Publications associées à Neural and algorithmic bases of odor guided trail following in mice

Publications associées (21)

Automated Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Traditional aerodynamic shape optimization (ASO) is costly and time-intensive, requiring extensive manual tuning for parameterization to define the design space and manipulate geometries. Conventional parameterization methods limit flexibility and demand s ...

American Institute of Aeronautics and Astronautics (AIAA)2025

Wear across scales

Sacha Zenon Wattel

When two objects are rubbed together, they create friction, heat, and their surfaces might be worn away. However, the fundamental mechanisms behind these processes remain poorly understood. While empirical engineering models exist, they are tailored to spe ...

EPFL2025

Fixed point geometry in chaotic neural networks

Christian Keup

Understanding the high-dimensional chaotic dynamics occurring in complex biological systems such as recurrent neural networks or ecosystems remains a conceptual challenge. For low-dimensional dynamics, fixed points provide the geometric scaffold of the dyn ...

American Physical Society (APS)2025

An end-to-end pipeline for historical censuses processing

Rémi Guillaume Petitpierre, Lucas Arnaud André Rappo, Marion Kramer

Censuses are structured documents of great value for social and demographic history, which became widespread from the nineteenth century on. However, the plurality of formats and the natural variability of historical data make their extraction arduous and ...

2023

Combining Weather Station Data and Short-Term LiDAR Deployment to Estimate Wind Energy Potential with Machine Learning: A Case Study from the Swiss Alps

Michael Lehning, Wolf Hendrik Huwald, Jérôme François Sylvain Dujardin, Franziska Gerber, Fanny Kristianti

Wind energy potential in complex terrain is still poorly understood and difficult to quantify. With Switzerland’s current efforts to shift to renewable energy resources, it is now becoming even more crucial to investigate the hidden potential of wind energ ...

2023

Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union

Devis Tuia

Semantic segmentation datasets often exhibit two types of imbalance: \textit{class imbalance}, where some classes appear more frequently than others and \textit{size imbalance}, where some objects occupy more pixels than others. This causes traditional eva ...

2023

A conditional glutamatergic synaptic vesicle marker for Drosophila

Evelyne Ruchti, Brian Donal McCabe

Glutamate is a principal neurotransmitter used extensively by the nervous systems of all vertebrate and invertebrate animals. It is primarily an excitatory neurotransmitter that has been implicated in nervous system development, as well as a myriad of brai ...

OXFORD UNIV PRESS INC2022

Large-scale traffic signal control and multimodal network design

Dimitrios Tsitsokas

Traffic congestion constitutes one of the most frequent, yet challenging, problems to address in the urban space. Caused by the concentration of population, whose mobility needs surpass the serving capacity of urban networks, congestion cannot be resolved ...

EPFL2022

Paradoxical neuronal hyperexcitability in a mouse model of mitochondrial pyruvate import deficiency

Maria del Carmen Sandi Perez, Simone Astori, Eva Ramos Fernandez

Neuronal excitation imposes a high demand of ATP in neurons. Most of the ATP derives primarily from pyruvate-mediated oxidative phosphorylation, a process that relies on import of pyruvate into mitochondria occuring exclusively via the mitochondrial pyruva ...

eLIFE SCIENCES PUBL LTD2022

Network Classifiers Based On Social Learning

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to ...

IEEE2021

Learning about the vertical structure of radar reflectivity using hydrometeor classes and neural networks in the Swiss Alps

Alexis Berne, Marco Gabella, Floortje Elisabeth Maria Van den Heuvel

The use of radar for precipitation measurement in mountainous regions is complicated by many factors, especially beam shielding by terrain features, which, for example, reduces the visibility of the shallow precipitation systems during the cold season. Whe ...

2020

DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction

Mattia Rossi

Deep Neural Networks (DNNs) have the potential to improve the quality of image-based 3D reconstructions. However, the use of DNNs in the context of 3D reconstruction from large and high-resolution image datasets is still an open challenge, due to memory an ...

IEEE2020

Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE

Jean-Philippe Thiran, Meritxell Bach Cuadra, Cristina Granziera, Muhamed Barakovic, Francesco La Rosa

The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stages of the illness and have been shown to correlate with the severity of clinical symptoms. However, cortic ...

2020

A Model Comparison for Chord Prediction on the Annotated Beethoven Corpus

Martin Alois Rohrmeier, Fabian Claude Moss, Robert Lieck

This paper models predictive processing of chords using a corpus of Ludwig van Beethoven’s string quartets. A recently published dataset consisting of expert harmonic analyses of all Beethoven string quartets was used to evaluate an n-gram language model a ...

2019

SemiHD: Semi-Supervised Learning Using Hyperdimensional Computing

Samuel Bosch

In the Internet of Things (IoT), the large volume of data generated by sensors poses significant computational challenges in resource-constrained environments. Most existing machine learning algorithms are unable to train a proper model using a significant ...

IEEE2019

Check Out This Place: Inferring Ambiance from Airbnb Photos

Daniel Gatica-Perez, Laurent Son Nguyen

Airbnb is changing the landscape of the hospitality industry. For guests, the process of selecting a place to stay is a type of zero-acquaintance situation, and to this day little is known about the inferences that guests make about Airbnb listings. Enviro ...

2018

Models and Algorithms in Biological Network Evolution with Modularity

Min Ye

Networks are commonly used to represent key processes in biology; examples include transcriptional regulatory networks, protein-protein interaction (PPI) networks, metabolic networks, etc. Databases store many such networks, as graphs, observed or inferred ...

EPFL2017

Statistical models of effective connectivity in neural microcircuits

Joao Emanuel Felipe Gerhard

To appreciate how neural circuits in the brain control behaviors, we must identify how the neurons comprising the circuit are connected. Neuronal connectivity is difficult to determine experimentally, whereas neuronal activity can often be readily measured ...

EPFL2014

Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning

Wulfram Gerstner, Maria del Carmen Sandi Perez, Gediminas Luksys

Suppose we train an animal in a conditioning experiment. Can one predict how a given animal, under given experimental conditions, would perform the task? Since various factors such as stress, motivation, genetic background, and previous errors in task perf ...

Curran Associates, Inc.2009

Machine learning for information retrieval

David Grangier

In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...

EPFL2008