Modeling Neurobiological Signals: Markov Chains

This lecture covers the modeling of neurobiological signals using Markov Chains, focusing on multiple state neurons and parameter estimation. It explains how the firing rate's time-dependent behavior can be modeled as a Markov Chain, considering initial and transition probabilities. The lecture also delves into the estimation of parameters using likelihood functions and Lagrange constrained maximization. Real data parameter estimation challenges are discussed, along with exercises on modeling samples generated by different classes.