Publications associées à Computational model

Neurobiological and metabolic basis of motivation and effort-based decision-making - A human 1H-MRS, fMRI and machine learning study

Motivation is a multifaceted phenomenon that we explore within the framework of decision-making. Through this cognitive process, actions are directed towards specific goals by performing a trade-off between the cost and benefit of an action. The dorsomedia ...

EPFL2024

Analytical Model of Single-Sided Linear Induction Motors for High-Speed Applications

Mario Paolone, André Hodder, Lucien André Félicien Pierrejean, Simone Rametti

This article describes a field-based analytical model of single-sided linear induction motors (SLIMs) that explicitly considers the following effects altogether: finite motor length, magnetomotive force mmf space harmonics, slot effect, edge effect, and ta ...

IEEE2024

Puppeteer and Marionette: Learning Anticipatory Quadrupedal Locomotion Based on Interactions of a Central Pattern Generator and Supraspinal Drive

Auke Ijspeert, Guillaume Denis Antoine Bellegarda, Milad Shafiee Ashtiani

Quadruped animal locomotion emerges from the interactions between the spinal central pattern generator (CPG), sensory feedback, and supraspinal drive signals from the brain. Computational models of CPGs have been widely used for investigating the spinal co ...

IEEE2023

Lattice-and-Plate Model: Mechanics Modeling of Physical Origami Robots

Jamie Paik, Hongying Zhang

Origami robots are characterized by their compact design, quasi-two-dimensional manufacturing process, and folding joint-based transmission kinematics. The physical requirements in terms of payload, range of motion, and embedding core robotic components ha ...

MARY ANN LIEBERT, INC2022

Prenatal auditory experience and its sequelae

Lukas Vogelsang, Marin Vogelsang

Towards the end of the second trimester of gestation, a human fetus is able to register environmental sounds. This in utero auditory experience is characterized by comprising strongly low-pass-filtered versions of sounds from the external world. Here, we p ...

2022

Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Auke Ijspeert, Silvestro Micera, Shravan Tata Ramalingasetty, Marc-Oliver Gewaltig, Emanuele Formento, Maria Pasquini, Egidio Falotico, Lorenzo Vannucci, Axel von Arnim, Emmanouil Angelidis

Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all ...

FRONTIERS MEDIA SA2020

Crowding and the Architecture of the Visual System

Adrien Christophe Doerig

Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...

EPFL2020

The One-Way Communication Complexity of Submodular Maximization with Applications to Streaming and Robustness

Ola Nils Anders Svensson, Moran Feldman, Rico Zenklusen, Ashkan Norouzi Fard

We consider the classical problem of maximizing a monotone submodular function subject to a cardinality constraint, which, due to its numerous applications, has recently been studied in various computational models. We consider a clean multi-player model t ...

ASSOC COMPUTING MACHINERY2020

A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras

Auke Ijspeert, Alessandro Crespi, Mehmet Hasan Mutlu, Simon Lukas Hauser, Jorg Conradt, Ibrahim Youssef Youssef, Alexandre Bernardino

The computational load associated with computer vision is often prohibitive, and limits the capacity for on-board image analysis in compact mobile robots. Replicating the kind of feature detection and neural processing that animals excel at remains a chall ...

2020

Phase-Change Memory Models for Deep Learning Training and Inference

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Non-volatile analog memory devices such as phase-change memory (PCM) enable designing dedicated connectivity matrices for the hardware implementation of deep neural networks (DNN). In this in-memory computing approach, the analog conductance states of the ...

IEEE2019