Portrait de Blake Richards

Blake Richards

Membre académique principal

blake.richards@mila.quebec

Chaire en IA Canada-CIFAR

Professeur adjoint, McGill University, École d'informatique et Département de neurologie et de neurochirurgie

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Sujets de recherche

Apprentissage de représentations

Apprentissage par renforcement

Modèles génératifs

Neurosciences computationnelles

Biographie

Blake Richards est professeur agrégé à l'École d'informatique et au Département de neurologie et de neurochirurgie de l'Université McGill et membre du corps professoral de Mila – Institut québécois d’intelligence artificielle. Ses recherches se situent à l'intersection des neurosciences et de l'intelligence artificielle. Son laboratoire étudie les principes universels de l'intelligence qui s'appliquent aux agents naturels et artificiels. Il a reçu plusieurs distinctions pour ses travaux, notamment une bourse Arthur-B.-McDonald du Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG) en 2022, le Prix du jeune chercheur de l'Association canadienne des neurosciences en 2019 et une chaire en IA Canada-CIFAR en 2018. M. Richards a en outre été titulaire d'une bourse postdoctorale Banting à l'hôpital SickKids de 2011 à 2013. Il a obtenu un doctorat en neurosciences de l'Université d'Oxford en 2010 et une licence en sciences cognitives et en IA de l'Université de Toronto en 2004.

Étudiants actuels

Stagiaire de recherche - UdeM

Benjamin Alsbury-Nealy

Doctorat - McGill

benjamin@silicolabs.ca

Antoine Boudreau LeBlanc

Postdoctorat - McGill

Colin Bredenberg

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

Ethan Caballero

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Superviseur⋅e principal⋅e :

Erica Cianfarano

Doctorat - McGill

Jonathan Cornford

Postdoctorat - McGill

Doctorat - McGill

aleksei.efremov@mail.mcgill.ca

Doctorat - McGill

Doctorat - McGill

Visiteur de recherche indépendant - Seoul National University

Roy Henha Eyono

Doctorat - McGill

Collaborateur·rice alumni

Doctorat - McGill

Visiteur de recherche indépendant - ETH Zurich

Divyansha Lachi

Collaborateur·rice de recherche - Georgia Tech

Daniel Levenstein

Postdoctorat - McGill

Baccalauréat - McGill

zixuan.li3@mail.mcgill.ca

Doctorat - McGill

Maîtrise recherche - McGill

Abdel Mfougouon Njupoun

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

Le Thuy Duong Nguyen

Collaborateur·rice alumni - McGill

Adrien Peyrache

Visiteur de recherche indépendant

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Guillaume Lajoie

Doctorat - McGill

Co-superviseur⋅e :

Ali Saheb Pasand

Doctorat - McGill

Co-superviseur⋅e :

Pablo Samuel Castro

Doctorat - McGill

Superviseur⋅e principal⋅e :

samiemandana@gmail.com

Maîtrise recherche - McGill

Co-superviseur⋅e :

Shahab Bakhtiari

Maîtrise recherche - McGill

Mashbayar Tugsbayar

Doctorat - McGill

Maîtrise recherche - McGill

Co-superviseur⋅e :

Shahab Bakhtiari

Visiteur de recherche indépendant - Seoul National University

Visiteur de recherche indépendant - York University

Mohammad Yaghoubi

Doctorat - McGill

AmirHossein Zamani

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Eugene Belilovsky

Billets de blogue

Représentation graphique d'un nerf vague

21 mai 2025

Apprentissage automatique pour la segmentation des différentes activations des fibres nerveuses à partir des signaux neuronaux du cerveau vers le corps

par

Olivier Tessier-Larivière

Pascal Fortier-Poisson

Guillaume Lajoie

Visuel abstrait

13 juin 2024

Que nous apprennent les distributions des coefficients synaptiques au sujet de l’apprentissage dans le cerveau ?

par

Jonathan Cornford

Arna Ghosh

Guillaume Lajoie

α-ReQ: Assessing Representation Quality in SSL

29 août 2023

α-ReQ : Évaluation de la qualité des représentations en apprentissage auto-supervisé

par

KK Agrawal

Arnab Kumar-Mondal

Arna Ghosh

Blake A. Richards

Publications

Relevance learning via inhibitory plasticity and its implications for schizophrenia

Nathan Insel

Jordan Guerguiev

Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked … (voir plus)to disruptions to cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which inputs are relevant and which irrelevant. Here, we develop an abstract but biologically plausible neural network model that demonstrates how the cortex may learn to ignore irrelevant inputs through plasticity processes affecting inhibition. The model is based on the proposal that the amount of excitatory output from a cortical circuit encodes expected magnitude of reward or punishment (”relevance”), which can be trained using a temporal difference learning mechanism acting on feed-forward inputs to inhibitory interneurons. The model exhibits learned irrelevance and blocking, which become impaired following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer, the relevance code is capable of modulating learning and activity. Accordingly, the combined network is capable of recapitulating published experimental data linking inhibition in frontal cortex with fear learning and expression. Finally, the model demonstrates how relevance learning can take place in parallel with other types of learning, through plasticity rules involving inhibitory and excitatory components respectively. Altogether, this work offers a theory of how the cortex learns to selectively inhibit inputs, providing insight into how relevance-assignment problems may emerge in schizophrenia.

2018-08-01

PLoS Comput. Biol. (publié)

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures

Sergey Bartunov

Adam Santoro

Luke Marris

Geoffrey Hinton

Timothy P. Lillicrap

The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learn… (voir plus)ing and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.