Neurobiological Signal Processing

This lecture covers the processing of neurobiological signals, focusing on modeling neurobiological spikes, classification of samples, de-noising signals, and data characterization using principal component analysis. The instructor explains the challenges in understanding brain function, experiments on animals, and research on stimulus-response relations. Various tools and methods for signal processing are discussed, such as the Poisson process, Markov chains, and mixture models. The lecture also delves into the use of electrodes to collect brain responses in insects and the importance of spike sorting. Additionally, the session explores the Poisson distribution, EM for Gaussian distributions, and numerical exercises for data classification.