A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control

Silvia Tolu, Marie Claire Capolei, Lorenzo Vannucci, Cecilia Laschi, Egidio Falotico, Mauricio Vanegas Hernandez

Research output: Contribution to journalJournal articleResearchpeer-review

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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
Original languageEnglish
Article number1950028
JournalInternational Journal of Neural Systems
Volume30
Issue number1
Number of pages16
ISSN0129-0657
DOIs
Publication statusPublished - 2020

Keywords

  • Internal forward model
  • Cerebellum
  • Motor control
  • Adaptive learning
  • Smith predictor
  • Bioinspired
  • Recurrent

Cite this

Tolu, Silvia ; Capolei, Marie Claire ; Vannucci, Lorenzo ; Laschi, Cecilia ; Falotico, Egidio ; Hernandez, Mauricio Vanegas. / A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control. In: International Journal of Neural Systems. 2020 ; Vol. 30, No. 1.
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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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A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control. / Tolu, Silvia; Capolei, Marie Claire; Vannucci, Lorenzo; Laschi, Cecilia; Falotico, Egidio; Hernandez, Mauricio Vanegas.

In: International Journal of Neural Systems, Vol. 30, No. 1, 1950028 , 2020.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control

AU - Tolu, Silvia

AU - Capolei, Marie Claire

AU - Vannucci, Lorenzo

AU - Laschi, Cecilia

AU - Falotico, Egidio

AU - Hernandez, Mauricio Vanegas

PY - 2020

Y1 - 2020

N2 - 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

AB - 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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KW - Cerebellum

KW - Motor control

KW - Adaptive learning

KW - Smith predictor

KW - Bioinspired

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