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

aleksei.efremov@mail.mcgill.ca

Colin Bredenberg

Postdoctorate - Université de Montréal

Principal supervisor :

Guillaume Lajoie

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

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Alexandre Payeur

Jordan Guerguiev

Friedemann Zenke

Richard Naud

2020-03-31

Nature Neuroscience (published)

Optogenetic activation of parvalbumin and somatostatin interneurons selectively restores theta-nested gamma oscillations and oscillation-induced spike timing-dependent long-term potentiation impaired by amyloid β oligomers

Kyerl Park

Jaedong Lee

Hyun Jae Jang

Michael M Kohl

Jeehyun Kwag

2020-01-15

BMC Biology (published)

Spike-based causal inference for weight alignment

Jordan Guerguiev

Konrad Paul Kording

In artificial neural networks trained with gradient descent, the weights used for processing stimuli are also used during backward passes to… (see more) calculate gradients. For the real brain to approximate gradients, gradient information would have to be propagated separately, such that one set of synaptic weights is used for processing and another set is used for backward passes. This produces the so-called "weight transport problem" for biological models of learning, where the backward weights used to calculate gradients need to mirror the forward weights used to process stimuli. This weight transport problem has been considered so hard that popular proposals for biological learning assume that the backward weights are simply random, as in the feedback alignment algorithm. However, such random weights do not appear to work well for large networks. Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem. The resulting algorithm is a special case of an estimator used for causal inference in econometrics, regression discontinuity design. We show empirically that this algorithm rapidly makes the backward weights approximate the forward weights. As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST, SVHN, CIFAR-10 and VOC. Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.

2020-01-01

ICLR.cc/2020/Conference (poster)

openreview.net

Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model

Lina M. Tran

Sheena A. Josselyn

Paul W. Frankland

2019-12-01

Behavioural Brain Research (published)

A deep learning framework for neuroscience

Timothy P. Lillicrap

Philippe Beaudoin

Yoshua Bengio

Rafal Bogacz

Amelia Christensen

Claudia Clopath

Rui Ponte Costa

Archy de Berker

Surya Ganguli

Colleen J Gillon

Danijar Hafner

Adam Kepecs

Nikolaus Kriegeskorte

Peter Latham

Grace W. Lindsay

Kenneth D. Miller

Richard Naud

Christopher C. Pack

Panayiota Poirazi … (see 12 more)

Pieter Roelfsema

João Sacramento

Andrew Saxe

Benjamin Scellier

Anna C. Schapiro

Walter Senn

Greg Wayne

Daniel Yamins

Friedemann Zenke

Joel Zylberberg

Denis Therien

Konrad Paul Kording

2019-10-28

Nature Neuroscience (published)

Dissociating memory accessibility and precision in forgetting

S. Berens

A. Horner

2019-06-04

Nature Human Behaviour (published)

Dendritic solutions to the credit assignment problem

Timothy P. Lillicrap

2019-02-01

Current Opinion in Neurobiology (published)

Irrelevance by inhibition: Learning, computation, and 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 … (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)

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 … (see more)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. (published)

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

Sergey Bartunov

Adam Santoro