MathDetour 1: Separation of time scales

This lecture covers the concept of separation of time scales in computational neuroscience, focusing on reducing detail in two-dimensional neuron models. The instructor explains the transition from Hodgkin-Huxley to 2D models, the role of nullclines, stability of fixed points, and different types of neuron models. The lecture also delves into nonlinear integrate-and-fire models.