A dynamic attractor network model of memory formation, reinforcement and forgetting

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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 ...

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 ...

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, ...

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 ...

This chapter offers a new view of post-Soviet Ukrainain literary memory as expressed in varous texts, redefining the role of clashing narratives of the past under the divisive political and social conditions of upheavals, crises, and military conflicts. Th ...

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 ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

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 ...

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

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 ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

A search is presented for the pair production of new heavy resonances, each decaying into a top quark (t) or antiquark and a gluon (g). The analysis uses data recorded with the CMS detector from proton-proton collisions at a center-of-mass energy of 13 TeV ...

Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliability of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid err ...

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 this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

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 ...

This study aims to determine whether 1) individuals with treatment-resistant schizophrenia display early cognitive impairment compared to treatment-responders and healthy controls and 2) N-methyl-D-aspartate-receptor hypofunction is an underlying mechanism ...

A dynamic attractor network model of memory formation, reinforcement and forgetting

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Learning to Reason with Neural Networks: Principles and Methods

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

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

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

Ukrainian Literary Imaginaries of Past after 1991: From Substitution to Restoration?

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

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

The neural correlates of topographical disorientation-a lesion analysis study

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Fast adaptation to rule switching using neuronal surprise

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

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

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Search for Pair Production of Heavy Particles Decaying to a Top Quark and a Gluon in the Lepton Plus Jets Final State in Proton-proton Collisions at √s=13 Tev

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Incorporating Projective Geometry into Deep Learning

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

Neurocognition and NMDAR co-agonists pathways in individuals with treatment resistant first-episode psychosis: a 3-year follow-up longitudinal study