Publications associées à Using the Green's function to simplify and understand dendrites

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

EPFL2024

Impact of beam-coupling impedance on the Schottky spectrum of bunched beam

Tatiana Pieloni, Nicolas Frank Mounet, Christophe Emmanuel R. Lannoy

The Schottky monitors of the Large Hadron Collider (LHC) can be used for non-invasive beam diagnostics to estimate various bunch characteristics, such as tune, chromaticity, bunch profile or synchrotron frequency distribution. However, collective effects, ...

Iop Publishing Ltd2024

Discrete choice modeling in the era of big data

Nicola Marco Ortelli

The technological advancements of the past decades have allowed transforming an increasing part of our daily actions and decisions into storable data, leading to a radical change in the scale and scope of available data in relation to virtually any object ...

EPFL2024

Subcortical correlates of consciousness with human single neuron recordings

Michael Eric Anthony Pereira, Olaf Blanke, Nathan Quentin Faivre, Fosco Bernasconi

Subcortical brain structures such as the basal ganglia or the thalamus are involved in regulating motor and cognitive behavior. However, their contribution to perceptual consciousness is still unclear, due to the inherent difficulties of recording subcorti ...

2024

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

Axel Bisi, Alberto Silvio Chiappa, Alexander Mathis, Alessandro Marin Vargas

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 ...

Zenodo2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Paul Scharnhorst

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub

Felix Schürmann, Armando Romani, Michele Migliore, Luca Leonardo Bologna

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...

2023

Principles of Network Plasticity in Neocortical Microcircuits

András Ecker

Synaptic plasticity underlies our ability to learn and adapt to the constantly changing environment. The phenomenon of synapses changing their efficacy in an activity-dependent manner is often studied in small groups of neurons in vitro or indirectly throu ...

EPFL2023

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

Alfio Quarteroni, Francesco Regazzoni, Marco Fedele, Stefano Pagani

Background: Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors ...

BMC2023

From event-based surprise to lifelong learning. A journey in the timescales of adaptation

Martin Louis Lucien Rémy Barry

Humans and animals constantly adapt to their environment over the course of their life. This thesis seeks to integrate various timescales of adaptation, ranging from the adaptation of synaptic connections between spiking neurons (milliseconds), rapid behav ...

EPFL2023

Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome

James Gonzalo King, Eilif Benjamin Muller, Michael Reimann, Marwan Muhammad Ahmed Abdellah, Giuseppe Chindemi, Pramod Shivaji Kumbhar, András Ecker, Daniela Egas Santander, Ioannis Magkanaris, Sirio Bolaños Puchet, Jorge Blanco Alonso, James Bryden Isbister

Synaptic plasticity underlies the brain’s ability to learn and adapt. While experiments in brain slices have revealed mechanisms and protocols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biologica ...

2024

Cortical cell assemblies and their underlying connectivity: an in silico study

Michael Reimann, András Ecker, Daniela Egas Santander, Sirio Bolaños Puchet, James Bryden Isbister

Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies rema ...

2024

Equivariant Neural Architectures for Representing and Generating Graphs

Clément Arthur Yvon Vignac

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

EPFL2023

A theory of memory consolidation and synaptic pruning in cortical circuits

Georgios Iatropoulos

Over the course of a lifetime, the human brain acquires an astonishing amount of semantic knowledge and autobiographical memories, often with an imprinting strong enough to allow detailed information to be recalled many years after the initial learning exp ...

EPFL2023

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

3D trigonal FAPbI(3)-based multilevel resistive switching nonvolatile memory for artificial neural synapse

Mohammad Khaja Nazeeruddin, Paul Joseph Dyson, Yong Ding, Bowen Jiang, Li Tao, Jun Zhang, Yao Zhang

Hybrid perovskites have attracted enormous attention in the next generation resistive switching (RS) memristor for the artificial synapses, owing to their ambipolar charge transport, long diffusion length, and tunable visible bandgap. However, the variable ...

WILEY2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Melanie Nicole Zeilinger, Xuan Truong Nghiem, Roland Schwan

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

IEEE2023

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

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

Investigating the roles of reflexes and central pattern generators in the control and modulation of human locomotion using a physiologically plausible neuromechanical model

Auke Ijspeert, Andrea Di Russo, Stéphane Armand, Dimitar Yuriev Stanev, Anushree Bapusaheb Sabnis

Objective. Studying the neural components regulating movement in human locomotion is obstructed by the inability to perform invasive experimental recording in the human neural circuits. Neuromechanical simulations can provide insights by modeling the locom ...

Iop Publishing Ltd2023

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Jesus Sanchez Rodriguez

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...

PUBLIC LIBRARY SCIENCE2023