Integration of Paired Spiking Cerebellar Models for Voluntary Movement Adaptation in a Closed-Loop Neuro-Robotic Experiment. A Simulation Study

Carlos Corchado, Alberto Antonietti, Marie Claire Capolei, Claudia Casellato, Silvia Tolu

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Abstract

Motor control is a very important feature in the human brain to achieve optimal performance in motor tasks. The biological basis of this feature can be better understood
by emulating the cerebellar mechanisms of learning. The cerebellum plays a key role in implementing fine motor control, since it extracts the information about movements from sensorymotor signals, stores it by means of internal models and uses them to adapt to the environment. The hypothesis is that different internal models could work both independently and dependently. So far, there have been a few studies that aimed to prove their dependency; however, this hypothesis has not been widely used in robot control. The purpose of this work is to build paired spiking cerebellar models and to incorporate them into a biologically plausible composite robotic control architecture for movement adaptation. This is achieved by combining feedback error learning and cerebellar internal models theories. Thus the control architecture is composed of cerebellar feed-forward and recurrent loops for torque-based control of a robot. The spiking cerebellar models are able to correct and improve the performance of the two-degrees of freedom robot module Fable by providing both adaptive torque corrections and sensory corrections to the reference generated by the trajectory planner. Simulations are carried out in the Neurorobotics platform of the Human Brain Project. Results show that the contribution provided by cerebellar learning
leads to an optimization of the performance with errors being reduced by 30% compared with the case where the cerebellar contribution is not applied.
Original languageEnglish
Title of host publicationProceedings of 2019 IEEE International Conference on Cyborg and Bionic Systems and HBP Workshop
Number of pages6
PublisherIEEE
Publication statusAccepted/In press - 2019
Event2019 IEEE International Conference on Cyborg and Bionic Systems
- TonHalle München, Munich, Germany
Duration: 18 Sep 201920 Sep 2019
http://cbs2019.com/

Conference

Conference2019 IEEE International Conference on Cyborg and Bionic Systems
LocationTonHalle München
CountryGermany
CityMunich
Period18/09/201920/09/2019
Internet address

Cite this

Corchado, C., Antonietti, A., Capolei, M. C., Casellato, C., & Tolu, S. (Accepted/In press). Integration of Paired Spiking Cerebellar Models for Voluntary Movement Adaptation in a Closed-Loop Neuro-Robotic Experiment. A Simulation Study. In Proceedings of 2019 IEEE International Conference on Cyborg and Bionic Systems and HBP Workshop IEEE.
Corchado, Carlos ; Antonietti, Alberto ; Capolei, Marie Claire ; Casellato, Claudia ; Tolu, Silvia. / Integration of Paired Spiking Cerebellar Models for Voluntary Movement Adaptation in a Closed-Loop Neuro-Robotic Experiment. A Simulation Study. Proceedings of 2019 IEEE International Conference on Cyborg and Bionic Systems and HBP Workshop. IEEE, 2019.
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title = "Integration of Paired Spiking Cerebellar Models for Voluntary Movement Adaptation in a Closed-Loop Neuro-Robotic Experiment. A Simulation Study",
abstract = "Motor control is a very important feature in the human brain to achieve optimal performance in motor tasks. The biological basis of this feature can be better understoodby emulating the cerebellar mechanisms of learning. The cerebellum plays a key role in implementing fine motor control, since it extracts the information about movements from sensorymotor signals, stores it by means of internal models and uses them to adapt to the environment. The hypothesis is that different internal models could work both independently and dependently. So far, there have been a few studies that aimed to prove their dependency; however, this hypothesis has not been widely used in robot control. The purpose of this work is to build paired spiking cerebellar models and to incorporate them into a biologically plausible composite robotic control architecture for movement adaptation. This is achieved by combining feedback error learning and cerebellar internal models theories. Thus the control architecture is composed of cerebellar feed-forward and recurrent loops for torque-based control of a robot. The spiking cerebellar models are able to correct and improve the performance of the two-degrees of freedom robot module Fable by providing both adaptive torque corrections and sensory corrections to the reference generated by the trajectory planner. Simulations are carried out in the Neurorobotics platform of the Human Brain Project. Results show that the contribution provided by cerebellar learningleads to an optimization of the performance with errors being reduced by 30{\%} compared with the case where the cerebellar contribution is not applied.",
author = "Carlos Corchado and Alberto Antonietti and Capolei, {Marie Claire} and Claudia Casellato and Silvia Tolu",
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Corchado, C, Antonietti, A, Capolei, MC, Casellato, C & Tolu, S 2019, Integration of Paired Spiking Cerebellar Models for Voluntary Movement Adaptation in a Closed-Loop Neuro-Robotic Experiment. A Simulation Study. in Proceedings of 2019 IEEE International Conference on Cyborg and Bionic Systems and HBP Workshop. IEEE, 2019 IEEE International Conference on Cyborg and Bionic Systems
, Munich, Germany, 18/09/2019.

Integration of Paired Spiking Cerebellar Models for Voluntary Movement Adaptation in a Closed-Loop Neuro-Robotic Experiment. A Simulation Study. / Corchado, Carlos; Antonietti, Alberto; Capolei, Marie Claire; Casellato, Claudia; Tolu, Silvia.

Proceedings of 2019 IEEE International Conference on Cyborg and Bionic Systems and HBP Workshop. IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - Integration of Paired Spiking Cerebellar Models for Voluntary Movement Adaptation in a Closed-Loop Neuro-Robotic Experiment. A Simulation Study

AU - Corchado, Carlos

AU - Antonietti, Alberto

AU - Capolei, Marie Claire

AU - Casellato, Claudia

AU - Tolu, Silvia

PY - 2019

Y1 - 2019

N2 - Motor control is a very important feature in the human brain to achieve optimal performance in motor tasks. The biological basis of this feature can be better understoodby emulating the cerebellar mechanisms of learning. The cerebellum plays a key role in implementing fine motor control, since it extracts the information about movements from sensorymotor signals, stores it by means of internal models and uses them to adapt to the environment. The hypothesis is that different internal models could work both independently and dependently. So far, there have been a few studies that aimed to prove their dependency; however, this hypothesis has not been widely used in robot control. The purpose of this work is to build paired spiking cerebellar models and to incorporate them into a biologically plausible composite robotic control architecture for movement adaptation. This is achieved by combining feedback error learning and cerebellar internal models theories. Thus the control architecture is composed of cerebellar feed-forward and recurrent loops for torque-based control of a robot. The spiking cerebellar models are able to correct and improve the performance of the two-degrees of freedom robot module Fable by providing both adaptive torque corrections and sensory corrections to the reference generated by the trajectory planner. Simulations are carried out in the Neurorobotics platform of the Human Brain Project. Results show that the contribution provided by cerebellar learningleads to an optimization of the performance with errors being reduced by 30% compared with the case where the cerebellar contribution is not applied.

AB - Motor control is a very important feature in the human brain to achieve optimal performance in motor tasks. The biological basis of this feature can be better understoodby emulating the cerebellar mechanisms of learning. The cerebellum plays a key role in implementing fine motor control, since it extracts the information about movements from sensorymotor signals, stores it by means of internal models and uses them to adapt to the environment. The hypothesis is that different internal models could work both independently and dependently. So far, there have been a few studies that aimed to prove their dependency; however, this hypothesis has not been widely used in robot control. The purpose of this work is to build paired spiking cerebellar models and to incorporate them into a biologically plausible composite robotic control architecture for movement adaptation. This is achieved by combining feedback error learning and cerebellar internal models theories. Thus the control architecture is composed of cerebellar feed-forward and recurrent loops for torque-based control of a robot. The spiking cerebellar models are able to correct and improve the performance of the two-degrees of freedom robot module Fable by providing both adaptive torque corrections and sensory corrections to the reference generated by the trajectory planner. Simulations are carried out in the Neurorobotics platform of the Human Brain Project. Results show that the contribution provided by cerebellar learningleads to an optimization of the performance with errors being reduced by 30% compared with the case where the cerebellar contribution is not applied.

M3 - Article in proceedings

BT - Proceedings of 2019 IEEE International Conference on Cyborg and Bionic Systems and HBP Workshop

PB - IEEE

ER -

Corchado C, Antonietti A, Capolei MC, Casellato C, Tolu S. Integration of Paired Spiking Cerebellar Models for Voluntary Movement Adaptation in a Closed-Loop Neuro-Robotic Experiment. A Simulation Study. In Proceedings of 2019 IEEE International Conference on Cyborg and Bionic Systems and HBP Workshop. IEEE. 2019