Blake Richards

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

Aliya Affdal

Stagiaire de recherche - UdeM

Benjamin Alsbury-Nealy

Doctorat - McGill

benjamin@silicolabs.ca

Stella Bae

Visiteur de recherche indépendant - Seoul National University

Antoine Boudreau LeBlanc

Postdoctorat - McGill

aleksei.efremov@mail.mcgill.ca

Colin Bredenberg

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

Ethan Caballero

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - McGill

Postdoctorat - McGill

Alex Efremov

Doctorat - McGill

Arna Ghosh

Doctorat - McGill

Adel Halawa

Doctorat - McGill

Danny Han

Visiteur de recherche indépendant - Seoul National University

Doctorat - McGill

Angela Hu

Stagiaire de recherche - McGill

Ann Huang

Collaborateur·rice alumni

Doctorat - McGill

Visiteur de recherche indépendant - ETH Zurich

Divyansha Lachi

Collaborateur·rice de recherche - Georgia Tech

Postdoctorat - McGill

zixuan.li3@mail.mcgill.ca

Zixuan Li Li

Baccalauréat - McGill

Dongyan Lin

Doctorat - McGill

Maîtrise recherche - McGill

Toky Raharison Ralambomihanta

Abdel Mfougouon Njupoun

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Baccalauréat - McGill

Krystal Pan

Maîtrise recherche - McGill

Adrien Peyrache

Visiteur de recherche indépendant

Roman Pogodin

Postdoctorat - McGill

Co-superviseur⋅e :

Baccalauréat - McGill

tokiniaina.raharisonralambomihan@mail.mcgill.ca

Alexis Roger

Doctorat - McGill

Co-superviseur⋅e :

Irina Rish

Ali Saheb Pasand

Doctorat - McGill

Co-superviseur⋅e :

Pablo Samuel Castro

Mandana Samiei

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doina Precup

samiemandana@gmail.com

Aidan Sirbu

Maîtrise recherche - McGill

Co-superviseur⋅e :

Shahab Bakhtiari

Josh Tindall

Maîtrise recherche - McGill

Mashbayar Tugsbayar

Doctorat - McGill

Charlotte Volk

Maîtrise recherche - McGill

Co-superviseur⋅e :

Shahab Bakhtiari

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

Hee-Hwan Wang

Visiteur de recherche indépendant - Seoul National University

Maren Wehrheim

Visiteur de recherche indépendant - York University

Doctorat - McGill

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Billets de blogue

13 juin 2024

par

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Lire l'article

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

Lire l'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 (publié)

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 (publié)

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… (voir plus) 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 (publié)

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 … (voir 12 de plus)

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 (publié)

Dissociating memory accessibility and precision in forgetting

S. Berens

A. Horner

2019-06-04

Nature Human Behaviour (publié)

Dendritic solutions to the credit assignment problem

Timothy P. Lillicrap

2019-02-01

Current Opinion in Neurobiology (publié)

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 … (voir plus)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. (publié)

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