Blake Richards

ali.sahebpasand@mila.quebec

Ali Saheb Pasand

PhD - McGill University

Principal supervisor :

Pouya Bashivan

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

daniel.levenstein@mila.quebec

Daniel Levenstein

Postdoctorate - McGill University

divyansha.lachi@mila.quebec

Divyansha Lachi

Collaborating researcher - Georgia Tech

Website

erica.cianfarano@mila.quebec

Dongyan Lin

PhD - McGill University

lindongy@mila.quebec

Erica Cianfarano

PhD - McGill University

Ethan Caballero

PhD - McGill University

Co-supervisor :

Irina Rish

ethan.caballero@mila.quebec

Website

etienne.barou-laforie@mila.quebec

Etienne Barou-Laforie

Research Intern - McGill University

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

Mohammad Yaghoubi

PhD - McGill University

yaghoubm@mila.quebec

Roman.Pogodin@mila.Quebec

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

PhD - McGill University

roy.eyono@mila.quebec

santiago.jaramillo@mila.quebec

Santiago Jaramillo

Independent visiting researcher - University of Oregon

Website

vemund.schoyen@mila.quebec

Sonia Joseph

PhD - McGill University

sonia.joseph@mila.quebec

Collaborating Alumni

Research Intern - University of Oslo

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

Adversarial Feature Desensitization

Pouya Bashivan

Reza Bayat

Adam Ibrahim

Kartik Ahuja

Mojtaba Faramarzi

Touraj Laleh

Irina Rish

Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can … (see more)drastically impair the network's performance. Many defense methods have been proposed for improving robustness of deep networks by training them on adversarially perturbed inputs. However, these models often remain vulnerable to new types of attacks not seen during training, and even to slightly stronger versions of previously seen attacks. In this work, we propose a novel approach to adversarial robustness, which builds upon the insights from the domain adaptation field. Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs. This is achieved through a game where we learn features that are both predictive and robust (insensitive to adversarial attacks), i.e. cannot be used to discriminate between natural and adversarial data. Empirical results on several benchmarks demonstrate the effectiveness of the proposed approach against a wide range of attack types and attack strengths. Our code is available at https://github.com/BashivanLab/afd.

openreview.net

Your head is there to move you around: Goal-driven models of the primate dorsal pathway

Patrick J Mineault

Shahab Bakhtiari

Christopher C. Pack

Neurons in the dorsal visual pathway of the mammalian brain are selective for motion stimuli, with the complexity of stimulus representation… (see more)s increasing along the hierarchy. This progression is similar to that of the ventral visual pathway, which is well characterized by artificial neural networks (ANNs) optimized for object recognition. In contrast, there are no image-computable models of the dorsal stream with comparable explanatory power. We hypothesized that the properties of dorsal stream neurons could be explained by a simple learning objective: the need for an organism to orient itself during self-motion. To test this hypothesis, we trained a 3D ResNet to predict an agent’s self-motion parameters from visual stimuli in a simulated environment. We found that the responses in this network accounted well for the selectivity of neurons in a large database of single-neuron recordings from the dorsal visual stream of non-human primates. In contrast, ANNs trained on an action recognition dataset through supervised or self-supervised learning could not explain responses in the dorsal stream, despite also being trained on naturalistic videos with moving objects. These results demonstrate that an ecologically relevant cost function can account for dorsal stream properties in the primate brain.

openreview.net

Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets

Marc-Andre Schulz

B.T. Thomas Yeo

Joshua T. Vogelstein

Janaina Mourao-Miranada

Jakob N. Kather

Konrad Paul Kording

Danilo Bzdok

2020-08-25

Nature Communications (published)

Distinct roles of parvalbumin and somatostatin interneurons in gating the synchronization of spike times in the neocortex

Hyun Jae Jang

Hyowon Chung

James M. Rowland

Michael M Kohl

Jeehyun Kwag

Sensory information–driven spikes are synchronized across cortical layers by distinct subtypes of interneurons. Synchronization of precise… (see more) spike times across multiple neurons carries information about sensory stimuli. Inhibitory interneurons are suggested to promote this synchronization, but it is unclear whether distinct interneuron subtypes provide different contributions. To test this, we examined single-unit recordings from barrel cortex in vivo and used optogenetics to determine the contribution of parvalbumin (PV)– and somatostatin (SST)–positive interneurons to the synchronization of spike times across cortical layers. We found that PV interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are low (12 Hz), whereas SST interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are high (>12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contributions to feedforward and feedback inhibition, respectively. Our findings demonstrate that distinct subtypes of inhibitory interneurons have frequency-selective roles in the spatiotemporal synchronization of precise spike times.

2020-04-22

Science Advances (published)

Systems consolidation impairs behavioral flexibility

Sankirthana Sathiyakumar

Sofia Skromne Carrasco

Lydia Saad

2020-04-15

Learning & memory (Cold Spring Harbor, N.Y.) (published)

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)