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

On the Information Geometry of Vision Transformers

Sonia Joseph

Kumar Krishna Agrawal

Arna Ghosh

2023-11-28

NeurIPS.cc/2023/Workshop/NeurReps (poster)

On the Varied Faces of Overparameterization in Supervised and Self-Supervised Learning

Matteo Gamba

Arna Ghosh

Kumar Krishna Agrawal

Agrawal

Hossein Azizpour

Mårten Björkman

The quality of the representations learned by neural networks depends on several factors, including the loss function, learning algorithm, a… (voir plus)nd model architecture. In this work, we use information geometric measures to assess the representation quality in a principled manner. We demonstrate that the sensitivity of learned representations to input perturbations, measured by the spectral norm of the feature Jacobian, provides valuable information about downstream generalization. On the other hand, measuring the coefficient of spectral decay observed in the eigenspectrum of feature covariance provides insights into the global representation geometry. First, we empirically establish an equivalence between these notions of representation quality and show that they are inversely correlated. Second, our analysis reveals the varying roles that overparameterization plays in improving generalization. Unlike supervised learning, we observe that increasing model width leads to higher discriminability and less smoothness in the self-supervised regime. Furthermore, we report that there is no observable double descent phenomenon in SSL with non-contrastive objectives for commonly used parameterization regimes, which opens up new opportunities for tight asymptotic analysis. Taken together, our results provide a loss-aware characterization of the different role of overparameterization in supervised and self-supervised learning.

2023-11-28

NeurIPS.cc/2023/Workshop/NeurReps (poster)

Learning from unexpected events in the neocortical microcircuit

Colleen J Gillon

Jason E. Pina

Jérôme A. Lecoq

Ruweida Ahmed

Yazan N. Billeh

Shiella Caldejon

Peter Groblewski

Timothy M. Henley

India Kato

Eric Lee

Jennifer Luviano

Kyla Mace

Chelsea Nayan

Thuyanh V. Nguyen

Kat North

Jed Perkins

Sam Seid

Matthew T. Valley

Ali Williford

Timothy P. Lillicrap

Joel Zylberberg

2023-11-21

Journal of Neuroscience (publié)

Responses to Pattern-Violating Visual Stimuli Evolve Differently Over Days in Somata and Distal Apical Dendrites

Colleen J Gillon

Jason E. Pina

Jérôme A. Lecoq

Ruweida Ahmed

Yazan N. Billeh

Shiella Caldejon

Peter Groblewski

Timothy M. Henley

India Kato

Eric Lee

Jennifer Luviano

Kyla Mace

Chelsea Nayan

Thuyanh V. Nguyen

Kat North

Jed Perkins

Sam Seid

Matthew T. Valley

Ali Williford

Timothy P. Lillicrap

Joel Zylberberg

Scientists have long conjectured that the neocortex learns patterns in sensory data to generate top-down predictions of upcoming stimuli. In… (voir plus) line with this conjecture, different responses to pattern-matching vs pattern-violating visual stimuli have been observed in both spiking and somatic calcium imaging data. However, it remains unknown whether these pattern-violation signals are different between the distal apical dendrites, which are heavily targeted by top-down signals, and the somata, where bottom-up information is primarily integrated. Furthermore, it is unknown how responses to pattern-violating stimuli evolve over time as an animal gains more experience with them. Here, we address these unanswered questions by analyzing responses of individual somata and dendritic branches of layer 2/3 and layer 5 pyramidal neurons tracked over multiple days in primary visual cortex of awake, behaving female and male mice. We use sequences of Gabor patches with patterns in their orientations to create pattern-matching and pattern-violating stimuli, and two-photon calcium imaging to record neuronal responses. Many neurons in both layers show large differences between their responses to pattern-matching and pattern-violating stimuli. Interestingly, these responses evolve in opposite directions in the somata and distal apical dendrites, with somata becoming less sensitive to pattern-violating stimuli and distal apical dendrites more sensitive. These differences between the somata and distal apical dendrites may be important for hierarchical computation of sensory predictions and learning, since these two compartments tend to receive bottom-up and top-down information, respectively.

2023-11-21

Journal of Neuroscience (publié)

The feature landscape of visual cortex

Rudi Tong

Ronan da Silva

Dongyan Lin

Arna Ghosh

James Wilsenach

Erica Cianfarano

Pouya Bashivan

Stuart Trenholm

Understanding computations in the visual system requires a characterization of the distinct feature preferences of neurons in different visu… (voir plus)al cortical areas. However, we know little about how feature preferences of neurons within a given area relate to that area’s role within the global organization of visual cortex. To address this, we recorded from thousands of neurons across six visual cortical areas in mouse and leveraged generative AI methods combined with closed-loop neuronal recordings to identify each neuron’s visual feature preference. First, we discovered that the mouse’s visual system is globally organized to encode features in a manner invariant to the types of image transformations induced by self-motion. Second, we found differences in the visual feature preferences of each area and that these differences generalized across animals. Finally, we observed that a given area’s collection of preferred stimuli (‘own-stimuli’) drive neurons from the same area more effectively through their dynamic range compared to preferred stimuli from other areas (‘other-stimuli’). As a result, feature preferences of neurons within an area are organized to maximally encode differences among own-stimuli while remaining insensitive to differences among other-stimuli. These results reveal how visual areas work together to efficiently encode information about the external world.

2023-11-05

bioRxiv (prépublication)

Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL

Chen Sun

Wannan Yang

Thomas Jiralerspong

Dane Malenfant

Benjamin Alsbury-Nealy

Yoshua Bengio

In real life, success is often contingent upon multiple critical steps that are distant in time from each other and from the final reward. T… (voir plus)hese critical steps are challenging to identify with traditional reinforcement learning (RL) methods that rely on the Bellman equation for credit assignment. Here, we present a new RL algorithm that uses offline contrastive learning to hone in on these critical steps. This algorithm, which we call Contrastive Retrospection (ConSpec), can be added to any existing RL algorithm. ConSpec learns a set of prototypes for the critical steps in a task by a novel contrastive loss and delivers an intrinsic reward when the current state matches one of the prototypes. The prototypes in ConSpec provide two key benefits for credit assignment: (i) They enable rapid identification of all the critical steps. (ii) They do so in a readily interpretable manner, enabling out-of-distribution generalization when sensory features are altered. Distinct from other contemporary RL approaches to credit assignment, ConSpec takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon (and ignoring other states) than it is to prospectively predict reward at every taken step. ConSpec greatly improves learning in a diverse set of RL tasks. The code is available at the link: https://github.com/sunchipsster1/ConSpec

Learning better with Dale’s Law: A Spectral Perspective

Pingsheng Li

Jonathan Cornford

Arna Ghosh

A Unified, Scalable Framework for Neural Population Decoding

Mehdi Azabou

Vinam Arora

Venkataramana Ganesh

Ximeng Mao

Santosh B Nachimuthu

Michael Jacob Mendelson

Matt Perich

Guillaume Lajoie

Eva L Dyer

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both the model … (voir plus)size and the datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large-scale multi-session model trained on large datasets from seven nonhuman primates, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale for neural decoding models.

The neuroconnectionist research programme

Adrien C. Doerig

R. Sommers

Katja Seeliger

J. Ismael

Grace W. Lindsay

Konrad Paul Kording

Talia Konkle

M. Gerven

Nikolaus Kriegeskorte

Tim Kietzmann

2023-05-30

Nature Reviews Neuroscience (publié)

arxiv.org

Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days

Colleen J Gillon

Jérôme A. Lecoq

Jason E. Pina

Ruweida Ahmed

Yazan N. Billeh

Shiella Caldejon

Peter Groblewski

Timothy M. Henley

India Kato

Eric Lee

Jennifer Luviano

Kyla Mace

Chelsea Nayan

Thuyanh V. Nguyen

Kat North

Jed Perkins

Sam Seid

Matthew T. Valley

Ali Williford

Timothy P. Lillicrap

Joel Zylberberg

2023-05-17

Scientific Data (publié)

Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days

Colleen J Gillon

Jérôme A. Lecoq

Jason E. Pina

Ruweida Ahmed

Yazan N. Billeh

Shiella Caldejon

Peter Groblewski

Timothy M. Henley

India Kato

Eric Lee

Jennifer Luviano

Kyla Mace

Chelsea Nayan

Thuyanh V. Nguyen

Kat North

Jed Perkins

Sam Seid

Matthew T. Valley

Ali Williford

Timothy P. Lillicrap

Joel Zylberberg

2023-05-17

Scientific Data (publié)

The study of plasticity has always been about gradients

Konrad Paul Kording

2023-05-01

The Journal of Physiology (publié)