Publications related to Neuronal Dynamics - Computational Neuroscience of Single Neurons

Predicting the long-term collective behaviour of fish pairs with deep learning

Francesco Mondada, Alexandre Massoud Alahi, Vaios Papaspyros

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

2024

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Olga Fink, Hao Lu, Chao Hu

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

Changes in spatial self-consciousness elicit grid cell–like representation in the entorhinal cortex

Olaf Blanke, Emanuela De Falco, Louis Philippe Albert, Hyeongdong Park, Baptiste Gauthier, Hyukjun Moon, Corentin Marie Hervé Robert Tasu

Grid cells in the entorhinal cortex (EC) encode an individual’s location in space, integrating both environmental and multisensory bodily cues. Notably, body-derived signals are also primary signals for the sense of self. While studies have demonstrated th ...

2024

Membrane specificity of the human cholesterol transfer protein STARD4

Charlotte Julie Caroline Gehin

STARD4 regulates cholesterol homeostasis by transferring cholesterol between the plasma membrane and endoplasmic reticulum. The STARD4 structure features a helix-grip fold surrounding a large hydrophobic cavity holding the sterol. Its access is controlled ...

Academic Press Ltd- Elsevier Science Ltd2024

Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps

Jan Sickmann Hesthaven, Niccolo' Discacciati

We propose a local, non -intrusive model order reduction technique to accurately approximate the solution of coupled multi -component parametrized systems governed by partial differential equations. Our approach is based on the approximation of the boundar ...

Lausanne2024

Spectral Estimators for High-Dimensional Matrix Inference

Farzad Pourkamali

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

EPFL2024

Robot Learning using Tensor Networks

Suhan Narayana Shetty

In various robotics applications, the selection of function approximation methods greatly influences the feasibility and computational efficiency of algorithms. Tensor Networks (TNs), also referred to as tensor decomposition techniques, present a versatile ...

EPFL2024

Partial discharge localization in power transformer tanks using machine learning methods

Marcos Rubinstein, Hamidreza Karami

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

2024

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Mohammad Khaja Nazeeruddin

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

Lausanne2024

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov, Scott William Pesme

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

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Predicting the long-term collective behaviour of fish pairs with deep learning

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Changes in spatial self-consciousness elicit grid cell–like representation in the entorhinal cortex

Membrane specificity of the human cholesterol transfer protein STARD4

Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps

Spectral Estimators for High-Dimensional Matrix Inference

Robot Learning using Tensor Networks

Partial discharge localization in power transformer tanks using machine learning methods

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Changes in spatial self-consciousness elicit grid cell–like representation in the entorhinal cortex

Membrane specificity of the human cholesterol transfer protein STARD4

Spectral Estimators for High-Dimensional Matrix Inference

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Predicting the long-term collective behaviour of fish pairs with deep learning

Partial discharge localization in power transformer tanks using machine learning methods

Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps

Robot Learning using Tensor Networks