Publications associées à The neuropsychology and neuroanatomy of reduplicative paramnesia

Publications associées (20)

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

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 ...

OXFORD UNIV PRESS2025

Learning to Reason with Neural Networks: Principles and Methods

Aryo Lotfi Jandaghi

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 ...

EPFL2025

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

Giuseppe Carleo, Jannes Willy E. Nys, Imelda Romero

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 ...

NATURE PORTFOLIO2025

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

Jan Steggemann, Anna Mascellani, Joanna Malgorzata Wanczyk

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 ...

SPRINGER2025

Human self and Neurosurgery: Advances and insights from Geneva

Olaf Blanke, Bruno Herbelin

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, ...

2025

A Longitudinal and Reproducible Anti-coactivation Pattern Between the Cerebellum and the Ventral Tegmental Area Relates to Apathy in Schizophrenia

Dimitri Nestor Alice Van De Ville, Farnaz Delavari

Negative symptoms of schizophrenia lack effective treatments. Anomalies in the reward system and cerebellum have been linked to these symptoms. The cerebellum modulates reward circuitry via the ventral tegmental area (VTA). The "cognitive dysmetria theory" ...

Elsevier BV2025

Acute optogenetic induction of the prodromal endophenotype of CA1 hyperactivity causes schizophrenia-related deficits in cognition and salience attribution

Christina Maria Grimm

Hyperactivity of the human anterior hippocampus has been reported to spread from its CA1 subfield to the subiculum around the onset of first-episode psychosis and could be a cellular target for early therapeutic intervention in the schizophrenia prodrome. ...

NATURE PORTFOLIO2024

Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Treatment-resistant depression is a severe form of major depressive disorder and deep brain stimulation is currently an investigational treatment. The stimulation's therapeutic effect may be explained through the functional and structural connectivities be ...

Springer Heidelberg2024

Embodiment in episodic memory through premotor-hippocampal coupling

Olaf Blanke, Bruno Herbelin, Jevita Potheegadoo, Baptiste Gauthier, Nathalie Heidi Meyer, Florian Lance, Mariana Babo Rebelo, Sara Stampacchia, Juliette Marie Elise Jocelyne Boscheron

Episodic memory (EM) allows us to remember and relive past events and experiences and has been linked to cortical-hippocampal reinstatement of encoding activity. While EM is fundamental to establish a sense of self across time, this claim and its link to t ...

Springer Science and Business Media LLC2024

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Weina Ji, Henry Markram, Felix Schürmann, Daniel Keller, Eilif Benjamin Muller, Michael Reimann, Werner Alfons Hilda Van Geit, Srikanth Ramaswamy, Matthias Wolf, James Gonzalo King, Alexis Arnaudon, András Ecker, Rajnish Ranjan, Jean-Denis Georges Emile Courcol, Armando Romani, Pramod Shivaji Kumbhar, Judit Planas Carbonell, Giuseppe Chindemi, Michael Emiel Gevaert, Christian Andreas Rössert, Fernando Joaquim Leite Pereira, Omar Awile, Mustafa Anil Tuncel, Daniela Egas Santander, James Bryden Isbister, Natali Barros Zulaica, Samuel Lieven D. Lapere, Sirio Bolaños Puchet, Maria Reva, Genrich Ivaska, Tanguy Pierre Louis Damart, Darshan Mandge, Joni Henrikki Herttuainen, Christoph Pokorny, Elvis Boci, Vishal Sood, Thomas Brice Delemontex, Aleksandra Zuzanna Teska, Polina Litvak, Alexander Dietz, Jorge Blanco Alonso, Gianluca Ficarelli

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a m ...

2024

High-performance deep spiking neural networks with 0.3 spikes per neuron

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Guillaume Emmanuel Fernand Bellec, Ana Stanojevic, Angeliki Pantazi

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical resul ...

Springer Science and Business Media LLC2024

Sparse autoregressive neural networks for classical spin systems

Giuseppe Carleo, Dian Wu, Indaco Biazzo

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Iop Publishing Ltd2024

Auditory stimuli suppress contextual fear responses in safety learning independent of a possible safety meaning

Elena Lidia Jeanine Mombelli, Ralf Schneggenburger, Olexiy Kochubey, Johanni Michael Brea, Denys Osypenko, Shriya Palchaudhuri, Christos Sourmpis

Safety learning allows the identification of non-threatening situations, a learning process instrumental for survival and psychic health. In contrast to fear learning, in which a sensory cue (conditioned stimulus, CS) is temporally linked to a mildly avers ...

2024

Using Physical Dynamics: Accurate and Real-Time Object Detection for High-Resolution Video Streaming on Internet of Things Devices

Yung-Cheng Chiang

Object detection is crucial in video analytics pipelines, but there is a need to optimize deep neural networks (DNNs)-based object detection for resource-constrained Internet of Things (IoT) devices. The computational constraints inherent to the IoT device ...

2024

Accelerating Dynamic Aperture Evaluation Using Deep Neural Networks

Tatiana Pieloni, Mike Seidel, Davide Di Croce

The Dynamic Aperture is an important concept for the study of non-linear beam dynamics in a circular accelerator. The DA is defined as the extent of the phase-space region in which the particle's motion remains bounded over a finite number of turns. Such a ...

Institute of Physics2024

Homological Landscape of Human Brain Functional Sub-Circuits

Enrico Amico

Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., a set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. ...

2024

Longitudinal Analysis of Brain Function-Structure Dependencies in 22q11.2 Deletion Syndrome and Psychotic Symptoms

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Background: Compared with conventional unimodal analysis, understanding how brain function and structure relate to one another opens a new biologically relevant assessment of neural mechanisms. However, how function-structure dependencies (FSDs) evolve thr ...

2024

Learning-based Hierarchical Control: Emulating the Central Nervous System for Bio-Inspired Legged Robot Locomotion

Auke Ijspeert, Guillaume Denis Antoine Bellegarda, Milad Shafiee Ashtiani

Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in the spinal cord, and musculoskeletal system. Traditional bioinspired control f ...

Institute of Electrical and Electronics Engineers Inc.2024

Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes

Pascal Fua, Mathieu Salzmann, Andrey Davydov

When enough annotated training data is available, supervised deep-learning. algorithms excel at estimating human body pose and shape using a single camera. The effects of too little such data being available can be mitigated by using other information sour ...

Journal of Machine Learning Research Inc.2024

Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting

Ana-Maria Cretu

Machine learning models have been shown to leak sensitive information about their training datasets. Models are increasingly deployed on devices, raising concerns that white-box access to the model parameters increases the attack surface compared to black- ...

Privacy Enhancing Technologies Symposium Advisory Board2024