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

Aliya Affdal

Research Intern - Université de Montréal

Benjamin Alsbury-Nealy

PhD - McGill University

benjamin@silicolabs.ca

Stella Bae

Independent visiting researcher - Seoul National University

Antoine Boudreau LeBlanc

Postdoctorate - McGill University

Colin Bredenberg

Postdoctorate - Université de Montréal

Principal supervisor :

aleksei.efremov@mail.mcgill.ca

Ethan Caballero

PhD - McGill University

Co-supervisor :

PhD - McGill University

Principal supervisor :

PhD - McGill University

Jonathan Cornford

Postdoctorate - McGill University

Alex Efremov

PhD - McGill University

Arna Ghosh

PhD - McGill University

Adel Halawa

PhD - McGill University

Danny Han

Independent visiting researcher - Seoul National University

Roy Henha Eyono

PhD - McGill University

Angela Hu

Research Intern - McGill University

Ann Huang

Collaborating Alumni

Sonia Joseph

PhD - McGill University

Clara Kümpel

Independent visiting researcher - ETH Zurich

Divyansha Lachi

Collaborating researcher - Georgia Tech

Postdoctorate - McGill University

zixuan.li3@mail.mcgill.ca

Zixuan Li Li

Undergraduate - McGill University

Dongyan Lin

PhD - McGill University

Master's Research - McGill University

Toky Raharison Ralambomihanta

Abdel Mfougouon Njupoun

PhD - Université de Montréal

Principal supervisor :

Undergraduate - McGill University

Krystal Pan

Master's Research - McGill University

Adrien Peyrache

Independent visiting researcher

Roman Pogodin

Postdoctorate - McGill University

Co-supervisor :

Undergraduate - McGill University

tokiniaina.raharisonralambomihan@mail.mcgill.ca

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

Co-supervisor :

Shahab Bakhtiari

Josh Tindall

Master's Research - McGill University

Mashbayar Tugsbayar

PhD - McGill University

Charlotte Volk

Master's Research - McGill University

Co-supervisor :

Shahab Bakhtiari

Hee-Hwan Wang

Independent visiting researcher - Seoul National University

Maren Wehrheim

Independent visiting researcher - York University

Mohammad Yaghoubi

PhD - McGill University

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

AmirHossein Zamani

PhD - Concordia University

Principal supervisor :

Blog Posts

June 13, 2024

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

Sequential predictive learning is a unifying theory for hippocampal representation and replay

Daniel Levenstein

Aleksei Efremov

Roy Henha Eyono

Adrien Peyrache

The mammalian hippocampus contains a cognitive map that represents an animal’s position in the environment 1 and generates offline “repl… (see more)ay” 2,3 for the purposes of recall 4, planning 5,6, and forming long term memories 7. Recently, it’s been found that artificial neural networks trained to predict sensory inputs develop spatially tuned cells 8, aligning with predictive theories of hippocampal function 9–11. However, whether predictive learning can also account for the ability to produce offline replay is unknown. Here, we find that spatially-tuned cells, which robustly emerge from all forms of predictive learning, do not guarantee the presence of a cognitive map with the ability to generate replay. Offline simulations only emerged in networks that used recurrent connections and head-direction information to predict multi-step observation sequences, which promoted the formation of a continuous attractor reflecting the geometry of the environment. These offline trajectories were able to show wake-like statistics, autonomously replay recently experienced locations, and could be directed by a virtual head direction signal. Further, we found that networks trained to make cyclical predictions of future observation sequences were able to rapidly learn a cognitive map and produced sweeping representations of future positions reminiscent of hippocampal theta sweeps 12. These results demonstrate how hippocampal-like representation and replay can emerge in neural networks engaged in predictive learning, and suggest that hippocampal theta sequences reflect a circuit that implements a data-efficient algorithm for sequential predictive learning. Together, this framework provides a unifying theory for hippocampal functions and hippocampal-inspired approaches to artificial intelligence.

2024-06-04

bioRxiv (preprint)

Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning.

Mariangela Panniello

Colleen J Gillon

Roberto Maffulli

Marco Celotto

Stefano Panzeri

Michael M Kohl

2024-05-25

Cell Reports (published)

Sequential predictive learning is a unifying theory for hippocampal representation and replay

Daniel Levenstein

Aleksei Efremov

Roy Henha Eyono

Adrien Peyrache

The mammalian hippocampus contains a cognitive map that represents an animal’s position in the environment 1 and generates offline “repl… (see more)ay” 2,3 for the purposes of recall 4, planning 5,6, and forming long term memories 7. Recently, it’s been found that artificial neural networks trained to predict sensory inputs develop spatially tuned cells 8, aligning with predictive theories of hippocampal function 9–11. However, whether predictive learning can also account for the ability to produce offline replay is unknown. Here, we find that spatially tuned cells, which robustly emerge from all forms of predictive learning, do not guarantee the presence of a cognitive map with the ability to generate replay. Offline simulations only emerged in networks that used recurrent connections and head-direction information to predict multi-step observation sequences, which promoted the formation of a continuous attractor reflecting the geometry of the environment. These offline trajectories were able to show wake-like statistics, autonomously replay recently experienced locations, and could be directed by a virtual head direction signal. Further, we found that networks trained to make cyclical predictions of future observation sequences were able to rapidly learn a cognitive map and produced sweeping representations of future positions reminiscent of hippocampal theta sweeps 12. These results demonstrate how hippocampal-like representation and replay can emerge in neural networks engaged in predictive learning, and suggest that hippocampal theta sequences reflect a circuit that implements a data-efficient algorithm for sequential predictive learning. Together, this framework provides a unifying theory for hippocampal functions and hippocampal-inspired approaches to artificial intelligence.

2024-04-29

bioRxiv (preprint)

Fast burst fraction transients convey information independent of the firing rate

Richard Naud

Xingyun Wang

Zachary Friedenberger

Alexandre Payeur

Jiyun N. Shin

Jean-Claude Béïque

Moritz Drüke

Matthew E. Larkum

Guy Doron

Theories of attention and learning have hypothesized a central role for high-frequency bursting in cognitive functions, but experimental rep… (see more)orts of burst-mediated representations in vivo have been limited. Here we used a novel demultiplexing approach by considering a conjunctive burst code. We studied this code in vivo while animals learned to report direct electrical stimulation of the somatosensory cortex and found two acquired yet independent representations. One code, the event rate, showed a sparse and succint stiumulus representation and a small modulation upon detection errors. The other code, the burst fraction, correlated more globally with stimulation and more promptly responded to detection errors. Bursting modulation was potent and its time course evolved, even in cells that were considered unresponsive based on the firing rate. During the later stages of training, this modulation in bursting happened earlier, gradually aligning temporally with the representation in event rate. The alignment of bursting and event rate modulation sharpened the firing rate response, and was strongly associated behavioral accuracy. Thus a fine-grained separation of spike timing patterns reveals two signals that accompany stimulus representations: an error signal that can be essential to guide learning and a sharpening signal that could implement attention mechanisms.

2024-03-27

bioRxiv (preprint)

Sufficient conditions for offline reactivation in recurrent neural networks

Nanda H Krishna

Colin Bredenberg

Daniel Levenstein

During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagemen… (see more)t. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior is poorly understood. In this study, we develop a mathematical framework that outlines sufficient conditions for the emergence of neural reactivation in circuits that encode features of smoothly varying stimuli. We demonstrate mathematically that noisy recurrent networks optimized to track environmental state variables using change-based sensory information naturally develop denoising dynamics, which, in the absence of input, cause the network to revisit state configurations observed during periods of online activity. We validate our findings using numerical experiments on two canonical neuroscience tasks: spatial position estimation based on self-motion cues, and head direction estimation based on angular velocity cues. Overall, our work provides theoretical support for modeling offline reactivation as an emergent consequence of task optimization in noisy neural circuits.

2024-01-16

ICLR.cc/2024/Conference (poster)

Synaptic Weight Distributions Depend on the Geometry of Plasticity

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic … (see more)plasticity in the brain. However, the vast majority of this work ignores a critical underlying assumption: the choice of distance for synaptic changes - i.e. the geometry of synaptic plasticity. Gradient descent assumes that the distance is Euclidean, but many other distances are possible, and there is no reason that biology necessarily uses Euclidean geometry. Here, using the theoretical tools provided by mirror descent, we show that the distribution of synaptic weights will depend on the geometry of synaptic plasticity. We use these results to show that experimentally-observed log-normal weight distributions found in several brain areas are not consistent with standard gradient descent (i.e. a Euclidean geometry), but rather with non-Euclidean distances. Finally, we show that it should be possible to experimentally test for different synaptic geometries by comparing synaptic weight distributions before and after learning. Overall, our work shows that the current paradigm in theoretical work on synaptic plasticity that assumes Euclidean synaptic geometry may be misguided and that it should be possible to experimentally determine the true geometry of synaptic plasticity in the brain.

2024-01-16

ICLR.cc/2024/Conference (spotlight)

Addressing Sample Inefficiency in Multi-View Representation Learning

Arna Ghosh

Kumar Krishna Agrawal

Shagun Sodhani

Adam M. Oberman

2024-01-01

NeurIPS (published)

Sufficient conditions for offline reactivation in recurrent neural networks

Nanda H Krishna

Colin Bredenberg

Daniel Levenstein

During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagemen… (see more)t. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior are poorly understood. In this study, we develop a mathematical framework that outlines sufficient conditions for the emergence of neural reactivation in circuits that encode features of smoothly varying stimuli. We demonstrate mathematically that noisy recurrent networks optimized to track environmental state variables using change-based sensory information naturally develop denoising dynamics, which, in the absence of input, cause the network to revisit state configurations observed during periods of online activity. We validate our findings using numerical experiments on two canonical neuroscience tasks: spatial position estimation based on self-motion cues, and head direction estimation based on angular velocity cues. Overall, our work provides theoretical support for modeling offline reactivation as an emergent consequence of task optimization in noisy neural circuits.

2024-01-01

International Conference on Learning Representations (published)

Harnessing small projectors and multiple views for efficient vision pretraining

Kumar Krishna Agrawal

Arna Ghosh

Shagun Sodhani

Adam M. Oberman

Recent progress in self-supervised (SSL) visual representation learning has led to the development of several different proposed frameworks … (see more)that rely on augmentations of images but use different loss functions. However, there are few theoretically grounded principles to guide practice, so practical implementation of each SSL framework requires several heuristics to achieve competitive performance. In this work, we build on recent analytical results to design practical recommendations for competitive and efficient SSL that are grounded in theory. Specifically, recent theory tells us that existing SSL frameworks are minimizing the same idealized loss, which is to learn features that best match the data similarity kernel defined by the augmentations used. We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute. We study the implicit bias of using gradient descent to minimize our reformulated loss function and find that using a stronger orthogonalization constraint with a reduced projector dimensionality should yield good representations. Furthermore, the theory tells us that approximating the reformulated loss should be improved by increasing the number of augmentations, and as such using multiple augmentations should lead to improved convergence. We empirically verify our findings on CIFAR, STL and Imagenet datasets, wherein we demonstrate an improved linear readout performance when training a ResNet-backbone using our theoretically grounded recommendations. Remarkably, we also demonstrate that by leveraging these insights, we can reduce the pretraining dataset size by up to 2

2023-12-17

ArXiv (preprint)

arxiv.org

Temporal encoding in deep reinforcement learning agents

Dongyan Lin

Ann Zixiang Huang

2023-12-15

Scientific Reports (published)

Temporal encoding in deep reinforcement learning agents

Dongyan Lin

Ann Zixiang Huang

2023-12-15

Scientific Reports (published)

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

Nizar Islah

Guillaume Etter

Mashbayar Tugsbayar

Tugce Gurbuz