Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models.
This course on introduces the dynamic features of the brain and their allied dynamical systems description including the twin perspectives from dynamical systems: geometric and algorithmic/computational. The course also covers linear dynamical systems in terms of approximations near equilibria and the usefulness of this approximation is in terms of geometrical approaches (eigenvector decompositions), including how this leads to line attractors and models of optimal decisions as well as “non-normal” surprises when eigenvectors are not orthogonal, as well as the treatment of time-dependent systems, including those driven by stochastic signals or noise, and close with examples on how networks and the activity that they produce co-evolve over time.