Fuseau neuromusculaire | EPFL Graph Search

Le fuseau neuromusculaire est un mécanorécepteur constitué de fibres musculaires modifiées. Disposé parallèlement aux fibres du muscle, il est sensible à l'allongement de celui-ci, et traduit un stimulus mécanique en un message nerveux. Rôles Ces fuseaux sont des récepteurs sensoriels. Ils jouent donc un rôle important dans la proprioception statique, ainsi que dans la kinesthésie. Ils sont également impliqués dans le réflexe myotatique : leur rôle est alors d'augmenter le niveau de contraction du muscle en réponse à son propre étirement. Le muscle tend à revenir à sa longueur initiale. Le fuseau neuromusculaire est donc sensible aux mouvements du muscle dans lequel il est situé. Il peut fournir au cerveau des informations sur l'activité et la tension du muscle sous forme d'impulsion électrique. Le fuseau neuromusculaire favorise également la contraction du muscle. Constitution Chaque fuseau est constitué de fibres intrafusales dont la partie polaire (part

Towards goal-driven deep neural network models to elucidate human arm proprioception

Pranav Mamidanna, Alexander Mathis

Proprioceptive signals are a critical component of our ability to perform complex movements, identify our posture and adapt to environmental changes. Our movements are generated by a large number of muscles and are sensed via a myriad of different receptor types. Even the most important ones, muscle spindles, carry highly multiplexed information. For instance, arm movements are sensed via distributed and individually ambiguous activity patterns of muscle spindles, which depend on relative joint configurations rather than the absolute hand position. This high dimensional input (~50 muscles for a human arm) of distributed information poses a challenging decoding problem for the nervous system. Given the diversity in muscle activity, what are the necessary computations that the proprioceptive system needs to perform to sense our movements? Here we studied a proprioceptive variant of the handwritten character recognition task to gain insight into potential computations that the proprioceptive system needs to perform. We focussed on handwritten character classification of muscle length configuration patterns that were required to draw that character. We started from a dataset comprising of pen-tip trajectory data recorded while subjects were writing individual single-stroke characters of the Latin alphabet (Williams et al. ICANN 2006), and employed a musculoskeletal model of the human upper limb to generate muscle length configurations corresponding to drawing the pen-tip trajectories in multiple horizontal and vertical planes. Using this model we created a large, scalable dataset of muscle length configurations corresponding to handwritten characters of varying sizes, locations and orientations (n > 105 samples). To determine the difficulty of this problem, we trained support vector machines (SVM) to solve a binary one-vs-all classification task on the dataset, which achieves an accuracy of 0.89 ± 0.08 (mean ± s.e.m). Contrary to naive expectation, reading out the character at the level of muscles is much easier whereas SVMs trained on the same task using pen-tip coordinates performed relatively poorly: 0.75 ± 0.14. This suggests that the musculoskeletal system itself serves as a non-linear projection to a higher dimensional space, which simplifies character recognition. Next we focussed on goal-driven deep neural network architectures to achieve higher accuracy. Training deep neural networks requires a large, diverse datasets and challenging tasks. We found that the scalable dataset for character recognition we generated is large enough to constrain deep convolutional architectures. We are currently exploring the performance of different deeplearning architectures in solving the handwritten character classification task to investigate which representations are learned and what computations are most efficient. We found that convolutional neural networks factoring out temporal and inter-muscle ('spatial') information achieve almost perfect accuracy for the multi-class problem. These preliminary results suggest that neural networks can learn pose-invariant character recognition from muscle configurations.

2018

Muscle Spindle Feedback Directs Locomotor Recovery and Circuit Reorganization after Spinal Cord Injury

Grégoire Courtine, Isabel Laura Vollenweider

Spinal cord injuries alter motor function by disconnecting neural circuits above and below the lesion, rendering sensory inputs a primary source of direct external drive to neuronal networks caudal to the injury. Here, we studied mice lacking functional muscle spindle feedback to determine the role of this sensory channel in gait control and locomotor recovery after spinal cord injury. High-resolution kinematic analysis of intact mutant mice revealed proficient execution in basic locomotor tasks but poor performance in a precision task. After injury, wildtype mice spontaneously recovered basic locomotor function, whereas mice with deficient muscle spindle feedback failed to regain control over the hindlimb on the lesioned side. Virus-mediated tracing demonstrated that mutant mice exhibit defective rearrangements of descending circuits projecting to deprived spinal segments during recovery. Our findings reveal an essential role for muscle spindle feedback in directing basic locomotor recovery and facilitating circuit reorganization after spinal cord injury.

Elsevier2014

Towards goal-driven deep neural network models to elucidate human arm proprioception

Pranav Mamidanna, Alexander Mathis

2018