Publications associées à Task-driven neural network models predict neural dynamics of proprioception

Publications associées (26)

Deep-learning models of the ascending proprioceptive pathway are subject to illusions

Olaf Blanke, Alexander Mathis, Merkourios Simos, Adriana Perez Rotondo, Florian Axel Raphaël David

Proprioception is essential for perception and action. Like any other sense, proprioception is also subject to illusions. In this study, we model classic proprioceptive illusions in which tendon vibrations lead to biases in estimating the state of the body ...

Wiley2025

Reward signals in the motor cortex: from biology to neurotechnology

Solaiman Shokur, Pierre Theopistos Vassiliadis

Over the past decade, research has shown that the primary motor cortex (M1), the brain's main output for movement, also responds to rewards. These reward signals may shape motor output in its final stages, influencing movement invigoration and motor learni ...

NATURE PORTFOLIO2025

Effect of Medication and Freezing of Gait on Rambling and Trembling in Quiet Standing in Individuals With Parkinson's Disease

Mohamed Bouri, Solaiman Shokur

Background: Individuals with Parkinson's disease (PD) with freezing of gait (FoG) exhibit difficulty with changes in sensory input, indicating a potential sensorimotor integration deficit. Understanding how levodopa impacts balance particularly in FoG, is ...

HUMAN KINETICS PUBL INC2025

Effect of Extrinsic Reward on Motor Plasticity during Skill Learning

Friedhelm Christoph Hummel, Pierre Theopistos Vassiliadis

Human motor skill acquisition is improved by performance feedback, and coupling such feedback with extrinsic reward (such as money) can enhance skill learning. However, the neurophysiology underlying such behavioral effect is unclear. To bridge this gap, w ...

2025

A Reconfigurable High-Dynamic Range ∆Σ Front-End with Event-Based Decimation for Bandwidth-Efficient Implantable Neural Interfaces

David Atienza Alonso, Giovanni Ansaloni, Pasquale Davide Schiavone, Juan Pablo Sapriza Araujo

As the demand for high channel counts and high-resolution recordings of neural activity continues to grow, the increased power and data rate generated impose hard constraints on the telemetry capabilities of wireless implantable neural interfaces. To addre ...

IEEE2025

Towards personalized mapping through lumbosacral spinal cord task fMRI

Dimitri Nestor Alice Van De Ville, Nawal Noëlle Kinany, Ilaria Ricchi

The lumbosacral spinal cord contains neural circuits crucial for locomotion, organized into rostrocaudal levels with distinct somatosensory and motor neuron pools that project to and from the muscles of the lower limbs. However, the specific spinal levels ...

2025

t-PiNet: A thermodynamics-informed hierarchical learning for discovering constitutive relations of geomaterials

Konstantinos Karapiperis

More attention has been paid to integrating existing knowledge with data to understand the complex mechanical behaviour of geomaterials, but it incurs scepticism and criticism on its generalizability and robustness. Moreover, a common mistake in current da ...

2025

Perspective on nonvolatile magnon-signal storage and in-memory computation for low-power consuming magnonics

Arnaud Emile S. Nizet, Dirk Grundler, Andrea Mucchietto, Mingran Xu, Shreyas Sanjay Joglekar

Magnons are the quanta of spin waves and transport angular momenta through magnetically ordered materials. They can be used to distribute and control on-chip GHz signals without charge flow, thereby avoiding Joule heating. Beyond multiplexed signal process ...

AIP Publishing2025

Machine Learning Applications to Conditional Asset Pricing: Attention, CNNs, and Neural Tangent Kernels

Boris Kuznetsov

The first two chapters develop ML-based models that generate conditional SDF estimates efficiently using conditioning information sets, resulting in minimal pricing errors and superior Sharpe ratios compared to traditional conditional asset pricing models. ...

EPFL2025

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Ramin Mohammadi, Nikolaos Stergiopoulos, Georgios Rovas, Lydia Aslanidou, Sokratis Anagnostopoulos

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

2025

Unraveling behavior and cortical signals to guide the development of soft neuroprostheses for auditory restoration and spreading depolarization

Emilie Cornelia Maria Revol

Neuroprostheses have been used clinically for decades, to help restore or preserve brain functions, when pharmaceutical treatments are inefficient. Although great progress in the field has been made over the years to interface with the nervous system, surf ...

EPFL2024

Enhancement of brain atlases with region-specific coordinate systems: flatmaps and barrel column annotations

Felix Schürmann, Michael Reimann, Armando Romani, Huanxiang Lu, Sirio Bolaños Puchet, Juan Bautista Hernando Vieites, Aleksandra Zuzanna Teska

Digital brain atlases define a hierarchy of brain regions and their locations in three-dimensional space. They provide a standard coordinate system in which diverse datasets can be integrated for visualization and analysis. They also enable building of dat ...

2024

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy

Henry Markram, Kathryn Hess Bellwald, Felix Schürmann, Daniel Keller, Eilif Benjamin Muller, Michael Reimann, Werner Alfons Hilda Van Geit, Lida Kanari, Srikanth Ramaswamy, Matthias Wolf, James Gonzalo King, Alexis Arnaudon, Ying Shi, András Ecker, Jean-Denis Georges Emile Courcol, Armando Romani, Pramod Shivaji Kumbhar, Judit Planas Carbonell, Michael Emiel Gevaert, Fernando Joaquim Leite Pereira, Liesbeth Maria L Vanherpe, Huanxiang Lu, Daniela Egas Santander, James Bryden Isbister, Ran Levi, Benoît Jean-Albert Coste, Samuel Lieven D. Lapere, Sirio Bolaños Puchet, Juan Bautista Hernando Vieites, Cyrille Pierre Henri Favreau, Joni Henrikki Herttuainen, Christoph Pokorny, Vishal Sood, Thomas Brice Delemontex, Adrien Michel Achille Devresse, Nicolas René Jean Ninin, Alexander Dietz, Hugo Thabo Dictus, Gianluca Ficarelli

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, hierarchical, interregional structure with a highly specific laminar architecture. The last decade has seen the emergence of extensive new ...

2024

Neural Exponential Stabilization of Control-affine Nonlinear Systems

Giancarlo Ferrari Trecate, Muhammad Zakwan

This paper proposes a novel learning-based approach for achieving exponential stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics (CCMs) framework to co-synthesize Neural Contraction Metrics (NCMs) and Neural Netw ...

Institute of Electrical and Electronics Engineers Inc.2024

DATA-DRIVEN INVERSE DESIGN METHOD FOR TURBOMACHINERY

Fernando Perez Cruz, Luis Salamanca Miño, Firat Ozdemir

A data-driven inverse design method based on neural networks is proposed for turbomachinery. In the devised methodology, design parameters are provided as input to the neural network, and performance attributes, e.g. efficiency, as output. Once trained, th ...

American Society of Mechanical Engineers (ASME)2024

A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays

Henry Markram, Werner Alfons Hilda Van Geit, Tanguy Pierre Louis Damart, Darshan Mandge, Aurélien Tristan Jaquier, Mickael Maurice Zbili

In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under diff ...

2024

Robust time series denoising with learnable wavelet packet transform

Olga Fink, Gaëtan Michel Frusque

Noise in the data is one of the main cause of model performance drop. Denoising is therefore a critical step in most data pipelines. In this paper we propose to fuse the learning abilities of Neural Network with the well-proven efficiency of the wavelet pa ...

2024

Uncovering interactions between Descending Neurons as a functional principle of behavioural control

Jonas Friedrich Braun

Animals, including humans, exhibit a remarkable variety of complex behaviours. How the nervous system controls all these behaviours ranging from simple, stereotyped movements to flexible, adaptive actions is a central questions of neuroscience. One of the ...

EPFL2024

Fast learning without synaptic plasticity in spiking neural networks

Guillaume Emmanuel Fernand Bellec

Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to r ...

Nature Portfolio2024

Nissl_4, Raw images for Machine learning for histological annotation and quantification of cortical layers

Julie Meystre

This dataset contains some images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pi ...

Zenodo2024