Pouya Bashivan

Associate Academic Member

Assistant Professor, McGill University, Department of Physiology

Biography

Pouya Bashivan is an assistant professor in the Department of Physiology at McGill University, a member of McGill’s Integrated Program in Neuroscience, and an associate academic member of Mila – Quebec Artificial Intelligence Institute.

Before joining McGill University, Bashivan was a postdoctoral fellow at Mila, where he worked with Irina Rish and Blake Richards. Prior to that, he was a postdoctoral researcher in the Department of Brain and Cognitive Sciences and at the McGovern Institute for Brain Research at MIT, where he worked with James DiCarlo.

He received his PhD in computer engineering from the University of Memphis in 2016, and his BSc and MSc degrees in electrical and control engineering from K.N. Toosi University of Technology (Tehran).

The goal of research in Bashivan’s lab is to develop neural network models that leverage memory to solve complex tasks. While we often rely on task-performance measures to find improved neural network models and learning algorithms, we also use neural and behavioral measurements from humans and other animal brains to evaluate the similarity of these models to biologically evolved brains. We believe that these additional constraints could expedite the progress towards engineering a human-level artificially intelligent agent.

Current Students

Reza Bayat

Master's Research - Université de Montréal

Principal supervisor :

Irina Rish

reza.bayat@mila.quebec

Maxime Daigle

Master's Research - McGill University

daiglema@mila.quebec

Lucas Gomez

Master's Research - McGill University

lucas.gomez@mila.quebec

Xiaoxuan Lei

PhD - McGill University

xiaoxuan@mila.quebec

Motahareh Pourrahimi

PhD - McGill University

Co-supervisor :

Irina Rish

motahareh.pourrahimi@mila.quebec

Github

Ali Saheb Pasand

PhD - McGill University

Co-supervisor :

Blake Richards

ali.sahebpasand@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… (see more)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 (preprint)

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 (published)

doi.org

Towards Out-of-Distribution Adversarial Robustness

Adam Ibrahim

Charles Guille-Escuret

Adversarial robustness continues to be a major challenge for deep learning. A core issue is that robustness to one type of attack often fail… (see more)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 (published)

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 (preprint)

doi.org

Learning Robust Kernel Ensembles with Kernel Average Pooling

Pouya Bashivan