Publications associées à Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data

Publications associées (14)

Fair Decentralized Learning

Anne-Marie Kermarrec, Rishi Sharma, Marinus Abraham de Vos, Sayan Biswas

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

IEEE2025

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Thomas Grégoire Stegmüller

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 ...

EPFL2025

A generalized machine learning framework to estimate fatigue life across materials with minimal data

Anastasios Vassilopoulos, Dharun Vadugappatty Srinivasan

In this research, a generalized machine learning (ML) framework is proposed to estimate the fatigue life of epoxy polymers and additively manufactured AlSi10Mg alloy materials, leveraging their failure surface void characteristics. An extreme gradient boos ...

2024

MCMC: Bridging Rendering, Optimization and Generative AI

Wenzel Alban Jakob

Generative artificial intelligence (AI) has made unprecedented advances in vision language models over the past two years. These advances are largely due to diffusion-based generative models, which are very stable and simple to train. These diffusion model ...

Association for Computing Machinery, Inc2024

Networks with correlated edge processes

Mária Süveges, Sofia Charlotta Olhede

This article proposes methods to model non-stationary temporal graph processes motivated by a hospital interaction data set. This corresponds to modelling the observation of edge variables indicating interactions between pairs of nodes exhibiting dependenc ...

OXFORD UNIV PRESS2023

Directional sensitivity of cortical neurons towards TMS induced electric fields

Werner Alfons Hilda Van Geit, Aurélien Tristan Jaquier

We derived computationally efficient average response models of different types of cortical neurons, which are subject to external electric fields from Transcranial Magnetic Stimulation. We used 24 reconstructions of pyramidal cells (PC) from layer 2/3, 24 ...

2023

Sparsely Observed Functional Time Series: Theory and Applications

Tomas Rubin

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

EPFL2021

Adaptive hot water production based on Supervised Learning

Alexandre Massoud Alahi, Dolaana Khovalyg, Amirreza Heidari

A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stoch ...

2021

Interpreting null models of resting-state functional MRI dynamics: not throwing the model out with the hypothesis

Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois

Null models are useful for assessing whether a dataset exhibits a non-trivial property of interest. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fM ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2021

Probabilistic approach for fatigue evaluation of welded connections with application to road steel bridges

Luca D'Angelo

Fatigue analysis of steel bridges asks for consideration of welded joints. Traditional fatigue analysis of welded joints under variable amplitude loadings is based on the nominal stress approach, wherein constant amplitude (CA) S-N curves are used in combi ...

EPFL2015

A Biophysically Detailed Model of Neocortical Local Field Potentials Predicts the Critical Role of Active Membrane Currents

Henry Markram, Sean Lewis Hill, Michael Reimann, Rodrigo de Campos Perin

Brain activity generates extracellular voltage fluctuations recorded as local field potentials (LFPs). It is known that the relevant microvariables, the ionic currents across membranes, jointly generate the macrovariables, the extracellular voltage, but ne ...

Elsevier2013

Improved Similarity Measures for Small Sets of Spike Trains

Wulfram Gerstner, Richard Naud, Joao Emanuel Felipe Gerhard, Skander Mensi

Multiple measures have been developed to quantify the similarity between two spike trains. These measures have been used for the quantification of the mismatch between neuron models and experiments as well as for the classification of neuronal responses in ...

Massachusetts Institute of Technology Press2011

Model-fitting in the presence of outliers

Jayakrishnan Unnikrishnan

We study the problem of parametric model-fitting in a finite alphabet setting. We characterize the weak convergence of the goodness-of-fit statistic with respect to an exponential family when the observations are drawn from some alternate distribution. We ...

IEEE Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Predicting single spikes and spike patterns with the Hindmarsh–Rose model

Martin Hasler

Most simple neuron models are only able to model traditional spiking behavior. As physiologists discover and classify different electrical phenotypes, computational neuroscientists become interested in using simple phenomenological models that can exhibit ...

Springer-Verlag2008