Primary Supervisor: Dr Richard Naud, Faculty of Medicine, Faculty of Science

Secondary supervisor: Prof Jean-Claude Béïque, Katalin Toth

University of Registration: University of Ottawa, Faculty of Medicine

Project outline:

Multiple Ph.D. and postdoctoral positions are available in the Neural Coding lab. The main goal of our research group is to understand how properties of neurons and synapses allows the brain to learn to complete difficult tasks. We are particularly interested in how cognitive-level functions - as formalized by artificial intelligence - can be implemented by neurons in particular systems [1-2]. In particular, our work explores the relationship between properties of cortical neurons and the diverse methods of credit assignment that have been used to train artificial neural networks. We are also interested in the relationship between mood-related networks (serotonergic centers, dopaminergic centers) and types of reinforcement learning.

To study this, we perform computational modeling closely related to experimental data. Our work typically involves the development of both data analysis methods and the study of simulated neuronal networks. For an example of previous work see Ref [1-4].

The PhD project will be well suited to candidates ideally having experience in scientific programming, mathematical modeling, phsyics, mathematics, computer science or similar disciplines. The research projects require a meaningful navigation of electrophysiological, opto-physiological, behavioral and anatomical data. Programming skills (preferably Python) skills are highly desirable and knowledge of Machine Learning/Deep Learning or numerical simulation of physical systems is a plus.

We are committed to working in an inclusive environment that encourages diversity while ensuring that everyone is treated equally. Our team is down-to-earth, diverse, inclusive and very dynamic. Please write to Dr. Richard Naud if you have questions.

References:

1.    Payeur, Alexandre, et al. "Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits." Nature Neuroscience 24.12 (2021): 1780-1780.

2.    Harkin, Emerson F., et al. "Temporal derivative computation in the dorsal raphe network revealed by an experimentally-driven augmented integrate-and-fire modeling framework." bioRxiv (2021).

3.    Harkin, E. F., et al. "Parallel and recurrent cascade models as a unifying force for understanding sub-cellular computation." Neuroscience (2021).

4.    Lynn, Michael B., et al. "A Synthetic Likelihood Solution to the Silent Synapse Estimation Problem." Cell reports 32.3 (2020): 107916.

Contact: Dr Richard Naud, University of Ottawa with up-to-date CV and research statement.