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A bi-hemispheric neuronal network model of the cerebellum with spontaneous climbing fiber firing produces asymmetrical motor learning during robot control.

Pinzon-Morales RD, Hirata Y - Front Neural Circuits (2014)

Bottom Line: The bi-hemispheric structure is inspired by the observation that lesioning one hemisphere reduces motor performance asymmetrically.Our results showed that asymmetrical conditions were successfully handled by the biCNN model, in contrast to a single hemisphere model or a classical non-adaptive proportional and derivative controller.Thus, we conclude that a bi-hemispheric structure and adequate spontaneous activity of cf inputs are critical for cerebellar asymmetrical motor learning.

View Article: PubMed Central - PubMed

Affiliation: Neural Cybernetics Laboratory, Department of Computer Science, Chubu University Kasugai, Japan.

ABSTRACT
To acquire and maintain precise movement controls over a lifespan, changes in the physical and physiological characteristics of muscles must be compensated for adaptively. The cerebellum plays a crucial role in such adaptation. Changes in muscle characteristics are not always symmetrical. For example, it is unlikely that muscles that bend and straighten a joint will change to the same degree. Thus, different (i.e., asymmetrical) adaptation is required for bending and straightening motions. To date, little is known about the role of the cerebellum in asymmetrical adaptation. Here, we investigate the cerebellar mechanisms required for asymmetrical adaptation using a bi-hemispheric cerebellar neuronal network model (biCNN). The bi-hemispheric structure is inspired by the observation that lesioning one hemisphere reduces motor performance asymmetrically. The biCNN model was constructed to run in real-time and used to control an unstable two-wheeled balancing robot. The load of the robot and its environment were modified to create asymmetrical perturbations. Plasticity at parallel fiber-Purkinje cell synapses in the biCNN model was driven by error signal in the climbing fiber (cf) input. This cf input was configured to increase and decrease its firing rate from its spontaneous firing rate (approximately 1 Hz) with sensory errors in the preferred and non-preferred direction of each hemisphere, as demonstrated in the monkey cerebellum. Our results showed that asymmetrical conditions were successfully handled by the biCNN model, in contrast to a single hemisphere model or a classical non-adaptive proportional and derivative controller. Further, the spontaneous activity of the cf, while relatively small, was critical for balancing the contribution of each cerebellar hemisphere to the overall motor command sent to the robot. Eliminating the spontaneous activity compromised the asymmetrical learning capabilities of the biCNN model. Thus, we conclude that a bi-hemispheric structure and adequate spontaneous activity of cf inputs are critical for cerebellar asymmetrical motor learning.

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Control plant and symmetrical/asymmetrical perturbations. (A) Two wheel balancing robot. (B) Cartoon representing the lateral view of the robot during the symmetrical perturbation by adding a load on-center of the robot's vertical axis. θ(t) and ϕ(t) are the robot's body and wheel position control variables irrespectively. (C) Asymmetrical perturbation by adding an external load off-center of the vertical axis of the robot. (D) Asymmetrical perturbation by changing the angle of the platform where the robot moves.
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Figure 5: Control plant and symmetrical/asymmetrical perturbations. (A) Two wheel balancing robot. (B) Cartoon representing the lateral view of the robot during the symmetrical perturbation by adding a load on-center of the robot's vertical axis. θ(t) and ϕ(t) are the robot's body and wheel position control variables irrespectively. (C) Asymmetrical perturbation by adding an external load off-center of the vertical axis of the robot. (D) Asymmetrical perturbation by changing the angle of the platform where the robot moves.

Mentions: The two-wheel balancing robot (e-nuvo wheel, ZMP INC, Tokyo) (Figure 5) is an inverted pendulum system widely used in control engineering for testing control strategies, because of its highly unstable dynamics. The robot is considered one of the most challenging control plants (Li et al., 2013). The robot is equipped with a set of sensors including a motor encoder and a gyroscope, which provide wheel angle (ϕ(t)) and body tilt angle (θ(t)), respectively. The robot is also equipped with a USART chip to allow serial communication with the computer on which the biCNN model is implemented at sampling period Ts = 10 ms, which is the same time interval used in the present biCNN model. The motion of the robot is driven by a single DC motor connected to the 2 wheels, which share the same shaft. The mf inputs for this control object carry the following signals: (1) desired wheel angle ϕref(t), (2) desired wheel angular velocity ref(t), (3) body tilt angle error θe(t) = θref(t) − θ(t), where θref(t) is the desired body tilt angle, (4) body tilt angular velocity error e(t) = ref(t) − (t), where ref(t) is the desired body tilt angle, (5) wheel angle error ϕe(t) = ϕref(t) − ϕ(t), (6) wheel angular velocity error e(t) = ref(t) − (t), and (7) efference copy of motor command. The desired body tilt angle θref(t) and velocity ref(t) were set to zero radians, so that the robot is commanded to remain vertical while following the desired wheel angle trajectory, which was set to a sinusoidal motion ϕref(t) = π sin(2π0.25t). These seven mfs were repeated 81 times to generate the 562 mfs required in the biCNN model. Perturbations to the robot, symmetrical and asymmetrical, were created by placing an external load (300 g, 50% of robot's mass) on the top and center of the vertical axis of the robot (symmetrical load depicted in Figure 5B), off-center on the front/back (asymmetrical load depicted in Figure 5C), or by changing the angle of the platform on which the robot was moving (depicted in Figure 5D).


A bi-hemispheric neuronal network model of the cerebellum with spontaneous climbing fiber firing produces asymmetrical motor learning during robot control.

Pinzon-Morales RD, Hirata Y - Front Neural Circuits (2014)

Control plant and symmetrical/asymmetrical perturbations. (A) Two wheel balancing robot. (B) Cartoon representing the lateral view of the robot during the symmetrical perturbation by adding a load on-center of the robot's vertical axis. θ(t) and ϕ(t) are the robot's body and wheel position control variables irrespectively. (C) Asymmetrical perturbation by adding an external load off-center of the vertical axis of the robot. (D) Asymmetrical perturbation by changing the angle of the platform where the robot moves.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4221029&req=5

Figure 5: Control plant and symmetrical/asymmetrical perturbations. (A) Two wheel balancing robot. (B) Cartoon representing the lateral view of the robot during the symmetrical perturbation by adding a load on-center of the robot's vertical axis. θ(t) and ϕ(t) are the robot's body and wheel position control variables irrespectively. (C) Asymmetrical perturbation by adding an external load off-center of the vertical axis of the robot. (D) Asymmetrical perturbation by changing the angle of the platform where the robot moves.
Mentions: The two-wheel balancing robot (e-nuvo wheel, ZMP INC, Tokyo) (Figure 5) is an inverted pendulum system widely used in control engineering for testing control strategies, because of its highly unstable dynamics. The robot is considered one of the most challenging control plants (Li et al., 2013). The robot is equipped with a set of sensors including a motor encoder and a gyroscope, which provide wheel angle (ϕ(t)) and body tilt angle (θ(t)), respectively. The robot is also equipped with a USART chip to allow serial communication with the computer on which the biCNN model is implemented at sampling period Ts = 10 ms, which is the same time interval used in the present biCNN model. The motion of the robot is driven by a single DC motor connected to the 2 wheels, which share the same shaft. The mf inputs for this control object carry the following signals: (1) desired wheel angle ϕref(t), (2) desired wheel angular velocity ref(t), (3) body tilt angle error θe(t) = θref(t) − θ(t), where θref(t) is the desired body tilt angle, (4) body tilt angular velocity error e(t) = ref(t) − (t), where ref(t) is the desired body tilt angle, (5) wheel angle error ϕe(t) = ϕref(t) − ϕ(t), (6) wheel angular velocity error e(t) = ref(t) − (t), and (7) efference copy of motor command. The desired body tilt angle θref(t) and velocity ref(t) were set to zero radians, so that the robot is commanded to remain vertical while following the desired wheel angle trajectory, which was set to a sinusoidal motion ϕref(t) = π sin(2π0.25t). These seven mfs were repeated 81 times to generate the 562 mfs required in the biCNN model. Perturbations to the robot, symmetrical and asymmetrical, were created by placing an external load (300 g, 50% of robot's mass) on the top and center of the vertical axis of the robot (symmetrical load depicted in Figure 5B), off-center on the front/back (asymmetrical load depicted in Figure 5C), or by changing the angle of the platform on which the robot was moving (depicted in Figure 5D).

Bottom Line: The bi-hemispheric structure is inspired by the observation that lesioning one hemisphere reduces motor performance asymmetrically.Our results showed that asymmetrical conditions were successfully handled by the biCNN model, in contrast to a single hemisphere model or a classical non-adaptive proportional and derivative controller.Thus, we conclude that a bi-hemispheric structure and adequate spontaneous activity of cf inputs are critical for cerebellar asymmetrical motor learning.

View Article: PubMed Central - PubMed

Affiliation: Neural Cybernetics Laboratory, Department of Computer Science, Chubu University Kasugai, Japan.

ABSTRACT
To acquire and maintain precise movement controls over a lifespan, changes in the physical and physiological characteristics of muscles must be compensated for adaptively. The cerebellum plays a crucial role in such adaptation. Changes in muscle characteristics are not always symmetrical. For example, it is unlikely that muscles that bend and straighten a joint will change to the same degree. Thus, different (i.e., asymmetrical) adaptation is required for bending and straightening motions. To date, little is known about the role of the cerebellum in asymmetrical adaptation. Here, we investigate the cerebellar mechanisms required for asymmetrical adaptation using a bi-hemispheric cerebellar neuronal network model (biCNN). The bi-hemispheric structure is inspired by the observation that lesioning one hemisphere reduces motor performance asymmetrically. The biCNN model was constructed to run in real-time and used to control an unstable two-wheeled balancing robot. The load of the robot and its environment were modified to create asymmetrical perturbations. Plasticity at parallel fiber-Purkinje cell synapses in the biCNN model was driven by error signal in the climbing fiber (cf) input. This cf input was configured to increase and decrease its firing rate from its spontaneous firing rate (approximately 1 Hz) with sensory errors in the preferred and non-preferred direction of each hemisphere, as demonstrated in the monkey cerebellum. Our results showed that asymmetrical conditions were successfully handled by the biCNN model, in contrast to a single hemisphere model or a classical non-adaptive proportional and derivative controller. Further, the spontaneous activity of the cf, while relatively small, was critical for balancing the contribution of each cerebellar hemisphere to the overall motor command sent to the robot. Eliminating the spontaneous activity compromised the asymmetrical learning capabilities of the biCNN model. Thus, we conclude that a bi-hemispheric structure and adequate spontaneous activity of cf inputs are critical for cerebellar asymmetrical motor learning.

Show MeSH
Related in: MedlinePlus