Contrôle moteur | EPFL Graph Search

En neurosciences, le contrôle moteur est la capacité de faire des ajustements posturaux dynamiques et de diriger le corps et les membres dans le but de faire un mouvement déterminé. Le mouvement volontaire est initié par le cortex moteur primaire et le cortex prémoteur. Le signal est ensuite transmis aux circuits du tronc cérébral et de la moelle épinière qui activent les muscles squelettiques qui, en se contractant, produisent un mouvement. Le mouvement produit renvoie des informations proprioceptives au système nerveux central (SNC). À la suite de la rétroaction reçue, le mouvement peut être modifié, ajusté ou continué. D’autres structures du SNC influencent le mouvement telles que les ganglions de la base et le cervelet. Les afférences sensorielles contribuent également à la rétroaction. Une atteinte à une ou plusieurs de ces structures ou des voies afférentes ou efférentes auxquelles elles participent peut donc affecter le contrôle moteur.(Purves et al., 2007) Plusieurs composan

Mechanical intelligence for learning embodied sensor-object relationships

Ahalya Prabhakar

Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsupervised learning problem. Current machine learning methods process new sensory data using prior knowledge defined by either domain knowledge or datasets. When datasets are not available, data acquisition is needed, though automating exploration in support of learning is still an unsolved problem. Here we develop a method that enables agents to efficiently collect data for learning a predictive sensor model-without requiring domain knowledge, human input, or previously existing data-using ergodicity to specify the data acquisition process. This approach is based entirely on data-driven sensor characteristics rather than predefined knowledge of the sensor model and its physical characteristics. We learn higher quality models with lower energy expenditure during exploration for data acquisition compared to competing approaches, including both random sampling and information maximization. In addition to applications in autonomy, our approach provides a potential model of how animals use their motor control to develop high quality models of their sensors (sight, sound, touch) before having knowledge of their sensor capabilities or their surrounding environment. Information-based search strategies are relevant for the learning of interacting agents dynamics and usually need predefined data. The authors propose a method to collect data for learning a predictive sensor model, without requiring domain knowledge, human input, or previously existing data.

NATURE PORTFOLIO2022

Reverse engineering the motor control system

Berat Denizdurduran

Mammalian motor control is implemented by a combination of different networks and system, working coherently to plan the movement of the body or a limb and to execute this movement a dynamical environment. While it is believed that complex, voluntary movements are planned in the motor areas of the cortex, the execution of the movement is controlled by a combination of cortical and cerebellar networks together with central pattern generators and reflex circuits in the spinal cord. In this thesis, we propose an abstract model that captures the basic properties of mammalian motor control, using the example of two different movements: arm movements as well as locomotion. Our model consists of three parts: first a high-level network, that learns movements, using a combination of actor-critic based reinforcement learning and Optimal Control theory. The second part corresponds to spinal reflex circuits that execute basic movements of the musculo-skeletal system. They are modeled by a simple neural network, that learns the dynamic properties of the muscoloskeletal system by the mechanism of spontaneous motor activity (muscle twithing) combined with a Hebbian learning rule. We demonstrate that this network can learn the antagonistic control of joint movements. The final part of the model is a cerebellar network, that translates a complex movement trajectory, such as reaching, and activates the spinal reflex circuits to execute the movement. The mapping between the cerebellar neurons and the spinal reflex circuits are trained with artificial neural networks. Using a musculoskeletal arm model, we demonstrate that the proposed neural motor control model can generate the movement to arbitrary goal position.

EPFL2019

The role of sensorimotor afferent feedback in mediating functional recovery after spinal cord injury

Kay Alexander Bartholdi

Spinal cord injury (SCI) leads to permanent deficits in sensory and motor function due to the physical disruption of descending and ascending pathways. As a consequence, spinal circuits below the level of lesion remain in an intact, but inactive state. A number of studies have shown that epidural electrical stimulation (EES) of the lumbar spinal cord can reactivate these spinal circuits after SCI. EES reversed paralysis of lower limbs in rodent and primate models of SCI as well as in a number of clinical case studies. However, although the positive effects of EES have been documented, little is known about the neural circuits through which EES enables motor pattern formation after SCI. In the first part of this thesis, we examined the neural circuits activated by EES. In the past, modeling experiments highlighted a pivotal role for large-diameter, myelinated afferent circuits in mediating the facilitating effects of EES after SCI. Using calcium imaging and chemogenetic inactivation experiments, we confirmed that EES primarily recruits proprioceptive and low-threshold mechanoreceptor feedback circuits. We next sought to exploit this knowledge to specifically enhance the effects of EES. We found that alpha2a receptors are prominently expressed on proprioceptive afferent neurons in DRGs, whereas alpha2c receptors are located on low-threshold mechanoreceptor interneurons in the dorsal spinal horn. Based on this knowledge, we built a computational model that identified beneficial and detrimental interactions between EES and noradrenergic circuits. This understanding guided the design of a circuit-specific, electrochemical neuromodulation that specifically gated the effects of EES towards proprioceptive feedback circuits and re-established locomotion in paralyzed mice and rats. The second part of this thesis uncovers the impact of movement on sensory afferent pathways. For this, mice were exposed to environmental enrichment (EE) or standard housing (SH). We found that EE primed sensory dorsal root ganglia neurons, inducing a lasting increase in their regenerative potential after SCI. This EE-mediated increase in regenerative potential was confined to proprioceptive afferent neurons and characterized through increased calcium signalling and CBP-dependent histone acetylation which is associated with enhanced gene expression. In a next step, we mimicked the exposure to EE using pharmacology. The pharmacological activation of CBP after injury enhanced axonal growth of proprioceptive afferent fibers and enhanced the recovery of sensory and motor functions. Here, we provide direct and conclusive experimental evidence for the mechanistic understanding of EES and EE and how they mediate functional recovery after SCI. Based on this knowledge, we identified specific receptors and epigenetic mechanisms for pharmacological modulation. Our findings demonstrate the importance of large-diameter afferent fibers in mediating functional recovery and will be important in establishing a framework for the design of targeted neuromodulation therapies after SCI in human patients.

EPFL2019

The role of sensorimotor afferent feedback in mediating functional recovery after spinal cord injury

Kay Alexander Bartholdi

EPFL2019