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Pouya Bashivan

Membre académique associé
Professeur adjoint, McGill University, Département de physiologie

Biographie

Pouya Bashivan est professeur adjoint au Département de physiologie et membre du programme intégré en neurosciences de l'Université McGill, ainsi que membre associé de Mila – Institut québécois d'intelligence artificielle. Avant de se joindre à l'Université McGill, il a été chercheur postdoctoral à Mila, travaillant avec Irina Rish et Blake Richards. Auparavant, il a été chercheur postdoctoral au Département des sciences du cerveau et de la cognition et à l'Institut McGovern pour la recherche sur le cerveau du Massachusetts Institute of Technology (MIT), où il a travaillé avec le professeur James DiCarlo. Il a obtenu un doctorat en génie informatique de l'Université de Memphis en 2016, après avoir obtenu une licence et une maîtrise en ingénierie électrique et de contrôle de l'Université KNT (Téhéran, Iran).

L'objectif de la recherche menée à son laboratoire est de développer des modèles de réseaux neuronaux qui exploitent la mémoire pour résoudre des tâches complexes. Alors que nous nous appuyons souvent sur des mesures de performance des tâches pour trouver des modèles de réseaux neuronaux et des algorithmes d'apprentissage améliorés, nous utilisons également des mesures neuronales et comportementales provenant de cerveaux d’humains et d'autres animaux pour évaluer la similitude de ces modèles avec des cerveaux biologiquement évolués. Nous pensons que ces contraintes supplémentaires pourraient accélérer les progrès vers l'ingénierie d'un agent artificiellement intelligent de niveau humain.

Étudiants actuels

Ali Saheb Pasand
Doctorat - McGill University
Co-superviseur⋅e :
Blake Richards
ali.sahebpasand@mila.quebec
Lucas Gomez
Maîtrise recherche - McGill University
lucas.gomez@mila.quebec
Site web
Maxime Daigle
Maîtrise recherche - McGill University
daiglema@mila.quebec
Motahareh Pourrahimi
Doctorat - McGill University
Co-superviseur⋅e :
Irina Rish
motahareh.pourrahimi@mila.quebec
Github
Reza Bayat
Maîtrise recherche - Université de Montréal
Superviseur⋅e principal⋅e :
Irina Rish
reza.bayat@mila.quebec
Site web
Xiaoxuan Lei
Doctorat - McGill University
xiaoxuan@mila.quebec

Publications

The feature landscape of visual cortex
Rudi Tong
Ronan da Silva
Dongyan Lin
Arna Ghosh
James Wilsenach
Erica Cianfarano
Pouya Bashivan
Blake Richards
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)
doi.org
Using modular connectome-based predictive modeling to reveal brain-behavior relationships of individual differences in working memory
Huayi Yang
Junjun Zhang
Zhenlan Jin
Pouya Bashivan
Ling Li
2023-06-22
Brain Structure and Function (publié)
doi.org
Towards Out-of-Distribution Adversarial Robustness
Adam Ibrahim
Charles Guille-Escuret
Ioannis Mitliagkas
Irina Rish
David Scott Krueger
Pouya Bashivan
Adversarial robustness continues to be a major challenge for deep learning. A core issue is that robustness to one type of attack often fail… (voir plus)s to transfer to other attacks. While prior work establishes a theoretical trade-off in robustness against different
2023-06-20
ICML.cc/2023/Workshop/AdvML-Frontiers (publié)
doi.org
openreview.net
How well do models of visual cortex generalize to out of distribution samples?
Yifei Ren
Pouya Bashivan
2023-05-03
bioRxiv (prépublication)
doi.org
Learning Robust Kernel Ensembles with Kernel Average Pooling
Pouya Bashivan
Adam Ibrahim
Amirozhan Dehghani
Yifei Ren
2022-09-30
ArXiv (preprint)
doi.org
openreview.net
Adversarial Feature Desensitization
Pouya Bashivan
Reza Bayat
Adam Ibrahim
Kartik Ahuja
Mojtaba Faramarzi
Touraj Laleh
Blake Richards
Irina Rish
Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can … (voir plus)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
Continual Learning with Self-Organizing Maps
Pouya Bashivan
Martin Schrimpf
Robert Ajemian
Irina Rish
Matthew D Riemer
Yuhai Tu
Despite remarkable successes achieved by modern neural networks in a wide range of applications, these networks perform best in domain-speci… (voir plus)fic stationary environments where they are trained only once on large-scale controlled data repositories. When exposed to non-stationary learning environments, current neural networks tend to forget what they had previously learned, a phenomena known as catastrophic forgetting. Most previous approaches to this problem rely on memory replay buffers which store samples from previously learned tasks, and use them to regularize the learning on new ones. This approach suffers from the important disadvantage of not scaling well to real-life problems in which the memory requirements become enormous. We propose a memoryless method that combines standard supervised neural networks with self-organizing maps to solve the continual learning problem. The role of the self-organizing map is to adaptively cluster the inputs into appropriate task contexts - without explicit labels - and allocate network resources accordingly. Thus, it selectively routes the inputs in accord with previous experience, ensuring that past learning is maintained and does not interfere with current learning. Out method is intuitive, memoryless, and performs on par with current state-of-the-art approaches on standard benchmarks.
2019-04-19
ArXiv (prépublication)
arxiv.org
arxiv.org