Topological exploration of artificial neuronal network dynamics

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Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

This paper introduces MNE-RT, a Python package designed for real-time neural feature extraction from magne-toencephalography (MEG) and electroencephalography (EEG) signals in Brain-Computer Interface (BCI) systems. The package incorporates efficient algori ...

Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

Previous studies have demonstrated that performance under selective attention and divided attention can be enhanced or impaired, depending on whether the stimuli from different modalities are the same or different. However, it remains unclear whether the m ...

This thesis explores the how computational methods can be used to enrich the history of architecture. Using Venice as a case study, it establishes a rich and multifaceted information system, made up of temporal, stylistic, photographic, architectural, and ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Thermal scene reconstruction holds great potential for various applications, such as building energy analysis and non-destructive infrastructure testing. However, existing methods rely on dense scene measurements and use RGB images for 3D reconstruction, i ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

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, ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Topological exploration of artificial neuronal network dynamics

Chattez avec Graph Search

Enabling Uncertainty Estimation in Iterative Neural Networks

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Advances on Real Time M/EEG Neural Feature Extraction

The neural correlates of topographical disorientation-a lesion analysis study

The modulation of selective attention and divided attention on cross-modal congruence

The Skin of Venice: A Computational Approach to the City's Urban Structure

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

ThermoNeRF: A multimodal Neural Radiance Field for joint RGB-thermal novel view synthesis of building facades

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

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

Robust machine learning for neuroscientific inference