Eilif Benjamin Muller

Membre académique associé

Chaire en IA Canada-CIFAR

Professeur adjoint, Université de Montréal, Département de neurosciences

Chercheur principal, Laboratoire des architectures de l’apprentissage biologique (ABL-Lab), CHU Ste-Justine - Research Center

Sujets de recherche

Apprentissage de représentations

Apprentissage en ligne

Apprentissage profond

Modèles génératifs

Neurosciences computationnelles

Réseaux de neurones récurrents

Systèmes dynamiques

Vision par ordinateur

Site web

Google Scholar

Biographie

Eilif B. Muller est un neuroscientifique et un chercheur en intelligence artificielle. Il utilise des approches informatiques et mathématiques pour étudier les mécanismes biologiques et algorithmiques de l'apprentissage dans le néocortex des mammifères. Il a obtenu un baccalauréat (2001) en physique mathématique de l'Université Simon Fraser, ainsi qu’une maîtrise (2003) et un doctorat en sciences naturelles (2007) en physique avec une spécialisation en neurosciences computationnelles de l'Université Ruprecht Karl de Heidelberg, l’université la plus ancienne d'Allemagne. Eilif B. Muller a entrepris son travail postdoctoral (2007-2010) au Laboratoire de neurosciences computationnelles de l’EPFL (Suisse) avec le professeur Wulfram Gerstner, en se concentrant sur la dynamique des réseaux, la technologie de simulation et la plasticité.

Par la suite, il a dirigé (2011-2019) l'équipe de recherche du Blue Brain Project, à l'EPFL, qui a ouvert la voie aux neurosciences in silico, une simulation inédite des tissus cérébraux basée sur les données. En 2015, Eilif B. Muller et ses collègues ont publié l’étude phare « Reconstruction and Simulation of Neocortical Microcircuitry » dans la revue Cell, décrivant « la simulation la plus complète d'un morceau de matière cérébrale excitable à ce jour », selon Christof Koch (président et directeur scientifique de l'Allen Institute for Brain Science). Cette approche lui a permis ainsi qu’à son équipe de contribuer de manière significative à la compréhension de la structure, de la dynamique et de la plasticité du néocortex, ce qui a donné lieu à des publications dans des revues de premier plan telles que Nature Neuroscience, Nature Communications et Cerebral Cortex.

En 2019, Eilif B. Muller a déménagé à Montréal, attiré par la communauté de recherche en neuro-IA florissante. Il y a d’abord été chercheur principal chez Element AI, avant sa nomination à l'Université de Montréal et au CHU Sainte-Justine, où il a lancé le Laboratoire des architectures d’apprentissage biologique (ABL-Lab).

Étudiants actuels

Sungjae Cho

Doctorat - UdeM

sungjae.cho@umontreal.ca

Doctorat - UdeM

Co-superviseur⋅e :

Shahab Bakhtiari

hafez.ghaemi@umontreal.ca

Site web

Github

Google Scholar

Dhuruva Priyan Gowri Mariyappan

Doctorat - UdeM

Doctorat - McGill

Nizar Islah

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Irina Rish

Nghi Nguyen

Maîtrise recherche - UdeM

Publications

Specific inhibition and disinhibition in the higher-order structure of a cortical connectome

Michael W. Reimann

Daniela Egas Santander

András Ecker

Eilif Benjamin Muller

Neuronal network activity is thought to be structured around the activation of assemblies, or low-dimensional manifolds describing states of… (voir plus) activity. Both views describe neurons acting not independently, but in concert, likely facilitated by strong recurrent excitation between them. The role of inhibition in these frameworks – if considered at all – is often reduced to blanket inhibition with no specificity with respect to which excitatory neurons are targeted. We analyzed the structure of excitation and inhibition in the MICrONS 1mm3 dataset, an electron microscopic reconstruction of a piece of cortical tissue. We found that excitation was structured around a feed-forward flow in non-random motifs of seven or more neurons. This revealed a structure of information flow from a small number of sources to a larger number of potential targets that became only visible when larger motifs were considered instead of individual pairs. Inhibitory neurons targeted and were targeted by neurons in specific sequential positions of these motifs. Additionally, disynaptic inhibition was strongest between target motifs excited by the same group of source neurons, implying competition between them. The structure of this inhibition was also highly specific and symmetrical, contradicting the idea of non-specific blanket inhibition. None of these trends are detectable in only pairwise connectivity, demonstrating that inhibition is specifically structured by these large motifs. Further, we found that these motifs represent higher order connectivity patterns which are present, but to a lesser extent in a recently released, detailed computational model, and not at all in a distance-dependent control. These findings have important implications for how synaptic plasticity reorganizes neocortical connectivity to implement learning and for the specific role of inhibition in this process.

2024-10-07

bioRxiv (prépublication)

doi.org

Top-down feedback matters: Functional impact of brainlike connectivity motifs on audiovisual integration

Mashbayar Tugsbayar

Mingze Li

Eilif Benjamin Muller

Blake Richards

2024-10-03

bioRxiv (prépublication)

doi.org

Long-term plasticity induces sparse and specific synaptic changes in a biophysically detailed cortical model

András Ecker

Daniela Egas Santander

Marwan Abdellah

Jorge Blanco Alonso

Sirio Bolaños-Puchet

Giuseppe Chindemi

Dhuruva Priyan Gowri Mariyappan

James B. Isbister

James King

Pramod Kumbhar

Ioannis Magkanaris

Eilif Benjamin Muller

Michael W. Reimann

2024-07-29

bioRxiv (prépublication)

doi.org

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

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 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 Benjamin 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 reconcile information obtained from diverse experimental approaches. To address this challenge, we present a community-driven, 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 address a range of scientific questions. In this article, we describe the methods used to set up simulations that reproduce and extend 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 and reconcile 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 neuroscience community-driven 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.

2023-05-17

bioRxiv (prépublication)

doi.org

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation

James B. Isbister

András Ecker

Christoph Pokorny

Sirio Bolaños-Puchet

Daniela Egas Santander

Alexis Arnaudon

Omar Awile

Natali Barros-Zulaica

Jorge Blanco Alonso

Elvis Boci

Giuseppe Chindemi

Jean-Denis Courcol

Tanguy Damart

Thomas Delemontex

Alexander Dietz

Gianluca Ficarelli

Michael Gevaert

Joni Herttuainen

Genrich Ivaska

Weina Ji … (voir 22 de plus)

Daniel Keller

James King

Pramod Kumbhar

Samuel Lapere

Polina Litvak

Darshan Mandge

Eilif Benjamin Muller

Fernando Pereira

Judit Planas

Rajnish Ranjan

Maria Reva

Armando Romani

Christian Rössert

Felix Schürmann

Vishal Sood

Aleksandra Teska

Anil Tuncel

Werner Van Geit

Matthias Wolf

Henry Markram

Srikanth Ramaswamy

Michael W. Reimann

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which can be studied in large-scale, b… (voir plus)iophysically detailed models. We present a model comprising eight somatosensory cortex subregions, 4.2 million morpho-logical and electrically-detailed neurons, and 13.2 billion local and long-range synapses. In silico tools enabled reproduction and extension of complex laboratory experiments under a single parameterization, providing strong validation. We reproduced millisecond-precise stimulus-responses, stimulus-encoding under targeted optogenetic activation, and selective propagation of stimulus-evoked activity to downstream areas. The model’s di-rect correspondence with biology generated predictions about how multiscale organisation shapes activity. We predict that structural and functional recurrency increases towards deeper layers and that stronger innervation by long-range connectivity increases local correlated activity. The model also predicts the role of inhibitory interneuron types in stimulus encoding, and of different layers in driving layer 2/3 stimulus responses. Simu-slation tools and a large subvolume of the model are made available.

2023-05-17

bioRxiv (prépublication)

doi.org

Predicting Infectiousness for Proactive Contact Tracing

Yoshua Bengio

Prateek Gupta

Tegan Maharaj

Nasim Rahaman

Martin Weiss

Tristan Deleu

Eilif Benjamin Muller

Meng Qu

Victor Schmidt

Pierre-Luc St-Charles

Hannah Alsdurf

Olexa Bilaniuk

David Buckeridge

gaetan caron

pierre luc carrier

Joumana Ghosn

satya ortiz gagne

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.

2021-01-12

ICLR.cc/2021/Conference (spotlight)

openreview.net