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

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Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

This article summarizes the recent advancements in the design, fabrication, and control of microrobotic devices for the diagnosis and treatment of brain disorders. With a focus on diverse actuation methods, we discuss how advancements in materials science ...

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

Background :Sensory reafferents are crucial to correct our posture and movements, both reflexively and in a cognitively driven manner. They are also integral to developing and maintaining a sense of agency for our actions. In cases of compromised reafferen ...

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

The decline of motor ability is a hallmark feature of aging and is accompanied by degeneration of motor synaptic terminals. Consistent with this, Drosophila motor synapses undergo characteristic age -dependent structural fragmentation co -incident with dim ...

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

Chattez avec Graph Search

Optimizing Dynamic Aperture Studies with Active Learning

Topics in statistical physics of high-dimensional machine learning

Enabling Uncertainty Estimation in Iterative Neural Networks

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Small-scale robotic devices for medical interventions in the brain

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

Augmenting locomotor perception by remapping tactile foot sensation to the back

Sparse autoregressive neural networks for classical spin systems

Driving and suppressing the human language network using large language models

Gibbs sampling the posterior of neural networks

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Machine Learning for Modeling Stock Returns

Trio preserves motor synapses and prolongs motor ability during aging