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The Joint CAMBAM/NSERC-CREATE in Complex Dynamics Summer School

June 18 - June 29

Living systems are typical examples of dynamical systems with many interrelated parts or subsystems, from small-scale cellular relationships to large-scale population relationships. Nonlinear dynamics arise when the behaviour of one subsystem, with its own dynamics, becomes the input for another subsystem, imposing certain constraints on its dynamics. Mathematics, physics, and biological sciences have contributed important theoretical developments to the understanding of how nonlinear dynamics explain behaviour in a wide range of disciplines in natural sciences, social sciences, and life sciences, based on common principles arising from differential equations. Nonlinear dynamics underlie the developmental trajectory of living organisms; the spread of information in neural networks and disease in populations; and the prediction of evolving ecosystems in changing environments. While different challenges arise in each research area, the required quantitative models are shared across areas. These models, accompanied by statistical and computational tools, provide young scientists with a platform to understand the dynamics of their systems and to guide new experiments. As a result, the fields of mathematical and computational modeling have had significant impact across the natural and life sciences, including neuroscience, physiology, psychology, computer science, ecology, and evolutionary biology.

In this summer school, we aim to provide a new generation of trainees with the opportunity to learn more about the basics of this field and give them an overview of the latest advancements made in quantitative biosciences, with particular emphasis on neuroscience and psychology.

Aims of the Summer School

  • Introduce students (at the graduate and undergraduate level) and postdoctoral fellows to the fields of mathematical modelling and nonlinear dynamics through daily lectures and tutorial sessions.
  • Teach them how to use certain mathematical methods and computational tools to analyze various physiological processes in neuroscience and psychology.
  • Provide them with proper training in research methodologies through group projects supervised by the instructors of the summer school.
  • Teach them how real experimental data is collected and analyzed, and how it is used to foster collaborations with traditional experimental-based research laboratories.
  • Improve their presentation skills through feedback during project presentations.


June 18
June 29


McGill University
Montreal, Canada
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