Blake Richards

Biography

Blake Richards is Research Scientist Manager with the Paradigms of Intelligence team at Google, and an Associate Professor in the School of Computer Science and Department of Neurology and Neurosurgery at McGill University. He is also a Core Faculty Member at Mila.

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

Aliya Affdal

Collaborating researcher - Université de Montréal

aliya.affdal@mila.quebec

Benjamin Alsbury-Nealy

PhD - McGill University

benjamin@silicolabs.ca

antoine.boudreau-leblanc@mila.quebec

Antoine Boudreau LeBlanc

Postdoctorate - McGill University

Colin Bredenberg

Independent visiting researcher - Université de Montréal

Principal supervisor :

colin.bredenberg@mila.quebec

Nour Chahine

PhD - McGill University

nour.chahine@mila.quebec

erica.cianfarano@mila.quebec

Raymond Chua

PhD - McGill University

Principal supervisor :

PhD - McGill University

Jonathan Cornford

Collaborating researcher - McGill University

cornforj@mila.quebec

Sabrina Du

Undergraduate - McGill University

sabrina.du@mila.quebec

aleksei.efremov@mail.mcgill.ca

Alex Efremov

PhD - McGill University

Tom George

Postdoctorate - McGill University

Co-supervisor :

tomgeorge1@btinternet.com

Independent visiting researcher - Université de Montréal

oumayma.gharbi@mila.quebec

Arna Ghosh

Collaborating researcher - McGill University

Adel Halawa

PhD - McGill University

adel.halawa@mila.quebec

Roy Henha Eyono

PhD - McGill University

roy.eyono@mila.quebec

Sonia Joseph

PhD - McGill University

sonia.joseph@mila.quebec

daniel.levenstein@mila.quebec

Daniel Levenstein

Collaborating Alumni - McGill University

Pingsheng Li

PhD - McGill University

Co-supervisor :

pingsheng.li@mila.quebec

matthew.loukine@mail.mcgill.ca

Matthew Loukine

PhD - McGill University

Dane Malenfant

Master's Research - McGill University

dane.malenfant@mila.quebec

Abdel Mfougouon Njupoun

PhD - Université de Montréal

Principal supervisor :

le-thuy-duong.nguyen@mila.quebec

abdelnjupoun@gmail.com

Master's Research - McGill University

Principal supervisor :

Matt Perich

qazaleh.ranjbaran@gmail.com

Ghazaleh Ranjbaran

Independent visiting researcher - Université de Montréal

Alexis Roger

PhD - McGill University

Co-supervisor :

Irina Rish

alexis.roger@mila.quebec

ali.sahebpasand@mila.quebec

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

PhD - McGill University

aidan.sirbu@mila.quebec

hiroyuki.tanabe@mila.quebec

Hiro Tanabe

Independent visiting researcher - NA

Josh Tindall

Collaborating Alumni - McGill University

joshua.tindall@mila.quebec

Mashbayar Tugsbayar

PhD - McGill University

charlotte.volk@mila.quebec

Charlotte Volk

PhD - McGill University

Co-supervisor :

Shahab Bakhtiari

maren.wehrheim@mila.quebec

Maren Wehrheim

Independent visiting researcher - York University

amirhossein.zamani@mila.quebec

Amirhossein Zamani

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

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

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

BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings

Alexandre Andre

Shivashriganesh P. Mahato

Vinam Arora

Keshav Balaji

Divyansha Lachi

Nanda H. Krishna

Jingyun Xiao

Yizi Zhang

Ximeng Mao

Wenrui Ma

Han Yu

International Brain Laboratory

Daniel Birman

Niccolò Bonacchi

Gaelle A. Chapuis

Joana A. Catarino

Felicia Davatolhagh

Mayo Faulkner

Laura Freitas-Silva

Fei Hu … (see 22 more)

Julia M. Huntenburg

Anup Khanal

Inês Laranjeira

Petrina Lau

Guido T. Meijer

Nathaniel J. Miska

Jean-Paul Noel

Alejandro Pan-Vazquez

Georg Raiser

Cyrille Rossant

Karolina Z. Socha

Anne E. Urai

Miles J. Wells

Steven J. West

Olivier Winter

Cole Hurwitz

Mehdi Azabou

Matthew R. Whiteway

Liam Paninski

Eva L. Dyer

Advances in large-scale neural recording have made it possible to collect data across many animals and distributed brain regions, raising th… (see more)e question of whether this scale can be exploited to learn general-purpose neural representations transferable across diverse downstream tasks. Yet, progress toward this goal has been limited by fragmented evaluation protocols and a narrow focus on individual task domains. Here, we present BrainWideBench, a benchmark for evaluating across-animal transfer on multi-region neural recordings, built on the International Brain Laboratory Brainwide Map dataset of neural and behavioral recordings spanning 276 brain regions from 139 mice performing a sensory-guided decision-making task. The benchmark is organized around three complementary task suites that evaluate whether learned representations support downstream decoding of behavior, can predict masked or future neural activity, and can recover biologically meaningful anatomical organization. With this benchmark, we systematically evaluate pretraining methods across transfer settings, including finetuning on downstream objectives and zero-shot generalization to unseen animals. Our results confirm pretraining improves performance over matched single-session baselines, but we show current methods exhibit heterogeneity in transfer capabilities: gains depend strongly on the alignment between pretraining objectives and downstream tasks. No single approach performs uniformly well across all three suites, and most methods are designed to only address a subset of them. Together, these findings suggest that learning representations that jointly generalize across behavior, dynamics, and anatomy remains an open challenge. By providing a unified and reproducible evaluation suite, BrainWideBench establishes a framework for measuring progress toward general-purpose models of the mouse brain.

2026-09-17

arXiv (preprint)

arxiv.org

Top-down feedback in deep neural networks leads to functional differences during audiovisual integration

Artificial neural networks (ANNs) are an important tool for studying neural computation, but many features of the brain are not captured by … (see more)standard ANN architectures. One notable missing feature in most ANN models is top-down feedback, that is projections from higher-order layers to lower-order layers in the network. Top-down feedback is ubiquitous in the brain, and it has a unique modulatory impact on activity in neocortical pyramidal neurons. However, we still do not understand its computational role. Here, we develop a deep neural network model that captures the core functional properties of top-down feedback in the neocortex, allowing us to construct hierarchical recurrent ANN models that more closely reflect the architecture of the brain. We use this to explore the impact of different hierarchical recurrent architectures on an audiovisual integration task. We find that certain hierarchies, namely those that mimic the architecture of the human brain, impart ANN models with a light visual bias similar to that seen in humans. This bias does not impair performance on the audiovisual tasks. The results further suggest that different configurations of top-down feedback make otherwise identically connected models functionally distinct from each other, and from traditional feedforward and laterally recurrent models. Altogether, our findings demonstrate that modulatory top-down feedback is a computationally relevant feature of biological brains, and that incorporating it into ANNs affects their behavior and constrains the solutions it is likely to discover.

2026-08-25

eLife (published)

A game theory for foundation models shows new paths to rational cooperation through similarity inference

Alexander Meulemans

Maciej Wołczyk

Marissa A. Weis

Rajai Nasser

Roberta Rocca

Seijin Kobayashi

Angelika Steger

Marcus Hutter

James Manyika

Rif A. Saurous

João Sacramento

Blaise Agüera y Arcas

As autonomous agents powered by foundation models are increasingly integrated into social and economic systems, understanding the principles… (see more) governing their collective behavior is essential for ensuring safety and cooperation. Classical game theory, the dominant framework for modeling rational interaction, is built upon the assumption of `decoupled agency,'where agents treat their own decision-making as independent of the environment and other actors. Modern AI agents, however, jointly predict their own future actions alongside external observations. Here, we report a striking finding: when interacting in stylized social dilemmas, foundation model agents engaging in optimal planning consistently converge to stable cooperation, directly contradicting classical game-theoretic predictions of mutual defection. To understand this phenomenon, we introduce the `embedded Bayesian agent,'a theoretical model for foundation model agents. By shifting from decoupled to embedded agency, these agents model themselves as part of the universe they inhabit, maintaining epistemic uncertainty about their own decision-making algorithms. We show that by inferring whether others are behaviorally similar, an embedded agent treats its own deliberation during planning as evidence: a decision to cooperate predicts a similar decision by a similar partner. We formalize this mechanism of similarity inference through the `embedded equilibrium,'a novel solution concept replacing the Nash equilibrium to provide a foundational game theory for the social behavior of modern AI agents.

2026-08-03

arXiv (preprint)

arxiv.org

Looking to the brain to improve energy efficiency of AI

Megan A.K. Peters

Seng Bum Michael Yoo

Michał Klincewicz

Taro Toyoizumi

Taylor Webb

Hakwan Lau

2026-06-30

Current Biology (published)

Balancing Plasticity and Stability with Fast and Slow Successor Features

Raymond Chua

Doina Precup

A hallmark of intelligence is the ability to adapt in non-stationary environments, yet deep Reinforcement Learning (RL) agents often struggl… (see more)e in such settings. Prior studies introduce non-stationarity through abrupt shifts in features or dynamics, whereas real-world environments often evolve gradually through continual drift. This distinction has important implications for the ``stability-plasticity dilemma'' in RL, as abrupt task changes may demand more plasticity than naturalistic settings. To address this, we modify existing 3D Miniworld and MuJoCo environments to incorporate naturalistic, continual non-stationarity, and use them to examine how stability and adaptation affect performance under continuous environmental change. We find that methods favoring stability, such as synaptic consolidation, outperform approaches focused on plasticity, such as parameters resetting. Motivated by this result, and prior evidence that Successor Features (SFs) reduce interference, we investigate whether SFs are better consolidation targets than Q-values. Across both environments, applying neuro-inspired synaptic consolidation to SFs yields superior performance on continually changing settings. Moreover, consolidation is most effective when SFs are stabilized across multiple timescales, which capture complementary aspects of gradual environmental change. Together, these results suggest that stability is more critical in continual learning when changes are gradual, and that multi-timescale consolidation of predictive representations is an effective approach.

2026-04-29

International Conference on Machine Learning (accepted)

openreview.net

Modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms

Classical psychedelics induce complex visual hallucinations in humans, generating percepts that are coherent at a low level, but which have … (see more)surreal, dream-like qualities at a high level. While there are many hypotheses as to how classical psychedelics could induce these effects, there are no concrete mechanistic models that capture the variety of observed effects in humans, while remaining consistent with the known pharmacological effects of classical psychedelics on neural circuits. In this work, we propose the ‘oneirogen hypothesis,’ which posits that the perceptual effects of classical psychedelics are a result of their pharmacological actions inducing neural activity states that truly are more similar to dream-like states. We simulate classical psychedelics’ effects via manipulating neural network models trained on perceptual tasks with the Wake-Sleep algorithm. This established machine learning algorithm leverages two activity phases: a perceptual phase (wake) where sensory inputs are encoded, and a generative phase (dream) where the network internally generates activity consistent with stimulus-evoked responses. We simulate the action of psychedelics by partially shifting the model to the ‘Sleep’ state, which entails a greater influence of top-down connections, in line with the impact of psychedelics on apical dendrites. The effects resulting from this manipulation capture a number of experimentally observed phenomena, including the emergence of hallucinations, increases in stimulus-conditioned variability, and large increases in synaptic plasticity. We further provide a number of testable predictions which could be used to validate or invalidate our oneirogen hypothesis.

2026-04-20

eLife (published)

The oneirogen hypothesis: modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms

Abstract Classical psychedelics induce complex visual hallucinations in humans, generating percepts that are co-herent at a … (see more)low level, but which have surreal, dream-like qualities at a high level. While there are many hypotheses as to how classical psychedelics could induce these effects, there are no concrete mechanistic models that capture the variety of observed effects in humans, while remaining consistent with the known pharmacological effects of classical psychedelics on neural circuits. In this work, we propose the “oneirogen hypothesis”, which posits that the perceptual effects of classical psychedelics are a result of their pharmacological actions inducing neural activity states that truly are more similar to dream-like states. We simulate classical psychedelics’ effects via manipulating neural network models trained on perceptual tasks with the Wake-Sleep algorithm. This established machine learning algorithm leverages two activity phases, a perceptual phase (wake) where sensory inputs are encoded, and a generative phase (dream) where the network internally generates activity consistent with stimulus-evoked responses. We simulate the action of psychedelics by partially shifting the model to the ‘Sleep’ state, which entails a greater influence of top-down connections, in line with the impact of psychedelics on apical dendrites. The effects resulting from this manipulation capture a number of experimentally observed phenomena including the emergence of hallucinations, increases in stimulus-conditioned variability, and large increases in synaptic plasticity. We further provide a number of testable predictions which could be used to validate or invalidate our oneirogen hypothesis.

2026-01-12

eLife (accepted)

Interpreting Physics in Video World Models

Sonia Joseph

Quentin Garrido

Randall Balestriero

Matthew Kowal

Thomas Fel

Shahab Bakhtiari

Mike Rabbat

A long-standing question in physical reasoning is whether video-based models need to rely on factorized representations of physical variable… (see more)s in order to make physically accurate predictions, or whether they can implicitly represent such variables in a distributed manner. While modern video world models achieve strong performance on intuitive physics benchmarks, it remains unclear which of these representational regimes they implement internally. Here, we present the first interpretability study to directly examine physical representations inside large-scale video encoders. Using layerwise probing, subspace geometry, patch-level decoding, and targeted attention ablations, we characterize where physical information becomes accessible and how it is organized within encoder-based video transformers. Across architectures, we identify a sharp intermediate-depth transition— which we call the \emph{Physics Emergence Zone}—at which physical variables become accessible. Physics-related representations peak shortly after this transition and degrade toward the output layers. Decomposing motion into explicit variables, we find that scalar quantities such as speed and acceleration are available from early layers onwards, whereas motion direction becomes accessible only at the Physics Emergence Zone. Notably, we find that direction is encoded through a high-dimensional population structure with circular geometry, requiring coordinated multi-feature intervention to control. These findings suggest that modern video models do not use factorized representations of physical variables like a classical physics engine. Instead, they use a distributed representation that is nonetheless sufficient for making physical predictions.

2025-12-31

International Conference on Machine Learning (Accept (regular))

openreview.net

RetINaBox: A hands-on learning tool for experimental neuroscience

Brune Bettler

Flavia Arias Armas

Erica Cianfarano

Vanessa Bordonaro

Megan Liu

Matthew Loukine

Mingyu Wan

Aude Villemain

Stuart Trenholm

An exciting aspect of neuroscience is developing and testing hypotheses via experimentation. However, due to logistical and financial hurdle… (see more)s, the experiment and discovery component of neuroscience is generally lacking in classroom and outreach settings. To address this issue, here we introduce RetINaBox: a low-cost open-source electronic visual system simulator that provides users with a hands-on tool to discover how the visual system builds feature detectors. RetINaBox features an LED array for generating visual stimuli and a photodiode array that acts as a mosaic of model photoreceptors. Custom software on a Raspberry Pi computer reads out responses from model photoreceptors and allows users to control the polarity and delay of the signal transfer from model photoreceptors to model retinal ganglion cells. Interactive lesson plans are provided, guiding users to discover different types of visual feature detectors—including ON/OFF, center-surround, orientation selective, and direction selective receptive fields—as well as their underlying circuit computations.

2025-12-31

eNeuro (published)

Know Thyself by Knowing Others: Learning Neuron Identity from Population Context

Vinam Arora

Divyansha Lachi

Ian J. Knight

Mehdi Azabou

Cole Hurwitz

Joshua H. Siegle

Eva L. Dyer

Identifying the functional identity of individual neurons is essential for interpreting circuit dynamics, yet it remains a major challenge i… (see more)n large-scale _in vivo_ recordings where anatomical and molecular labels are often unavailable. Here we introduce NuCLR, a self-supervised framework that learns context-aware representations of neuron identity by modeling each neuron's role within the broader population. NuCLR employs a spatio-temporal transformer that captures both within-neuron dynamics and across-neuron interactions. It is trained with a sample-wise contrastive objective that encourages temporally-stable and discriminative embeddings. Across multiple open-access datasets, NuCLR outperforms prior methods in both cell type and brain region classification. Critically, it exhibits strong zero-shot generalization to entirely new populations, without any retraining or access to stimulus labels. Furthermore, we demonstrate that our framework scales effectively with data size. Overall, our results demonstrate that modeling population context is crucial for understanding neuron identity and that rich signal for cell-typing and neuron localization is present in neural activity alone.Code available at: https://github.com/nerdslab/nuclr.

2025-12-02

NEURIPS (Accept (poster))

openreview.net

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

Mashbayar Tugsbayar

Mingze Li

Eilif B Muller

Artificial neural networks (ANNs) are an important tool for studying neural computation, but many features of the brain are not captured by … (see more)standard ANN architectures. One notable missing feature in most ANN models is top-down feedback, i.e. projections from higher-order layers to lower-order layers in the network. Top-down feedback is ubiquitous in the brain, and it has a unique modulatory impact on activity in neocortical pyramidal neurons. However, we still do not understand its computational role. Here we develop a deep neural network model that captures the core functional properties of top-down feedback in the neocortex, allowing us to construct hierarchical recurrent ANN models that more closely reflect the architecture of the brain. We use this to explore the impact of different hierarchical recurrent architectures on an audiovisual integration task. We find that certain hierarchies, namely those that mimic the architecture of the human brain, impart ANN models with a light visual bias similar to that seen in humans. This bias does not impair performance on the audiovisual tasks. The results further suggest that different configurations of top-down feedback make otherwise identically connected models functionally distinct from each other, and from traditional feedforward and laterally recurrent models. Altogether our findings demonstrate that modulatory top-down feedback is a computationally relevant feature of biological brains, and that incorporating it into ANNs affects their behavior and constrains the solutions it’s likely to discover.

2025-11-09

bioRxiv (preprint)

Learning to combine top-down context and feed-forward representations under ambiguity with apical and basal dendrites

Guillaume Etter

Busra Tugce Gurbuz

One of the hallmark features of neocortical anatomy is the presence of extensive top-down projections into primary sensory areas, with many … (see more)impinging on the distal apical dendrites of pyramidal neurons. While it is known that they exert a modulatory effect, altering the gain of responses, their functional role remains an active area of research. It is hypothesized that these top-down projections carry contextual information that can help animals to resolve ambiguities in sensory data. One proposed mechanism of contextual integration is a non-linear integration of distinct input streams at apical and basal dendrites of pyramidal neurons. Computationally, however, it is yet to be demonstrated how such an architecture could leverage distinct compartments for flexible contextual integration and sensory processing when both sensory and context signals can be unreliable. Here, we implement an augmented deep neural network with distinct apical and basal compartments that integrates a) contextual information from top-down projections to apical compartments, and b) sensory representations driven by bottom-up projections to basal compartments, via a biophysically inspired rule. In addition, we develop a new multi-scenario contextual integration task using a generative image modeling approach. In addition to generalizing previous contextual integration tasks, it better captures the diversity of scenarios where neither contextual nor sensory information are fully reliable. To solve this task, this model successfully learns to select among integration strategies. We find that our model outperforms those without the "apical prior" when contextual information contradicts sensory input. Altogether, this suggests that the apical prior and biophysically inspired integration rule could be key components necessary for handling the ambiguities that animals encounter in the diverse contexts of the real world.

2025-06-22

Cerebral Cortex (New York, NY) (published)