Lecture
Modeling Neurobiological Signals: Spikes & Firing Rate
In course
DEMO: tempor velit incididunt
Ullamco commodo cillum dolore dolore esse laborum id anim aute enim. Ea aute deserunt amet anim irure exercitation. Velit voluptate culpa Lorem adipisicing nisi mollit sit nostrud.
Description
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.
Instructor
cillum velit
Fugiat dolor sint consectetur ut ad occaecat adipisicing et amet nulla ullamco. Consectetur dolore ex ex non voluptate eu irure eu sunt tempor ut eu. Esse mollit consequat aliqua deserunt commodo velit labore esse et ut nisi Lorem. Dolor ea deserunt eu ad exercitation laboris ea reprehenderit proident incididunt aliquip sunt velit deserunt. Amet et eiusmod enim laborum esse.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (35)
Gaussian Mixture Models: Data Classification
Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.
Spectral Estimation Methods
Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.
Interval Estimation: Method of Moments
Covers the method of moments for estimating parameters and constructing confidence intervals based on empirical moments matching distribution moments.
Statistical Signal Processing
Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.
Modeling Neurobiological Signals: Markov Chains
Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.