About the lab
We aim to understand how the brain represents the environment around us to support navigation and memory, and how we can use brain-inspired solutions to address emerging challenges in AI.
Our research
How does the brain represent the environment?
How our brain represents the environment allows us to interact with it efficiently to achieve our goals and generalize knowledge to new contexts and problems. Yet, we cannot directly sense space – it must be constructed! We aim to understand how the brain represents diverse environments in patterns of neural activity, and how such representations are used for intelligent behaviour.
What is the importance of sparsity in cognition?
At a given moment, diverse brain areas show patterns of activity in a small fraction of neurons. Such sparse representation has important consequences for neural computation, including efficiency, storage capacity, and fidelity. We are interested in how the brain constructs sparse representations, and how this might be important for things like learning and memory.
How can AI benefit from brain-inspired solutions?
Many emerging problems in AI research are also present in the natural lives of many organisms and somehow solved in the brain. We examine how the brain-inspired solutions to problems such as learning, the representation of environments, and sparsity could be implemented to also support intelligent behaviour in artificial systems. Through modelling processes in artificial systems, we aim to advance our understanding of biological intelligence, too.
Our Approach
Advanced behavioural analysis
To examine the behaviour of learning, navigation, and memory, we use cutting-edge techniques to quantify complex behaviour in behaving animals and artificial agents. To do so, we combine approaches from classical conditioning, navigation in dynamic environments, and augmented reality.
High-yield neural recording
Advances in neural recording techniques now allow experimenters to record large populations of neurons (hundreds to thousands) in freely-behaving animals. Such approaches afford unprecedented abilities to track activity in large populations of neurons over long periods of time (days to weeks).
Functional molecular imaging
We use recently developed tools to measure the types and numbers of neurons across distributed brain systems involved in learning, memory and navigation. These techniques provide us with insights into how the brain creates and uses sparse representations for learning and remembering.
J. Quinn Lee, PhD
Dr. Quinn Lee is an Assistant Professor in the Department of Psychology (Faculty of Science) at the University of Alberta and a Fellow at the Alberta Machine Intelligence Institute (Amii). Dr. Lee leads the Navigation and Memory Systems (NMS) Lab, which combines cutting-edge methods for high-yield neural and behavioral recording to understand how we learn and remember in changing environments. To this end, his group leverages machine learning techniques both as tools for neuroscientific data analysis and theoretical modelling to advance our understanding of biological and artificial intelligence. Previously, Dr. Lee earned his Ph.D. in Neuroscience at the Canadian Centre for Behavioural Neuroscience at the University of Lethbridge with Drs. Robert J. Sutherland and Robert J. McDonald, and completed his postdoctoral research with Dr. Mark Brandon at McGill University.
we are accepting phd applications
If you are passionate about Neuroscience, Psychology, AI, or related fields and interested in joining a dynamic research team, we encourage you to reach out.
To do so, please email Dr. Quinn Lee at nmslab@ualberta.ca with a copy of your CV, academic transcripts, and brief statement of research interests.