Emergent modular neural control drives coordinated motor actions.

A remarkable feature of motor control is the ability to coordinate movements across distinct body parts into a consistent, skilled action. To reach and grasp an object, 'gross' arm and 'fine' dexterous movements must be coordinated as a single action. How the nervous system achieves this coordination is currently unknown. One possibility is that, with training, gross and fine movements are co-optimized to produce a coordinated action; alternatively, gross and fine movements may be modularly refined to function together. To address this question, we recorded neural activity in the primary motor cortex and dorsolateral striatum during reach-to-grasp skill learning in rats. During learning, the refinement of fine and gross movements was behaviorally and neurally dissociable. Furthermore, inactivation of the primary motor cortex and dorsolateral striatum had distinct effects on skilled fine and gross movements. Our results indicate that skilled movement coordination is achieved through emergent modular neural control

Optimal anticipatory control as a theory of motor preparation

Supported by a decade of primate electrophysiological experiments, the prevailing theory of neural motor control holds that movement generation is accomplished by a preparatory process that progressively steers the state of the motor cortex into a movement-specific optimal subspace prior to movement onset. The state of the cortex then evolves from these optimal subspaces, producing patterns of neural activity that serve as control inputs to the musculature. This theory, however, does not address the following questions: what characterizes the optimal subspace and what are the neural mechanisms that underlie the preparatory process? We address these questions with a circuit model of movement preparation and control. Specifically, we propose that preparation can be achieved by optimal feedback control (OFC) of the cortical state via a thalamo-cortical loop. Under OFC, the state of the cortex is selectively controlled along state-space directions that have future motor consequences, and not in other inconsequential ones. We show that OFC enables fast movement preparation and explains the observed orthogonality between preparatory and movement-related monkey motor cortex activity. This illustrates the importance of constraining new theories of neural function with experimental data. However, as recording technologies continue to improve, a key challenge is to extract meaningful insights from increasingly large-scale neural recordings. Latent variable models (LVMs) are powerful tools for addressing this challenge due to their ability to identify the low-dimensional latent variables that best explain these large data sets. One shortcoming of most LVMs, however, is that they assume a Euclidean latent space, while many kinematic variables, such as head rotations and the configuration of an arm, are naturally described by variables that live on non-Euclidean latent spaces (e.g., SO(3) and tori). To address this shortcoming, we propose the Manifold Gaussian Process Latent Variable Model, a method for simultaneously inferring nonparametric tuning curves and latent variables on non-Euclidean latent spaces. We show that our method is able to correctly infer the latent ring topology of the fly and mouse head direction circuits.This work was supported by a Trinity-Henry Barlow scholarship and a scholarship from the Ministry of Education, ROC Taiwan

Estudio e implementación de algoritmos de fusión sensorial para sensores pulsantes y clásicos con protocolo AER de comunicación y aplicación en sistemas robóticos neuroinspirados

The objective of this thesis is to analyze, design, simulate and implement a model that follows the principles of the human nervous system when a reaching movement is made. The background of the thesis is the neuromorphic engineering field. This term was first coined in the late eighties by Caver Mead. Its main objective is to develop hardware devices, based on the neuron as the basic unit, to develop a range of tasks such as: decision making, image processing, learning, etc. During the last twenty years, this field of research has gathered a large number of researchers around the world. Spike-based sensors and devices that perform spike processing tasks have been developed. A neuro-inspired controller model based on the classic algorithms VITE and FLETE is proposed in this thesis (specifically, the two algorithms presented are: the VITE model which generates a non-planned trajectory and the FLETE model to generate the forces needed to hold a position reached). The hardware platforms used to implement them are a FPGA and a VLSI multi-chip setup. Then, considering how a reaching movement is performed by humans, these algorithms are translated under the constraints of each hardware device. The constraints are: spike-processing blocks described in VHDL for the FPGA and neurons LIF for the VLSI chips. To reach a successful translation of VITE algorithm under the constraints of the FPGA, a new spike-processing block is designed, simulated and implemented: GO Block. On the other hand, to perform an accurate translation of the VITE algorithm under VLSI requirements, the recent biological advances are studied. Then, a model which implements the co-activation of NMDA channels (this activity is related to the activity detected in the basal ganglia short time before a movement is made) is modeled, simulated and implemented. Once the model is defined for both platforms, it is simulated using the Matlab Simulink environment for FPGA and Brian simulator for VLSI chips. The hardware results of the algorithms translated are presented. The open-loop spike-based VITE (on both platforms) and closed-loop (FPGA) applied and connected to a robotic platform using the AER bus show an excellent behaviour in terms of power and resources consumption. They show also an accurate and precise functioning for reaching and tracking movements when the target is supplied by an AER retina or jAER. Thus, a full neuro-inspired architecture is implemented: from the sensor (retina) to the end effector (robot) going through the neuro-inspired controller designed. An alternative for the SVITE platform is also presented. A random element is added to the neuron model to include variability in the neural response. The results obtained for this variant, show a similar behaviour if a comparison with the deterministic algorithms is made. The possibility to include this pseudo-random controller in noise and / or random environment is demonstrated. Finally, this thesis claims that PFM is the most suitable modulation to drive motors in a neuromorphic hardware environment. It allows supplying the events directly to the motors. Furthermore, it is achieved that the system is not affected by spurious or noisy events. The novel results achieved with the VLSI multi-chip setup, this is the first attempt to control a robotic platform using sub-thresold low-power neurons, intended to set the basis for designing neuro-inspired controllers

L’influence de l'anticipation sur les modulations de puissance dans la bande de fréquence bêta durant la préparation du mouvement et L'effet de la variance dans les rétroactions sensorielles sur la rétention à court terme

La production du mouvement est un aspect primordial de la vie qui permet aux organismes vivants d'interagir avec l'environnement. En ce sens, pour être efficaces, tous les mouvements doivent être planifiés et mis à jour en fonction de la complexité et de la variabilité de l'environnement. Des chercheurs du domaine du contrôle moteur ont étudié de manière approfondie les processus de planification et d’adaptation motrice. Puisque les processus de planification et d'adaptation motrice sont influencés par la variabilité de l'environnement, le présent mémoire cherche à fournir une compréhension plus profonde de ces deux processus moteurs à cet égard. La première contribution scientifique présentée ici tire parti du fait que les temps de réaction (TR) sont réduits lorsqu'il est possible d'anticiper l’objectif moteur, afin de déterminer si les modulations de TR associées à l'anticipation spatiale et temporelle sont sous-tendues par une activité préparatoire similaire. Cela a été fait en utilisant l'électroencéphalographie (EEG) de surface pour analyser l'activité oscillatoire dans la bande de fréquence bêta (13 - 30 Hz) au cours de la période de planification du mouvement. Les résultats ont révélé que l'anticipation temporelle était associée à la désynchronisation de la bande bêta au-dessus des régions sensorimotrices controlatérales à la main effectrice, en particulier autour du moment prévu de l'apparition de la cible. L’ampleur de ces modulations était corrélée aux modulations de TR à travers les participants. En revanche, l'anticipation spatiale a augmenté de manière sélective la puissance de la bande bêta au-dessus des régions pariéto-occipitales bilatérales pendant toute la période de planification. Ces résultats suggèrent des états de préparation distinct en fonction de l’anticipation temporelle et spatiale. D’un autre côté, le deuxième projet traite de la façon dont la variabilité de la rétroaction sensorielle interfère avec la rétention à court terme dans l’étude de l’adaptation motrice. Plus précisément, une tâche d'adaptation visuomotrice a été utilisée au cours de laquelle la variance des rotations a été manipulée de manière paramétrique à travers trois groupes, et ce, tout au long de la période d’acquisition. Par la suite, la rétention de cette nouvelle relation visuomotrice a été évaluée. Les résultats ont révélé que, même si le processus d'adaptation était robuste à la manipulation de la variance, la rétention à court terme était altérée par des plus hauts niveaux de variance. Finalement, la discussion a d'abord cherché à intégrer ces deux contributions en revisitant l'interprétation des résultats sous un angle centré sur l'incertitude et en fournissant un aperçu des potentielles représentations internes de l'incertitude susceptibles de sous-tendre les résultats expérimentaux observés. Par la suite, une partie de la discussion a été réservée à la manière dont le champ du contrôle moteur migre de plus en plus vers l’utilisation de tâches et d’approches expérimentales plus complexes, mais écologiques aux dépends des tâches simples, mais quelque peu dénaturées que l’on retrouve dans les laboratoires du domaine. La discussion a été couronnée par une brève proposition allant dans ce sens.Abstract: Motor behavior is a paramount aspect of life that enables the living to interact with the environment through the production of movement. In order to be efficient, movements need to be planned and updated according to the complexity and the ever-changing nature of the environment. Motor control experts have extensively investigated the planning and adaptation processes. Since both motor planning and motor adaptation processes are influenced by variability in the environment, the present thesis seeks to provide a deeper understanding of both these motor processes in this regard. More specifically, the first scientific contribution presented herein leverages the fact that reaction times (RTs) are reduced when the anticipation of the motor goal is possible to elucidate whether the RT modulations associated with temporal and spatial anticipation are subtended by similar preparatory activity. This was done by using scalp electroencephalography (EEG) to analyze the oscillatory activity in the beta frequency band (13 – 30 Hz) during the planning period. Results revealed that temporal anticipation was associated with beta-band desynchronization over contralateral sensorimotor regions, specifically around the expected moment of target onset, the magnitude of which was correlated with RT modulations across participants. In contrast, spatial anticipation selectively increased beta-band power over bilateral parieto-occipital regions during the entire planning period, suggesting that distinct states of preparation are incurred by temporal and spatial anticipation. Additionally, the second project addressed how variance in the sensory feedback interferes with short-term retention of motor adaptation. Specifically, a visuomotor adaptation task was used during which the variance of exposed rotation was parametrically manipulated across three groups, and retention of the adapted visuomotor relationship was assessed. Results revealed that, although the adaptation process was robust to the manipulation of variance, the short-term retention was impaired. The discussion first sought to integrate these two projects by revisiting the interpretation of both projects under the scope of uncertainty and by providing an overview of the internal representation of uncertainty that might subtend the experimental results. Subsequently, a part of the discussion was reserved to allude how the motor control field is transitioning from laboratory-based tasks to more naturalistic paradigms by using approaches to move motor control research toward real-world conditions. The discussion culminates with a brief scientific proposal along those lines

Movement curvature planning through force field internal models

Human motion studies have focused primarily on modeling straight point-to-point reaching movements. However, many goal-directed reaching movements, such as movements directed towards oneself, are not straight but rather follow highly curved trajectories. These movements are particularly interesting to study since they are essential in our everyday life, appear early in development and are routinely used to assess movement deficits following brain lesions. We argue that curved and straight-line reaching movements are generated by a unique neural controller and that the observed curvature of the movement is the result of an active control strategy that follows the geometry of one's body, for instance to avoid trajectories that would hit the body or yield postures close to the joint limits. We present a mathematical model that accounts for such an active control strategy and show that the model reproduces with high accuracy the kinematic features of human data during unconstrained reaching movements directed toward the head. The model consists of a nonlinear dynamical system with a single stable attractor at the target. Embodiment-related task constraints are expressed as a force field that acts on the dynamical system. Finally, we discuss the biological plausibility and neural correlates of the model's parameters and suggest that embodiment should be considered as a main cause for movement trajectory curvatur

Eye-Head-Hand Coordination During Visually Guided Reaches in Head-Unrestrained Macaques

Our goal was to determine if reaching influences eye-head coordination (and vice versa) in Rhesus macaques. Eye, head, and hand motion were recorded in two animals using search coil and touch screen technology, respectively. Animals were seated in a customized chair which allowed unencumbered head motion and reaching in depth. In the reach condition, animals were trained to touch a central LED at waist level while maintaining central gaze and were then rewarded if they touched a target appearing at one of 15 locations. In other variants, initial hand or gaze position were varied in the horizontal plane. In similar control tasks, animals were rewarded for gaze accuracy. In the reach task, animals made eye-head gaze shifts toward the target followed by reaches that were accompanied by prolonged head motion toward the target. This resulted in significantly larger velocities and final ranges of head position compared with the gaze control

On the structure of natural human movement

Understanding of human motor control is central to neuroscience with strong implications in the fields of medicine, robotics and evolution. It is thus surprising that the vast majority of motor control studies have focussed on human movement in the laboratory while neglecting behaviour in natural environments. We developed an experimental paradigm to quantify human behaviour in high resolution over extended periods of time in ecologically relevant environments. This allows us to discover novel insights and contradictory evidence to well-established findings obtained in controlled laboratory conditions. Using our data, we map the statistics of natural human movement and their variability between people. The variability and complexity of the data recorded in these settings required us to develop new tools to extract meaningful information in an objective, data-driven fashion. Moving from descriptive statistics to structure, we identify stable structures of movement coordination, particularly within the arm-hand area. Combining our data with numerous published findings, we argue that current hypotheses that the brain simplifies motor control problems by dimensionality reduction are too reductionist. We propose an alternative hypothesis derived from sparse coding theory, a concept which has been successfully applied to the sensory system. To investigate this idea, we develop an algorithm for unsupervised identification of sparse structures in natural movement data. Our method outperforms state-of-the-art algorithms for accuracy and data-efficiency. Applying this method to hand data reveals a dictionary of \emph{sparse eigenmotions} (SEMs) which are well preserved across multiple subjects. These are highly efficient and invariant representation of natural movement, and suggest a potential higher-order grammatical structure or ``movement language''. Our findings make a number of testable predictions about neural coding of movement in the cortex. This has direct consequences for advancing research on dextrous prosthetics and robotics, and has profound implications for our understanding of how the brain controls our body.Open Acces

Neurally Plausible Model of Robot Reaching Inspired by Infant Motor Babbling

In this dissertation, we present an abstract model of infant reaching that is neurally-plausible. This model is grounded in embodied artificial intelligence, which emphasizes the importance of the sensorimotor interaction of an agent and the world. It includes both learning sensorimotor correlations through motor babbling and also arm motion planning using spreading activation. We introduce a mechanism called bundle formation as a way to generalize motions during the motor babbling stage. We then offer a neural model for the abstract model, which is composed of three layers of neural maps with parallel structures representing the same sensorimotor space. The motor babbling period shapes the structure of the three neural maps as well as the connections within and between them; these connections encode trajectory bundles in the neural maps. We then investigate an implementation of the neural model using a reaching task on a humanoid robot. Through a set of experiments, we were able to find the best way to implement different components of this model such as motor babbling, neural representation of sensorimotor space, dimension reduction, path planning, and path execution. After the proper implementation had been found, we conducted another set of experiments to analyze the model and evaluate the planned motions. We evaluated unseen reaching motions using jerk, end effector error, and overshooting. In these experiments, we studied the effect of different dimensionalities of the reduced sensorimotor space, different bundle widths, and different bundle structures on the quality of arm motions. We hypothesized a larger bundle width would allow the model to generalize better. The results confirmed that the larger bundles lead to a smaller error of end-effector position for testing targets. An experiment with the resolution of neural maps showed that a neural map with a coarse resolution produces less smooth motions compared to a neural map with a fine resolution. We also compared the unseen reaching motions under different dimensionalities of the reduced sensorimotor space. The results showed that a smaller dimension leads to less smooth and accurate movements