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Pouya Bashivan

Associate Academic Member
Assistant Professor, McGill University, Department of Physiology
Research Topics
Computational Neuroscience

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

Maxime Daigle
Master's Research - McGill University
Ozhan Dehghani
Master's Research - McGill University
Github
Google Scholar
Lucas Gomez
PhD - McGill University
Website
Herbie he
Master's Research - McGill University
Website
Github
Xiaoxuan Lei
PhD - McGill University
Motahareh Pourrahimi
PhD - McGill University
Co-supervisor :
Irina Rish
Zihan Weng Weng
PhD - McGill University
Andrew Zeng
McGill University
Github
Google Scholar

Publications

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… (see more)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.
2019-04-18
ArXiv (preprint)
arxiv.org
arxiv.org