Parameter estimation

This lecture covers the process of parameter estimation in neuron models, focusing on quadratic and convex optimization methods. It delves into the Spike Response Model (SRM) and the Generalized Linear Model (GLM), discussing the addition of noise to the SRM. The instructor explains the concept of linear fit in parameters and its application to neuron modeling, emphasizing the comparison between model and data. The lecture also explores the use of vector notation for parameter estimation and the importance of quadratic optimization in modeling subthreshold voltage. Extracted parameters from neuron models are analyzed, showcasing the practical application of parameter estimation in computational neuroscience.