A Rapid Form of Offline Consolidation in Skill Learning

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Although classically Rapid-Eye Movement (REM) sleep was thought to generate desynchronized activity similar to wakefulness, it was found that some brain regions could express Slow Wave activity (SWA), a pattern which was normally typical of slow-wave sleep ...

The mammalian brain develops through precisely orchestrated events that transform a simple neural tube into a complex structure. While single-cell RNA sequencing has revealed remarkable cellular heterogeneity, the lack of spatial context limits our underst ...

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

We present the experimental campaigns – namely, three per facility – carried out between 2018 and 2021 in the AKR–2 and CROCUS zero power reactors within the framework of the Horizon 2020 European project CORTEX. Their purpose was to produce high-quality a ...

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

Introduction: Impaired sleep is a global health concern. However, the environmental factors contributing to sleep impairment in urban settings are still not well understood. Methodology: This study involved 179 participants from a Swiss municipality (Yverd ...

Introduction: The preservation of the human self—a fundamental yet underexplored aspect of neurosurgical practice—has gained increasing attention in recent years. Research question: How can neural correlates of self-consciousness be identified, monitored, ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

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 ...

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

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 ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

In the last few years, stroke ranked as the second most common cause of death and is the third most significant condition affecting disability-adjusted life years (DALYs) worldwide. Being the most prevalent and quality of life impacting post-stroke symptom ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

A Rapid Form of Offline Consolidation in Skill Learning

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Experimental and computational analysis of REM sleep distributed cortical activity in mice

Fine-grained mapping of neural progenitors subtypes in the embryonic brain unveils context-dependent roles of signalling on cell fate decisions

Computational design of complex protein folds and soluble analogs of membrane proteins

CORTEX experiments – Part I: Modulation campaigns in AKR-2 & CROCUS for the validation of neutron noise codes

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Cluster-specific urban contexts associated with high levels of sleep impairment and daytime sleepiness: Findings from the Urbasan collaborative study

Human self and Neurosurgery: Advances and insights from Geneva

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Driving and suppressing the human language network using large language models

Learning to Reason with Neural Networks: Principles and Methods

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Optimizing Dynamic Aperture Studies with Active Learning

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Probing and modulating inter-areal coupling in the cortical visual motion processing pathway with non-invasive brain stimulation

Safe Deep Neural Networks