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Linear Systems I (Intro Lecture)

Difficulty level

This lecture provides an introduction to linear systems.

Topics covered in this lesson
  • Dynamic features of the brain and their allied dynamical systems description
  • Geometric and algorithmic/computational perspectives
  • Motivation for linear dynamical systems in terms of approximations near equilibria
  • Utility of this approximation is in terms of geometrical approaches (eigenvector decompositions)
  • Treatment of time-dependent systems, including those driven by stochastic signals or noise
  • Examples on how networks and the activity that they produce co-evolve over time

Experience with Python Programming Language.

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