Muscle activities in similar arms performing identical tasks reveal the neural basis of muscle synergies

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

Objective: We predicted that accelerometry would be a viable alternative to electromyography (EMG) for assessing fundamental Transcranial Magnetic Stimulation (TMS) measurements (e.g. Resting Motor Threshold (RMT), recruitment curves, latencies). New Metho ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, ...

Upper-limb occupational exoskeletons to support the workers' upper arms are typically designed to provide antigravitational support. Although typical work activities require workers to perform static and dynamic actions, the majority of the studies in lite ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

Among the inherited myopathies, a group of muscular disorders characterized by structural and metabolic impairments in skeletal muscle, Duchenne muscular dystrophy (DMD) stands out for its devastating progression. DMD pathogenesis is driven by the progress ...

Electrical stimulation of the nervous system has emerged as a promising assistive technology in case of many injuries and illnesses across various parts of the nervous system. In particular, the invasive neuromodulation of the peripheral nervous system see ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

IntroductionThe current assessment and standardization of microsurgical skills are subjective, posing challenges in reliable skill evaluation. We aim to address these limitations by developing a quantitative and objective framework for accurately assessing ...

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

Muscle activities in similar arms performing identical tasks reveal the neural basis of muscle synergies

Chat with Graph Search

Context selectivity with dynamic availability enables lifelong continual learning

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Accelerometry as a tool for measuring the effects of transcranial magnetic stimulation

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

Evaluation of antigravitational support levels provided by a passive upper-limb occupational exoskeleton in repetitive arm movements

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Fast adaptation to rule switching using neuronal surprise

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

The neural correlates of topographical disorientation-a lesion analysis study

Safe Deep Neural Networks

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Cyclo His-Pro Attenuates Muscle Degeneration in Murine Myopathy Models

Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

An objective skill assessment framework for microsurgical anastomosis based on ALI scores

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes