Publications associées à Precision non-invasive brain stimulation: an in silico pipeline for personalized control of brain dynamics

Publications associées (19)

Revealing Brain and Spinal Cord Functional Networks by Block-Structured Graph Laplacian Mixture Models

Pascal Frossard, Dimitri Nestor Alice Van De Ville, Ilaria Ricchi, Ekansh Sareen, William Cappelletti

The Graph Laplacian Mixture Model (GLMM) allows to infer multiple underlying graph structures from multivariate time series data. Given its effectiveness in identifying brain states from brain activity measured by functional magnetic resonance imaging (fMR ...

IEEE2025

NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability

Alexandre Massoud Alahi, Krishna Kanth Nakka

The generation of transferable adversarial perturbations typically involves training a generator to maximize embedding separation between clean and adversarial images at a single mid-layer of a source model. In this work, we build on this approach and intr ...

IEEE2025

Graph Neural Networks With Adaptive Structures

Zepeng Zhang

Graph neural networks (GNNs) have made significant progress in various machine learning tasks. Despite their success, many existing GNN models are shown to be vulnerable to adversarial attacks, creating a stringent need to build robust GNN architectures. I ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Tutorial: Intelligent Neural Interfaces: Fundamentals and Future Directions

Mahsa Shoaran

In the past decade, there has been growing interest in developing AI-enabled neural interfaces for various neurological disorders and emerging brain-machine interface (BMI) applications. The focus has shifted from raw signal acquisition and data compressio ...

IEEE2025

Intelligent and Self-Driving Microscopy of Protein Aggregation in Neurodegenerative Diseases

Khalid Ashraf Mohie Ibrahim

Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's, afflict tens of millions of patients worldwide. They are characterized by protein aggregation and progressive neuronal loss, leading to cognitive and motor impairments, and ult ...

EPFL2025

Temporal Fluctuations of Brain Structure-Function Coupling as a Potential Marker of Drowsiness During Resting State

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Resting-state functional magnetic resonance imaging (fMRI) is a well-established methodology to probe brain organization. Previous work has demonstrated that the probability of drowsiness and even sleep increases significantly in participants during restin ...

IEEE2024

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

Dimitri Nestor Alice Van De Ville, Ayberk Ozkirli

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizi ...

Nature Portfolio2024

Hierarchical syntax model of music predicts theta power during music listening

Martin Alois Rohrmeier, Steffen Alexander Herff, Gabriele Cecchetti

Linguistic research showed that the depth of syntactic embedding is reflected in brain theta power. Here, we test whether this also extends to non-linguistic stimuli, specifically music. We used a hierarchical model of musical syntax to continuously quanti ...

2024

Bridging Discrete Choice Modeling and Neural Networks: Unifying Statistical Rigor and Predictive Power

Brian Alan Tappy-Sifringer

This thesis explores the innovative integration of Discrete Choice Modeling (DCM) and Machine Learning (ML), specifically Neural Networks (NN), within the transportation sector. DCM, with its highly interpretable and hand-designed models grounded in robust ...

EPFL2024

Neural Distributed Controllers with Port-Hamiltonian Structures

Giancarlo Ferrari Trecate, Muhammad Zakwan

Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly ...

Institute of Electrical and Electronics Engineers Inc.2024

Machine learning potential for the Cu-W system

William Curtin

Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix composites and nano-multilayers are finding applications as brazing fillers and shieldin ...

2024

Distributed Machine Learning Targeting Embedded Systems for Epilepsy Detection

Saleh Baghersalimi

Epileptic seizure detection and monitoring is critical in healthcare, particularly for individuals requiring continuous oversight. Current methods, primarily based on electroencephalogram (EEG) technologies, face limitations due to their complexity in term ...

EPFL2024

PyTorch 2 Model for the prediction of single-artery segmentations for "AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation"

Edward Carlo Giorgio Andò, Thabodhan Mahendiran

These are pytorch model weights for the angiopy-segmentation deep learning model, descriped in a paper published in 2024 in the International Journal of Cardiology: AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery se ...

Zenodo2024

NoiseNCA: Noisy Seed Improves Spatio-Temporal Continuity of Neural Cellular Automata

Sabine Süsstrunk, Ehsan Pajouheshgar, Yitao Xu

Neural Cellular Automata (NCA) is a class of Cellular Automata where the update rule is parameterized by a neural network that can be trained using gradient descent. In this paper, we focus on NCA models used for texture synthesis, where the update rule is ...

MIT Press2024

Quantum Variational Algorithms: Exploring Applications and Potentials Amid Trainability and Classical Simulability Limitations

Su Yeon Chang

Since its proposal, quantum computing has made significant strides in various domains. Among the different emerging techniques, Variational Quantum Algorithms (VQAs) have become one of the most promising approaches in the current era, where quantum computa ...

EPFL2024

Cochlear implantation in adults with acquired single-sided deafness improves cortical processing and comprehension of speech presented to the non-implanted ears: a longitudinal EEG study

Patrick Karl Alois Neff

Former studies have established that individuals with a cochlear implant (CI) for treating single-sided deafness experience improved speech processing after implantation. However, it is not clear how each ear contributes separately to improve speech percep ...

Oxford University Press2024

Facilitated machine learning for image-based fruit quality assessment

Fernando Perez Cruz

Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult due to the lack of centralization and automation of postharvest supply chai ...

2023

Analysis of Large Market Data Using Neural Networks: A Causal Approach

Damir Filipovic, Negar Kiyavash, Marc-Aurèle Antoine Divernois

We develop a data-driven framework to identify the interconnections between firms using an information-theoretic measure. This measure generalizes Granger causality and is capable of detecting nonlinear relationships within a network. Moreover, we develop ...

2023

Stability Verification of Neural Network Controllers using Mixed-Integer Programming

Colin Neil Jones, Daniel Kuhn, Roland Schwan

We propose a framework for the stability verification of Mixed-Integer Linear Programming (MILP) representable control policies. This framework compares a fixed candidate policy, which admits an efficient parameterization and can be evaluated at a low comp ...

2023