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

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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

Chattez avec Graph Search

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

NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability

Graph Neural Networks With Adaptive Structures

Tutorial: Intelligent Neural Interfaces: Fundamentals and Future Directions

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

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

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

Hierarchical syntax model of music predicts theta power during music listening

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

Neural Distributed Controllers with Port-Hamiltonian Structures

Machine learning potential for the Cu-W system

Distributed Machine Learning Targeting Embedded Systems for Epilepsy Detection

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"

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

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

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

Facilitated machine learning for image-based fruit quality assessment

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

Stability Verification of Neural Network Controllers using Mixed-Integer Programming