Neurobiological Signals: Processing and Classification

This lecture covers the processing and classification of neurobiological signals, focusing on modeling neurobiological spikes using Poisson processes and Markov chains, de-noising discrete valued signals, and characterizing data through principal component analysis. It also delves into experimental setups for studying the stimulus-response relation in insects, such as cockroaches and grasshoppers. The instructor explores the challenges of decoding brain functions and the importance of understanding the brain's response to different stimuli. Additionally, the lecture discusses data classification techniques like hidden Markov chains and mixture models, providing insights into the practical application of these concepts through Matlab practice.