Giovanni Maffei

Speed generalization capabilities of a cerebellar model on a rapid navigation task

The cerebellum is a brain structure necessary for skilled motor behaviour and has a well understood and repetitive architecture. Such an architecture inspired the Marr-Albus-Ito theory of cerebellar learning, that provides an explanation for the acquisition of motor skills by the cerebellum. Numerous computational models inspired in such a theory have already been employed in robotic tasks. Here we look into one of the suggested roles of the cerebellum, the replacement of reflexes by anticipatory actions and we apply it to a robot navigation task. The acquisition of anticipatory actions has been thoroughly studied in the field of classical conditioning. Of particular interest is the so-called CS-intensity effect, an effect that links the rapidity of execution of an anticipatory protective action, the Conditioned Response (CR), to the intensity of a predictive signal, the Conditioning Stimulus (CS). We propose that the CS-intensity effect implements a built-in sensory-motor contingency that allows to carry over a skill learned in a safe and easy context, e.g., turning at slow velocity, to a more difficult one, e.g., a turning at a faster speed. We demonstrate this hypothesis in a series of experiments where a robot has to navigate a track that has a turn. We show that after being trained at a slow velocity, by means of the CS-intensity effect, the cerebellar controller modulates the turning such that its onset anticipates as the robot speed increases. Ultimately, through incremental learning, this generalization allows the robot to learn to navigate the track at its maximum speed.

The Role of a Cerebellum-Driven Perceptual Prediction within a Robotic Postural Task

Postural adjustments are acquired compensatory and anticipatory motor responses maintaining balance and equilibrium against self-induced or external perturbations. It has been proposed that the cerebellum could be involved in issuing such predictive motor actions. However, it remains unclear what strategy is adopted by the brain in order to make such prediction and how anticipatory and compensatory components are integrated into a single response. Within this study we are interested in the computational mechanisms underlying the acquisition of anticipatory responses in a postural task. We compare two alternative architectures representing two different hypotheses: anticipation either as sensory-to-motor association or as sensory-to-sensory association. We propose to use a cerebellar model to control the acquisition of an adaptive motor response in a simulated robotic setup. We devise a scenario where a cart-pole robot is trained to predict a perturbation and issue an anticipatory action to minimize the disturbance on its state of equilibrium. Our results show that a cerebellum based architecture can efficiently learn to reduce errors through anticipation. We also suggest that a sensory-to-sensory prediction could be less expensive in terms of energy cost and more robust when events violate the acquired prediction.

Acquisition of Synergistic Motor Responses through Cerebellar Learning in a Robotic Postural Task

Coordination of synergistic movements is a crucial aspect of goal oriented motor behavior in postural control. It has been proposed that the cerebellum could be involved in the acquisition of adaptive fine-tuned motor responses. However, it remains unclear whether motor patterns and action sequences can be learned as a result of recurrent connections among multiple cerebellar microcircuits. Within this study we hypothesize that such link could be found in the Nucleo-Pontine projection and we investigate the behavioral advantages of cerebellar driven synergistic motor responses in a robotic postural task. We devise a scenario where a double-joint cart-pole robot has to learn to stand and balance interconnected segments by issuing multiple actions in order to minimize the deviation from a state of equilibrium. Our results show that a cerebellum based architecture can efficiently learn to reduce errors through well-timed motor coordination. We also suggest that such strategy could reduce energy cost by progressively synchronizing multiple joints movements.

Learning Modular Sequences in the Striatum

The execution of habitual actions is thought to rely on the exploitation of procedural motor memories. These memories encode motor commands as organized in functional sequences with well defined boundaries in the Striatum. Here, we present a biophysical model of the striatal network composed by inhibitory medium spiny neurons (MSNs) governed by anti-hebbian STDP. We show that these two features allow for learning an arbitrary sequence through multiple exposures to cortical inputs and reproducing it under a single, non-specific excitatory drive. Our results shed light on the computational properties of biologically plausible inhibitory networks and suggest a simple, yet effective mechanism of behavioral control through striatal circuits.

The perceptual shaping of anticipatory actions

Humans display anticipatory motor responses to minimize the adverse effects of predictable perturbations. A widely accepted explanation for this behaviour relies on the notion of an inverse model that, learning from motor errors, anticipates corrective responses. Here, we propose and validate the alternative hypothesis that anticipatory control can be realized through a cascade of purely sensory predictions that drive the motor system, reflecting the causal sequence of the perceptual events preceding the error. We compare both hypotheses in a simulated anticipatory postural adjustment task. We observe that adaptation in the sensory domain, but not in the motor one, supports the robust and generalizable anticipatory control characteristic of biological systems. Our proposal unites the neurobiology of the cerebellum with the theory of active inference and provides a concrete implementation of its core tenets with great relevance both to our understanding of biological control systems and, possibly, to their emulation in complex artefacts.

Learning to Balance While Reaching: A Cerebellar-Based Control Architecture for a Self-balancing Robot

In nature, Anticipatory Postural Adjustments (APAs) are actions that precede predictable disturbances with the goal of maintaining a stable body posture. Neither the structure of the computations that enable APAs are known nor adaptive APAs have been exploited in robot control. Here we propose a computational architecture for the acquisition of adaptive APAs based on current theories about the involvement of the cerebellum in predictive motor control. The architecture is applied to a simulated self-balancing robot (SBR) mounting a moveable arm, whose actuation induces a perturbation of the robot balance that can be counteracted by an APA. The architecture comprises both reactive (feedback) and anticipatory-adaptive (feed-forward) layers. The reactive layer consists of a cascade-PID controller and the adaptive one includes cerebellar-based modules that supply the feedback layer with predictive signals. We show that such architecture succeeds in acquiring functional APAs, thus demonstrating in a simulated robot an adaptive control strategy for the cancellation of a self-induced disturbance grounded in animal motor control. These results also provide a hypothesis for the implementation of APAs in nature that could inform further experimental research.

Acquisition of anticipatory postural adjustment through cerebellar learning in a mobile robot

Anticipatory Postural Adjustments (APAs) are motor responses which anticipate a perturbation on the current body position caused by a voluntary act. Here we propose that APAs can be decomposed into a compensatory and an anticipatory component and that the cerebellum might be involved in the acquisition of such responses. To test this hypothesis, we use a cerebellar model to control the acquisition of an APA in a robotic task: we devise a setup where a mobile robot is trained to acquire an APA which minimizes a perturbation in its speed after a collision with an obstacle. Our results show that the same cerebellar model can support the acquisition of an APA separately learning its two sub-components. Moreover, our solution suggests that the acquisition of an APA involves two stages: acquisition of a compensatory motor response and prediction of an incoming sensory signal useful to trigger the same response in an anticipatory manner.

Adaptively Learning Levels of Coordination from One’s, Other’s and Task Related Errors Through a Cerebellar Circuit: A Dual Cart-Pole Setup

Behavioral and theoretical studies have shown that during joint action in an interpersonal skilled activity, like carrying an object collaboratively, anticipation is required to further improve the precision in the realization of the task. We model this task as a dual cart pole setup, and we provide a computational basis of how this anticipation can be realized at different levels: anticipating errors originating from the agent’s body control, errors related to the global task and errors derived from the anticipation of the other’s actions. We model computationally the control loops of the agents as an interplay of feedback and feedforward components and we base the latter on previous research on the cerebellar circuit network. Our results confirm experimentally that anticipating the error in the task including inputs extracted from the behavior of the other, further improves precision in the realization.

Visuotactile integration modulates motor performance in a perceptual decision-making task

Body ownership is critically dependent on multimodal integration as for instance revealed in the Rubber Hand Illusion (RHI) and a number of studies which have addressed the neural correlates of the processes underlying this phenomenon. Both experimental and clinical research have shown that the structures underlying body ownership seem to significantly overlap with those of motor control including the parietal and ventral premotor cortices, Temporal Parietal Junction (TPJ) and the insula. This raises the question of whether this structural overlap between body ownership and motor control structures is of any functional significance. Here, we investigate the specific question of whether experimentally induced ownership over a virtual limb can modulate the performance of that limb in a simple sensorimotor task. Using a Virtual reality (VR) environment we modulate body ownership in three experimental conditions with respect to the (in)congruence of stimulus configurations. Our results show that the degree of ownership directly modulates motor performance. This implies that body ownership is not exclusively a perceptual and/or subjective multimodal state but that it is tightly coupled to systems for decision-making and motor control.

Author Correction: Visuotactile integration modulates motor performance in a perceptual decision-making task

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.a