Computational neuroscience is an interdisciplinary field that combines principles from neuroscience, computer science, and mathematics to study the brain and its functions - via mathematical modelling and mathematical methods of analysis. Through this approach, the field aims to uncover underlying mechanisms of brain function and provide insights into ways the brain processes information and gives rise to complex phenomena such as perception, learning, memory, and decision-making. 

Work in neuroscience focuses on various scales, from a single neuron, to populations of neurons in a specific brain region, to large scale networks between brain regions. While much progress has been made at each of these scales, some of the deepest and most challenging questions that remain concern bridging scales.

This Thematic Program will bring together experts in neuroscience and mathematics as well as a wide array of other disciplines to make key advances in these cutting-edge questions. Central to the theme of the program is the notion of emergence, as properties at large-scale emerge from and are supported by properties at smaller-scales. Also in focus are methods which enable multiscale analysis and modelling, including not only techniques to reduce or coarse-grained models, but also tools such as topological data analysis which capture patterns present in data across scales.

Many of the methods and insights touched on - as well as manifestations of emergence - have commonalities with those appearing at even smaller or larger scale, in such diverse fields as biochemistry, sociology, and cosmology. This Thematic Program will be resolutely interdisciplinary, with a core focus on the Mathematics of Neuroscience across scales, and active participation from researchers in Artificial Intelligence, Physics, Astrophysics, Medicine, Psychology, Philosophy, Biology and Chemistry.