Modeling Neurobiological Signals: Spikes & Firing Rate

Related lectures (32)

Page 3 of 4

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Covers distributions and techniques in Quantitative Risk Management for financial modeling.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Explores Statistical Signal & Data Processing fundamentals, tools, and applications in various fields.

Explores EMG signal analysis, mixture models, Gaussian models, and spike sorting in neural signal processing using PCA.

Covers the generation of Markov processes and Markov chains, including transition matrices and stochastic matrices.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Explores the optimization of neuroprosthetic systems, including sensory feedback restoration and neural stimulation strategies.