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Eilif Muller

Alumni

Publications

Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region
Armando Romani
Alberto Antonietti
Davide Bella
Julian Budd
Elisabetta Giacalone
Kerem Kurban
Sára Sáray
Marwan Abdellah
Alexis Arnaudon
Elvis Boci
Cristina Colangelo
Jean-Denis Courcol
Thomas Delemontex
András Ecker
Joanne Falck
Cyrille Favreau
Michael Gevaert
Juan B. Hernando
Joni Herttuainen
Genrich Ivaska … (voir 28 de plus)
Lida Kanari
Anna-Kristin Kaufmann
James Gonzalo King
Pramod Kumbhar
Sigrun Lange
Huanxiang Lu
Carmen Alina Lupascu
Rosanna Migliore
Fabien Petitjean
Judit Planas
Pranav Rai
Srikanth Ramaswamy
Michael W. Reimann
Juan Luis Riquelme
Nadir Román Guerrero
Ying Shi
Vishal Sood
Mohameth François Sy
Werner Van Geit
Liesbeth Vanherpe
Tamás F. Freund
Audrey Mercer
Eilif Muller
Felix Schürmann
Alex M. Thomson
Michele Migliore
Szabolcs Káli
Henry Markram
The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive fun… (voir plus)ctions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to integrate information obtained from diverse experimental approaches. To address this challenge, we present a community-based, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to potentially address a range of scientific questions. In this article, we describe the methods used to set up simulations to reproduce in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce experimental findings. Finally, we make data, code, and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This community-based model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.
2024-10-31
PLoS Biology (publié)
doi.org
BlueCelluLab: Biologically Detailed Neural NetworkExperimentation API
Anıl Tuncel
Werner Van Geit
Mike Gevaert
Benjamin Torben-Nielsen
Darshan Mandge
İlkan Kılıç
Aurélien T. Jaquier
Eilif Muller
Lida Kanari
Henry Markram
The NEURON simulator, established in 1984 and continuously developed since, stands as\nthe preeminent tool for neuron simulation within comp… (voir plus)utational neuroscience. Its widespread\nadoption and compatibility with computational clusters and supercomputers underscore its\npivotal role in large-scale neuronal research. However, its integration with the Python pro-\ngramming language has introduced complexities, particularly concerning memory management\nand object lifecycle. To conceal these challenges from the user and seamlessly interface\nwith community standards for neural network representation data formats such as SONATA,\nwe introduce BlueCelluLab. The high-level Python API simplifies the execution of neural\nsimulations, ranging from single neurons to intricate networks, by managing complexities\nrelated to memory management and object lifecycle, thus providing a streamlined experience\nfor users. Today, BlueCelluLab is powering various Python packages, command line interfaces,\nweb applications, and data analysis workflows.
2024-08-16
Journal of Open Source Software (publié)
doi.org
Predicting Infectiousness for Proactive Contact Tracing
Yoshua Bengio
Prateek Gupta
Tegan Maharaj
Nasim Rahaman
Martin Weiss
Tristan Deleu
Eilif Muller
Meng Qu
Victor Schmidt
Pierre-Luc St. Charles
Hannah Alsdurf
Olexa Bilaniuk
David Buckeridge
Gaétan Marceau-Caron
Pierre-Luc Carrier
Joumana Ghosn
Satya Ortiz-Gagné
Chris Pal
Irina Rish
Bernhard Schölkopf … (voir 3 de plus)
Abhinav Sharma
Jian Tang
Andrew Williams
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (voir plus)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.
2020-12-31
ICLR (publié)
doi.org
openreview.net