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Frances K. Skinner

Frances Skinner is a Senior Scientist at the Krembil Research Institute and a Professor at the University of Toronto. After finishing highschool (in Trindad and Canada) she was set on becoming a veterinarian but abandoned those plans after being convinced to follow her love of math at the then newly established Faculty of Mathematics at the University of Waterloo. After graduating from Waterloo (B.Math.) with a double honours in Applied Mathematics and Computer Science, she missed biology in her studies, and moved to Toronto to do graduate work in Biomedical and Mechanical Engineering at the University of Toronto (M.A.Sc., Ph.D.). From there she got immersed in Computational Neuroscience and Neurobiology fields doing postdoctoral studies on the east (Brandeis University) and west (University of California, Davis) coasts of the US before returning to Toronto as an independent scientist at the Krembil (formerly the Toronto Western Research Institute/Playfair Neuroscience Unit). She enjoys collaborative work and is interested in determining cellular-based mechanisms underlying the dynamic output of neuronal networks in normal and pathological states. She is particularly interested in creating win-win scenarios with the plethora of data and theoretical and experimental approaches available today.
  • Senior Scientist, Krembil Brain Institute, Divison of Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network
  • Professor, Departments of Medicine (Neurology) and Physiology, University of Toronto
  • Adjunct Professor (2023+), Department of Physiology, University of Toronto
  • Emerita Scientist (2025+), Krembil Research Institute, University Health Network
  • Frances Skinner has strong research interests in creating bidirectional links between models and experiments. The work in her lab involves: (i) establishing intimate links with experimental studies to allow mathematical models with a neurological and pathophysiological functional basis to be developed, and (ii) simulating and analyzing developed mathematical models to enable insights and predictions to emerge. At present, there is a strong focus on the hippocampus, with a specialization on inhibitory GABAergic cells, and in the context of physiologically relevant population activity outputs of theta and gamma rhythms. This is being expanded to include the cortex. Inhibitory cells and networks have been found to play critical roles in learning and memory as well as in pathological conditions such as epilepsy and Alzheimer’s disease. The work is highly inter-disciplinary and collaborative by nature.

Computational neuroscience, nonlinear dynamics, inhibitory networks, interneurons, ion channel biophysics, hippocampus, brain rhythms, theta, gamma

Jeremie Lefebvre

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