Eilif Benjamin Muller

Associate Academic Member

Canada CIFAR AI Chair

Assistant Professor, Université de Montréal, Department of Neurosciences

Principal investigator, Architectures of Biological Learning Lab (ABL-Lab), CHU Ste-Justine - Research Center

Biography

Eilif B. Muller is a neuroscientist and AI researcher who uses computational and mathematical approaches to study the biological and algorithmic mechanisms of learning in the mammalian neocortex. Muller obtained his BSc in mathematical physics (2001) from Simon Fraser University, and his MSc (2003) and PhD (2007) in physics, with a focus on computational neuroscience, from the Ruprecht Karl University of Heidelberg, Germany’s oldest university. His postdoctoral work (2007–2010) with Wulfram Gerstner at the Laboratory for Computational Neuroscience of the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, focused on network dynamics, simulation technology and plasticity.

From 2011 to 2019, he led the research team at the Institute’s Blue Brain Project, which pioneered in-silico neuroscience, a new era of data-driven brain tissue simulation. In 2015, Muller and his colleagues published their landmark team-science study “Reconstruction and Simulation of Neocortical Microcircuitry” in Cell. According to Christof Koch, President and CSO of the Allen Institute for Brain Science, this new approach represents “the most complete simulation of a piece of excitable brain matter to date.” This work also enabled Muller and his team to make significant contributions to our understanding of the structure, dynamics and plasticity of the neocortex, resulting in publications in top journals, such as Nature Neuroscience, Nature Communications and Cerebral Cortex.

In 2019, Muller moved to Montréal, attracted by the thriving Neuro-AI research community. He initially served as a senior researcher at Element AI, prior to being appointed to the Université de Montréal and Centre Hospitalier Universitaire (CHU) Sainte-Justine to launch the Architectures of Biological Learning Lab.

Current Students

Sungjae Cho

PhD - Université de Montréal

sungjae.cho@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Shahab Bakhtiari

hafez.ghaemi@mila.quebec

Postdoctorate - McGill University

Principal supervisor :

Dhuruva Priyan Gowri Mariyappan

PhD - Université de Montréal

dhuruva-priyan.gowri-mariyappan@mila.quebec

Tugce Gurbuz

PhD - McGill University

busra-tugce.gurbuz@mila.quebec

Website

Github

Google Scholar

Nizar Islah

PhD - Université de Montréal

Co-supervisor :

Irina Rish

nizar.islah@mila.quebec

Master's Research - Université de Montréal

Principal supervisor :

Golnoosh Farnadi

armin.moradi@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Irina Rish

timothy.nest@mila.quebec

Michael Noukhovitch

PhD - Université de Montréal

Principal supervisor :

Publications

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

James B. Isbister

James King

Pramod Kumbhar

Ioannis Magkanaris

Eilif Benjamin Muller

Michael W. Reimann

2024-01-11

bioRxiv (preprint)

doi.org

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… (see more) 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.

2023-12-22

bioRxiv (preprint)

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 … (see 28 more)

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… (see more)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 (preprint)

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 … (see 22 more)

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… (see more)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 (preprint)

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 … (see 3 more)

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… (see more)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