Blake Richards

Biography

Blake Richards is an associate professor at the School of Computer Science and in the Department of Neurology and Neurosurgery at McGill University, and a core academic member of Mila – Quebec Artificial Intelligence Institute.

Richards’ research lies at the intersection of neuroscience and AI. His laboratory investigates universal principles of intelligence that apply to both natural and artificial agents.

He has received several awards for his work, including the NSERC Arthur B. McDonald Fellowship in 2022, the Canadian Association for Neuroscience Young Investigator Award in 2019, and a Canada CIFAR AI Chair in 2018. Richards was a Banting Postdoctoral Fellow at SickKids Hospital from 2011 to 2013.

He obtained his PhD in neuroscience from the University of Oxford in 2010, and his BSc in cognitive science and AI from the University of Toronto in 2004.

Current Students

Benjamin Alsbury-Nealy

PhD - McGill University

benjamin@silicolabs.ca

Antoine Boudreau LeBlanc

Postdoctorate - McGill University

Colin Bredenberg

Postdoctorate - Université de Montréal

Principal supervisor :

Guillaume Lajoie

Ethan Caballero

PhD - McGill University

Co-supervisor :

PhD - McGill University

Anthony Chen

Independent visiting researcher - NYU

PhD - McGill University

Principal supervisor :

PhD - McGill University

Jonathan Cornford

Collaborating Alumni - McGill University

Sabrina Du

Undergraduate - McGill University

aleksei.efremov@mail.mcgill.ca

Alex Efremov

PhD - McGill University

Arna Ghosh

Collaborating Alumni - McGill University

Adel Halawa

PhD - McGill University

Danny Han

Independent visiting researcher - Seoul National University

Roy Henha Eyono

PhD - McGill University

Ann Huang

Collaborating Alumni

Sonia Joseph

PhD - McGill University

Divyansha Lachi

Collaborating researcher - Georgia Tech

Postdoctorate - McGill University

Pingsheng Li

PhD - McGill University

matthew.loukine@mail.mcgill.ca

Dongyan Lin

PhD - McGill University

PhD - McGill University

Dane Malenfant

PhD - McGill University

Abdel Mfougouon Njupoun

PhD - Université de Montréal

Principal supervisor :

Guillaume Lajoie

abdelnjupoun@gmail.com

Collaborating Alumni - McGill University

qazaleh.ranjbaran@gmail.com

Adrien Peyrache

Independent visiting researcher

Roman Pogodin

Collaborating Alumni - McGill University

Co-supervisor :

Independent visiting researcher - Université de Montréal

Alexis Roger

PhD - McGill University

Co-supervisor :

Irina Rish

Ali Saheb Pasand

PhD - McGill University

Co-supervisor :

Pablo Samuel Castro

Mandana Samiei

PhD - McGill University

Principal supervisor :

Doina Precup

samiemandana@gmail.com

Aidan Sirbu

Master's Research - McGill University

Hiro Tanabe

Independent visiting researcher - NA

Josh Tindall

Master's Research - McGill University

Mashbayar Tugsbayar

PhD - McGill University

Charlotte Volk

Master's Research - McGill University

Co-supervisor :

Shahab Bakhtiari

Maren Wehrheim

Independent visiting researcher - York University

Mohammad Yaghoubi

PhD - McGill University

Machine Learning for the Segmentation of Different Nerve Fibre Activations from Brain-to-body Neural Signals

AmirHossein Zamani

PhD - Concordia University

Principal supervisor :

Blog Posts

Représentation graphique d'un nerf vague

May 21, 2025

Param Raval

Olivier Tessier-Larivière

Pascal Fortier-Poisson

Blake Richards

Guillaume Lajoie

Read the article

June 13, 2024

What Do Synaptic Weight Distributions Tell Us About Learning in the Brain ?

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Read the article

August 29, 2023

α-ReQ: Assessing Representation Quality in SSL

KK Agrawal

Arnab Kumar-Mondal

Arna Ghosh

Blake A. Richards

Read the article

Publications

$\alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay

Kumar Krishna Agrawal

Arnab Kumar Mondal

Arna Ghosh

Self-Supervised Learning (SSL) with large-scale unlabelled datasets enables learning useful representations for multiple downstream tasks. H… (see more)owever, assessing the quality of such representations efficiently poses nontrivial challenges. Existing approaches train linear probes (with frozen features) to evaluate performance on a given task. This is expensive both computationally, since it requires retraining a new prediction head for each downstream task, and statistically, requires task-specific labels for multiple tasks. This poses a natural question, how do we efficiently determine the "goodness" of representations learned with SSL across a wide range of potential downstream tasks? In particular, a task-agnostic statistical measure of representation quality, that predicts generalization without explicit downstream task evaluation, would be highly desirable. In this work, we analyze characteristics of learned representations

openreview.net

Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules

Yuhan Helena Liu

Eric Todd SheaBrown

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

Anthony Zador

Bence Ölveczky

Sean Escola

Yoshua Bengio

Kwabena Boahen

Matthew Botvinick

Dmitri Chklovskii

Anne Churchland

Claudia Clopath

James DiCarlo

Surya Ganguli

Jeff Hawkins

Konrad Paul Kording

Alexei Koulakov

Yann LeCun

Timothy P. Lillicrap

Adam Marblestone

Bruno Olshausen

Alexandre Pouget … (see 7 more)

Cristina Savin

Terrence Sejnowski

Eero Simoncelli

Sara Solla

David Sussillo

Andreas S. Tolias

Doris Tsao

2022-01-01

arXiv.org (preprint)

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

Anthony Zador

Bence Ölveczky

Sean Escola

Yoshua Bengio

Kwabena Boahen

Matthew Botvinick

Dmitri Chklovskii

Anne Churchland

Claudia Clopath

James DiCarlo

Surya Ganguli

Jeff Hawkins

Konrad Paul Kording

Alexei Koulakov

Yann LeCun

Timothy P. Lillicrap

Adam Marblestone

Bruno Olshausen

Alexandre Pouget … (see 7 more)

Cristina Savin

Terrence Sejnowski

Eero Simoncelli

Sara Solla

David Sussillo

Andreas S. Tolias

Doris Tsao

2022-01-01

arXiv.org (preprint)

The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning

Shahab Bakhtiari

Patrick J Mineault

Timothy P. Lillicrap

Christopher C. Pack

The visual system of mammals is comprised of parallel, hierarchical specialized pathways. Different pathways are specialized in so far as th… (see more)ey use representations that are more suitable for supporting specific downstream behaviours. In particular, the clearest example is the specialization of the ventral (“what”) and dorsal (“where”) pathways of the visual cortex. These two pathways support behaviours related to visual recognition and movement, respectively. To-date, deep neural networks have mostly been used as models of the ventral, recognition pathway. However, it is unknown whether both pathways can be modelled with a single deep ANN. Here, we ask whether a single model with a single loss function can capture the properties of both the ventral and the dorsal pathways. We explore this question using data from mice, who like other mammals, have specialized pathways that appear to support recognition and movement behaviours. We show that when we train a deep neural network architecture with two parallel pathways using a self-supervised predictive loss function, we can outperform other models in fitting mouse visual cortex. Moreover, we can model both the dorsal and ventral pathways. These results demonstrate that a self-supervised predictive learning approach applied to parallel pathway architectures can account for some of the functional specialization seen in mammalian visual systems.

openreview.net

From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence

Nicholas Roy

Ingmar Posner

T. Barfoot

Philippe Beaudoin

Yoshua Bengio

Jeannette Bohg

Oliver Brock

Isabelle Depatie

Dieter Fox

D. Koditschek

Tom'as Lozano-p'erez

Vikash K. Mansinghka

Chris Pal

Dorsa Sadigh

Stefan Schaal

G. Sukhatme

Denis Therien

Marc Emile Toussaint

Michiel van de Panne

2021-10-28

ArXiv (preprint)

arxiv.org

Promoting and Optimizing the Use of 3D-Printed Objects in Spontaneous Recognition Memory Tasks in Rodents: A Method for Improving Rigor and Reproducibility

Mehreen Inayat

Arely Cruz-Sanchez

Hayley H. A. Thorpe

Jude A. Frie

Jibran Y. Khokhar

Maithe Arruda-Carvalho

2021-09-01

eNeuro (published)

Forgetting Enhances Episodic Control With Structured Memories

Annik Yalnizyan-Carson

Forgetting is a normal process in healthy brains, and evidence suggests that the mammalian brain forgets more than is required based on limi… (see more)tations of mnemonic capacity. Episodic memories, in particular, are liable to be forgotten over time. Researchers have hypothesized that it may be beneficial for decision making to forget episodic memories over time. Reinforcement learning offers a normative framework in which to test such hypotheses. Here, we show that a reinforcement learning agent that uses an episodic memory cache to find rewards in maze environments can forget a large percentage of older memories without any performance impairments, if they utilize mnemonic representations that contain structural information about space. Moreover, we show that some forgetting can actually provide a benefit in performance compared to agents with unbounded memories. Our analyses of the agents show that forgetting reduces the influence of outdated information and states which are not frequently visited on the policies produced by the episodic control system. These results support the hypothesis that some degree of forgetting can be beneficial for decision making, which can help to explain why the brain forgets more than is required by capacity limitations.

2021-08-12

bioRxiv (preprint)

Learning function from structure in neuromorphic networks

Laura E. Suárez

Guillaume Lajoie

Bratislav Mišić

2021-08-09

Nature Machine Intelligence (published)

Neocortical inhibitory interneuron subtypes are differentially attuned to synchrony- and rate-coded information

Luke Y. Prince

Matthew M. Tran

Dorian Grey

Lydia Saad

Helen Chasiotis

Jeehyun Kwag

Michael M Kohl

2021-08-05

Communications Biology (published)

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation

Emerson F. Harkin

Peter R. Shen

Anisha Goel

Richard Naud

Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysica… (see more)l models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.

2021-07-05

Neuroscience (published)

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation

Emerson F. Harkin

Peter R. Shen

Anisha Goel

Richard Naud

2021-07-05

Neuroscience (published)