Publications related to Neural decoding and feature selection methods for closed-loop control of avoidance behavior

Related publications (19)

Oscillatory Traveling Waves Provide Evidence for Predictive Coding Abnormalities in Schizophrenia

Michael Herzog, Dario Alejandro Gordillo Lopez

Background: The computational mechanisms underlying psychiatric disorders are hotly debated. One hypothesis, grounded in the Bayesian predictive coding framework, proposes that patients with schizophrenia have abnormalities in encoding prior beliefs about ...

2025

Temporally-Consistent Surface Reconstruction Using Metrically-Consistent Atlases

Pascal Fua, Mathieu Salzmann, Shaifali Parashar, Jan Bednarík

We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. It yields dense and semantically meaningful correspondences between frames. We represent the reconstructed su ...

2025

An Analysis of Model Robustness across Concurrent Distribution Shifts

Myeongho Jeon, Shuqing Teresa Yeo

Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occ ...

2025

Low-Rank Adaptation for Transformers in Dynamic Hand Gesture Recognition Using High-Density Surface Electromyography

Xingce Bao

Dynamic hand gesture recognition (HGR) is critical for real-time prosthetics and neural robotics applications, where high-density surface electromyography (HD-sEMG) signals provide valuable insights into muscle activity. However, adapting machine learning ...

IEEE2025

Learning Algorithms in the Limit

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov

This paper studies the problem of learning computable functions in the limit by extending Gold's inductive inference framework to incorporate computational observations and restricted input sources. Complimentary to the traditional Input-Output Observation ...

2025

Fast-adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging

Chong Liu

Lattice light-sheet microscopy provides a crucial observation window into intra- and intercellular physiology of living specimens but at the diffraction-limited resolution or anisotropic super-resolution with structured illumination. Here we present meta-l ...

Nature Publishing Group2025

Advances in Algorithms for Sampling, Optimization and Inference in Disordered Systems

Giovanni Piccioli

This thesis focuses on the development of advanced algorithmic techniques, primarily Markov Chain Monte Carlo (MCMC) methods, and message passing algorithms, to tackle high-dimensional optimization and inference problems. The algorithms used have a probabi ...

EPFL2024

Multi-Label Generalized Zero Shot Chest X-Ray Classification by Combining Image-Text Information With Feature Disentanglement

Seyedbehzad Bozorgtabar

In fully supervised learning-based medical image classification, the robustness of a trained model depends on its exposure to various disease classes. Generalized Zero Shot Learning (GZSL) aims to predict both seen and novel unseen classes. While most GZSL ...

Institute of Electrical and Electronics Engineers2024

Nissl_1, Raw images for Machine learning for histological annotation and quantification of cortical layers

Julie Meystre

This dataset contains 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 Pike F5 ...

Zenodo2024

Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high-spatial resolution

David Pascucci, Joan Rue Queralt

Connectome spectrum electromagnetic tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and t ...

Wiley2024

Accelerated design of nickel-cobalt based catalysts for CO2 hydrogenation with human-in-the-loop active machine learning

Andreas Züttel, Yasemen Kuddusi

Thermo-catalytic conversion of CO2 into more valuable compounds, such as methane, is an attractive strategy for energy storage in chemical bonds and creating a carbon-based circular economy. However, designing heterogeneous catalysts remains a challenging, ...

2024

Optimal performance of Graph Convolutional Networks on the Contextual Stochastic Block Model

Patrick Thiran, Guillaume Cyprien Dalle

For Graph Neural Networks, oversmoothing denotes the homogenization of vertex embeddings as the number of layers increases. To better understand this phenomenon, we study community detection with a linearized Graph Convolutional Network on the Contextual S ...

2024

Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

Volkan Cevher, Grigorios Chrysos, Igor Krawczuk, Justin Samuel Deschenaux

Denoising Diffusion Probabilistic Models (DDPMs) exhibit remarkable capabilities in image generation, with studies suggesting that they can generalize by composing latent factors learned from the training data. In this work, we go further and study DDPMs t ...

2024

Ground-based image deconvolution with Swin Transformer UNet

Frédéric Courbin, Pascale Jablonka, Utsav Akhaury

Aims. As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these imag ...

2024

Capturing Dichotomic Solvent Behavior in Solute−Solvent Reactions with Neural Network Potentials

Anne-Clémence Corminboeuf, Matthew Wodrich, Shubhajit Das, Veronika Juraskova, Frédéric Célerse

Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the r ...

American Chemical Society (ACS)2024

On the modelling of the fatigue-induced damage in Francis turbines start-up sequences

Elena Vagnoni, Martin Seydoux, Ekaterina Krymova, Alessandro Morabito

The European Green Deal is prescribing a very ambitious rise in the annual production of renewable energy sources, and grid regulation services are increasingly important in the course of a massive integration of intermittent renewable energy sources. Alth ...

IOP Publishing2024

Changes in spinal motor behaviour are associated with reduction in disability in chronic low back pain: A longitudinal cohort study with 1-year follow-up

Brigitte Jolles-Haeberli

Background: The need to improve spinal motor behaviour in chronic low back pain (CLBP) rehabilitation remains unclear. The objective of this study was to test if changes in spinal motor behaviour were associated with changes in disability after an interdis ...

2024

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields: Processed data and trained models

Mathieu Salzmann, Alexander Mathis, Chen Zhao, Haozhe Qi

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024 Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis. Affiliation: EPFL Date: June, 2024 Link to the CVPR article: https://openaccess.thecvf.com/conte ...

Zenodo2024

Direct imaging of neuronal activity: a multivariate perspective on DIANA data in human subjects

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Functional magnetic resonance imaging (fMRI) relies upon the blood-oxygenation-level-dependent (BOLD) signal as a proxy for neuronal activity. Whole-brain coverage and millimetric resolution have propelled this modality as a workhorse for human neuroscienc ...

European Signal Processing Conference, EUSIPCO2024