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This lecture covers the modeling of neurobiological signals, focusing on neurobiological spikes and firing rate. It explains how the activity of a neuron can be measured by its average firing rate and how spikes can be associated with a Poisson process. The lecture also delves into multiple state neurons, where firing rates change over time and can be modeled as piecewise constant. Additionally, it discusses parameter estimation for Markov chains and the modeling of samples generated by different classes using mixture models.