Blake Richards

Core Academic Member

Canada CIFAR AI Chair

Associate Professor, McGill University, School of Computer Science and Department of Neurology and Neurosurgery

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

Adrien Peyrache

Independent visiting researcher

adrien.peyrache@mila.quebec

Alex Efremov

PhD - McGill University

aleksei.efremov@mila.quebec

Github

Google Scholar

Ali Saheb Pasand

PhD - McGill University

Principal supervisor :

Pouya Bashivan

ali.sahebpasand@mila.quebec

Alissia Di Maria

Research Intern - McGill University

alissia.dimaria@mila.quebec

Ann Huang

Collaborating Alumni

zixiang.huang@mila.quebec

Arna Ghosh

PhD - McGill University

Chen Chen

Postdoctorate - Université de Montréal

Principal supervisor :

Yoshua Bengio

chen.sun@mila.quebec

Colin Bredenberg

Postdoctorate - Université de Montréal

Principal supervisor :

Guillaume Lajoie

colin.bredenberg@mila.quebec

Dane Malenfant

Master's Research - McGill University

dane.malenfant@mila.quebec

Github

Google Scholar

Daniel Levenstein

Postdoctorate - McGill University

daniel.levenstein@mila.quebec

Github

Google Scholar

Divyansha Lachi

Collaborating researcher - Georgia Tech

divyansha.lachi@mila.quebec

Website

Github

Google Scholar

Dongyan Lin

PhD - McGill University

lindongy@mila.quebec

Erica Cianfarano

PhD - McGill University

erica.cianfarano@mila.quebec

Ethan Caballero

PhD - McGill University

Co-supervisor :

Irina Rish

ethan.caballero@mila.quebec

Website

Google Scholar

Etienne Barou-Laforie

Research Intern - McGill University

etienne.barou-laforie@mila.quebec

Jonathan Cornford

Postdoctorate - McGill University

cornforj@mila.quebec

Josh Tindall

Master's Research - McGill University

joshua.tindall@mila.quebec

Mandana Samiei

PhD - McGill University

Principal supervisor :

PhD - McGill University

Github

Mohammad Yaghoubi

PhD - McGill University

yaghoubm@mila.quebec

Github

Quinn Lee

Postdoctorate - McGill University

j.quinn.lee@mila.quebec

Ray Chua

PhD - McGill University

Principal supervisor :

Doina Precup

chuaraym@mila.quebec

Roman Pogodin

Postdoctorate - McGill University

Co-supervisor :

Guillaume Lajoie

Roman.Pogodin@mila.Quebec

PhD - McGill University

roy.eyono@mila.quebec

Github

Google Scholar

Santiago Jaramillo

Independent visiting researcher - University of Oregon

santiago.jaramillo@mila.quebec

Website

Github

Sonia Joseph

PhD - McGill University

sonia.joseph@mila.quebec

Collaborating Alumni

Research Intern - University of Oslo

vemund.schoyen@mila.quebec

Krystal Pan

Master's Research - McGill University

xuejing.pan@mila.quebec

Blog Posts

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

Irrelevance by inhibition: Learning, computation, and implications for schizophrenia

Nathan Insel

Jordan Guerguiev

Blake Richards

Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked … (see more)to disruptions in cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which sensory inputs are relevant versus irrelevant. Here, we develop a 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 the expected magnitude of reward or punishment (“relevance”), which can be trained using a temporal difference learning mechanism acting on feedforward inputs to inhibitory interneurons. In the model, irrelevant and blocked stimuli drive lower levels of excitatory activity compared with novel and relevant stimuli, and this difference in activity levels is lost following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer with a threshold, the relevance code can be shown to “gate” both learning and behavioral responses to irrelevant stimuli. 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. (published)

doi.org

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

Sergey Bartunov

Adam Santoro

Blake Richards

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… (see more)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.

openreview.net

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