Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

Abstract

BackgroundSeveral authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model).Methodology/Principal FindingsThis study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised learning, this model learns to control an anthropomorphic robot arm actuated by two antagonists McKibben artificial muscles. This was achieved by using internal parallel feedback loops containing neural networks which anticipate the sensorimotor consequences of the neural commands. The artificial neural networks architecture was similar to the large-scale connectivity of the cerebellar cortex. Movements in the sagittal plane were performed during three sessions combining different initial positions, amplitudes and directions of movements to vary the effects of the gravitational torques applied to the robotic arm. The results show that this model acquired an internal representation of the gravitational effects during vertical arm pointing movements.Conclusions/SignificanceThis is consistent with the proposal that the cerebellar cortex contains an internal representation of gravitational torques which is encoded through a learning process. Furthermore, this model suggests that the cerebellum performs the inverse dynamics computation based on sensorimotor predictions. This highlights the importance of sensorimotor predictions of gravitational torques acting on upper limb movements performed in the gravitational field.

Effect of gravity-like torque on goal-directed arm movements in microgravity

“…Overall, the present study, in line with others, strongly suggests that gravitational influences are taken into account for arm movement organization and execution in a predictive manner (Bockisch and Haslwanter 2007;Crevecoeur et al 2009;Gentili et al 2007;Guillaud et al 2011;Papaxanthis et al 1998aPapaxanthis et al , 1998bPapaxanthis et al , 2005. For instance, while the final accuracy of upward/downward arm reaching movements is impaired during initial exposure to microgravity, typical kinematic features (e.g., curvature differences between upward and downward movements) are maintained despite the absence of gravity-related biomechanical constraints (Papaxanthis et al 1998a(Papaxanthis et al , 1998b.…”

“…Crevecoeur et al 2009;Gaveau and Papaxanthis 2011), arm motor commands are optimized with respect to the action of gravity on the limb, whose consequences are integrated in motor planning and anticipated in terms of expected sensory states. It has been further hypothesized that gravity is encoded in the central nervous system and that the cerebellum may contain an internal representation of gravitational torques used for sensorimotor predictions (Gentili et al 2009). Taking this idea further, it is tempting to hypothesize that reintroducing gravitational constraints on the moving limb by adding shoulder torque may reactivate forward internal models associated with 1g sensorimotor predictions, on the basis of an enhanced position sense.…”

Cortical network modeling for inverse kinematic computation of an anthropomorphic finger

“…On the long term, future work will focus on the dynamics of the fingers since this neural model controls a biomechanical system without including any dynamic components (e.g., gravity, inertia). This could be performed by modeling more explicitly structures such as the Cerebellum that has been considered to encode this type of information [5],[6]. In summary, the aim of this research is to design a bio-mimetic controller providing adaptive, robust and flexible control of dexterous robotic/prosthetics hands.…”

Cortex inspired model for inverse kinematics computation for a humanoid robotic finger

“…The first one includes models that do not account for any particular neurophysiological substrate, resulting in very limited biological plausibility (e.g., [7]). The second approach proposes neural models that are biologically conceivable by incorporating particular brain structures and/or functions such as the cerebellum [8],[9] or the population vector coding processes that were previously revealed in motor/premotor areas [10]–[14]. Consistent with the second approach, recently a cortical network model able to learn the internal inverse kinematics model of a simulated anthropomorphic robot finger was proposed [13],[14].…”