitation.
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Affiliation(s)
- Jan Warchoł
- Department of Biophysics, Medical University of Lublin, K. Jaczewskiego 4, 20-090 Lublin, Poland; (A.T.); (B.K.); (G.O.)
| | - Anna Tetych
- Department of Biophysics, Medical University of Lublin, K. Jaczewskiego 4, 20-090 Lublin, Poland; (A.T.); (B.K.); (G.O.)
| | - Robert Tomaszewski
- Department of Computer Science, University of Applied Sciences in Biala Podlaska, Sidorska 95/97, 21-500 Biala Podlaska, Poland;
| | - Bartłomiej Kowalczyk
- Department of Biophysics, Medical University of Lublin, K. Jaczewskiego 4, 20-090 Lublin, Poland; (A.T.); (B.K.); (G.O.)
| | - Grażyna Olchowik
- Department of Biophysics, Medical University of Lublin, K. Jaczewskiego 4, 20-090 Lublin, Poland; (A.T.); (B.K.); (G.O.)
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Antonietti A, Geminiani A, Negri E, D'Angelo E, Casellato C, Pedrocchi A. Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System. Front Neurorobot 2022; 16:817948. [PMID: 35770277 PMCID: PMC9234954 DOI: 10.3389/fnbot.2022.817948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model for studying active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modeling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Human Brain Project's Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was adequately connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behavior experimentally recorded in mice.
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Affiliation(s)
- Alberto Antonietti
- Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Nearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- *Correspondence: Alberto Antonietti
| | - Alice Geminiani
- Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Edoardo Negri
- Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Nearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Egidio D'Angelo
- Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Claudia Casellato
- Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Alessandra Pedrocchi
- Nearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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Zahra O, Tolu S, Navarro-Alarcon D. Differential mapping spiking neural network for sensor-based robot control. BIOINSPIRATION & BIOMIMETICS 2021; 16:036008. [PMID: 33706302 DOI: 10.1088/1748-3190/abedce] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
In this work, a spiking neural network (SNN) is proposed for approximating differential sensorimotor maps of robotic systems. The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor space. The SNN consists of an input (sensory) layer and an output (motor) layer connected through plastic synapses, with inter-inhibitory connections at the output layer. Spiking neurons are modeled as Izhikevich neurons with a synaptic learning rule based on spike timing-dependent plasticity. Feedback data from proprioceptive and exteroceptive sensors are encoded and fed into the input layer through a motor babbling process. A guideline for tuning the network parameters is proposed and applied along with the particle swarm optimization technique. Our proposed control architecture takes advantage of biologically plausible tools of an SNN to achieve the target reaching task while minimizing deviations from the desired path, and consequently minimizing the execution time. Thanks to the chosen architecture and optimization of the parameters, the number of neurons and the amount of data required for training are considerably low. The SNN is capable of handling noisy sensor readings to guide the robot movements in real-time. Experimental results are presented to validate the control methodology with a vision-guided robot.
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Affiliation(s)
- Omar Zahra
- The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China
| | | | - David Navarro-Alarcon
- The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China
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Wilson ED, Assaf T, Rossiter JM, Dean P, Porrill J, Anderson SR, Pearson MJ. A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing. J R Soc Interface 2021; 18:20200750. [PMID: 33499769 DOI: 10.1098/rsif.2020.0750] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The cerebellum is a neural structure essential for learning, which is connected via multiple zones to many different regions of the brain, and is thought to improve human performance in a large range of sensory, motor and even cognitive processing tasks. An intriguing possibility for the control of complex robotic systems would be to develop an artificial cerebellar chip with multiple zones that could be similarly connected to a variety of subsystems to optimize performance. The novel aim of this paper, therefore, is to propose and investigate a multizone cerebellar chip applied to a range of tasks in robot adaptive control and sensorimotor processing. The multizone cerebellar chip was evaluated using a custom robotic platform consisting of an array of tactile sensors driven by dielectric electroactive polymers mounted upon a standard industrial robot arm. The results demonstrate that the performance in each task was improved by the concurrent, stable learning in each cerebellar zone. This paper, therefore, provides the first empirical demonstration that a synthetic, multizone, cerebellar chip could be embodied within existing robotic systems to improve performance in a diverse range of tasks, much like the cerebellum in a biological system.
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Affiliation(s)
- Emma D Wilson
- Lancaster University, School of Computing and Communications, Lancaster, UK
| | - Tareq Assaf
- University of Bath, Department of Electronic and Electrical Engineering, Bath, UK
| | | | - Paul Dean
- University of Sheffield, Department of Psychology, Sheffield, UK
| | - John Porrill
- University of Sheffield, Department of Psychology, Sheffield, UK
| | - Sean R Anderson
- University of Sheffield, Department of Automatic Control and Systems Engineering, Sheffield, UK
| | - Martin J Pearson
- University of the West of England, Bristol Robotics Laboratory, Bristol, UK
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Capolei MC, Andersen NA, Lund HH, Falotico E, Tolu S. A Cerebellar Internal Models Control Architecture for Online Sensorimotor Adaptation of a Humanoid Robot Acting in a Dynamic Environment. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2019.2943818] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Tolu S, Capolei MC, Vannucci L, Laschi C, Falotico E, Hernández MV. A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control. Int J Neural Syst 2019; 30:1950028. [PMID: 31771377 DOI: 10.1142/s012906571950028x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The cerebellum, which is responsible for motor control and learning, has been suggested to act as a Smith predictor for compensation of time-delays by means of internal forward models. However, insights about how forward model predictions are integrated in the Smith predictor have not yet been unveiled. To fill this gap, a novel bio-inspired modular control architecture that merges a recurrent cerebellar-like loop for adaptive control and a Smith predictor controller is proposed. The goal is to provide accurate anticipatory corrections to the generation of the motor commands in spite of sensory delays and to validate the robustness of the proposed control method to input and physical dynamic changes. The outcome of the proposed architecture with other two control schemes that do not include the Smith control strategy or the cerebellar-like corrections are compared. The results obtained on four sets of experiments confirm that the cerebellum-like circuit provides more effective corrections when only the Smith strategy is adopted and that minor tuning in the parameters, fast adaptation and reproducible configuration are enabled.
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Affiliation(s)
- Silvia Tolu
- Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Richard Petersens Plads, Building 326, Kgs. Lyngby, 2800, Denmark
| | - Marie Claire Capolei
- Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Richard Petersens Plads, Building 326, Kgs. Lyngby, 2800, Denmark
| | - Lorenzo Vannucci
- The BioRobotics Institute, Scuola Superiore SantAnna, Viale Rinaldo Piaggio 34, Pontedera, 56025, Pisa, Italy
| | - Cecilia Laschi
- The BioRobotics Institute, Scuola Superiore SantAnna, Viale Rinaldo Piaggio 34, Pontedera, 56025, Pisa, Italy
| | - Egidio Falotico
- The BioRobotics Institute, Scuola Superiore SantAnna, Viale Rinaldo Piaggio 34, Pontedera, 56025, Pisa, Italy
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Massi E, Vannucci L, Albanese U, Capolei MC, Vandesompele A, Urbain G, Sabatini AM, Dambre J, Laschi C, Tolu S, Falotico E. Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion. Front Neurorobot 2019; 13:71. [PMID: 31555118 PMCID: PMC6727738 DOI: 10.3389/fnbot.2019.00071] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 08/14/2019] [Indexed: 11/13/2022] Open
Abstract
In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network. On top of this, fine motor control is learned during the lifetime of the animal thanks to the plastic connections of the cerebellum that provide descending corrective inputs. This research aims at understanding and identifying the possible advantages of using learning during an evolution-inspired optimization for finding the best locomotion patterns in a robotic locomotion task. Accordingly, we propose a comparative study between two bio-inspired control architectures for quadruped legged robots where learning takes place either during the evolutionary search or only after that. The evolutionary process is carried out in a simulated environment, on a quadruped legged robot. To verify the possibility of overcoming the reality gap, the performance of both systems has been analyzed by changing the robot dynamics and its interaction with the external environment. Results show better performance metrics for the robotic agent whose locomotion method has been discovered by applying the adaptive module during the evolutionary exploration for the locomotion trajectories. Even when the motion dynamics and the interaction with the environment is altered, the locomotion patterns found on the learning robotic system are more stable, both in the joint and in the task space.
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Affiliation(s)
- Elisa Massi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Lorenzo Vannucci
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Ugo Albanese
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Marie Claire Capolei
- Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Copenhagen, Denmark
| | - Alexander Vandesompele
- AIRO, Electronics and Information Systems Department, Ghent University - imec, Ghent, Belgium
| | - Gabriel Urbain
- AIRO, Electronics and Information Systems Department, Ghent University - imec, Ghent, Belgium
| | - Angelo Maria Sabatini
- Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Copenhagen, Denmark
| | - Joni Dambre
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Cecilia Laschi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Silvia Tolu
- Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Copenhagen, Denmark
| | - Egidio Falotico
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
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Falotico E, Berthoz A, Dario P, Laschi C. Sense of movement: Simplifying principles for humanoid robots. Sci Robot 2017; 2:2/13/eaaq0882. [PMID: 33157877 DOI: 10.1126/scirobotics.aaq0882] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 12/04/2017] [Indexed: 11/02/2022]
Abstract
Brain simplifying principles can improve robot capabilities, but currently robotic control takes different paths.
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Affiliation(s)
- Egidio Falotico
- BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
| | | | - Paolo Dario
- BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Cecilia Laschi
- BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
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