A review on visualization schemes and quality metrics used to evaluate the success of spike sorting procedures, published in Journal of Neuroscience.

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In this tutorial by Dr. Liam Paninski, the Expectation-Maximization (EM) algorithm is discussed and illustrated in a variety of neural examples. Read the rest of this entry…

The following set of tutorials focus on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms.
These include classification algorithms such as decision trees, neural nets, Bayesian classifiers, Support Vector Machines and cased-based (aka non-parametric) learning. They include regression algorithms such as multivariate polynomial regression, MARS, Locally Weighted Regression, GMDH and neural nets. And they include other data mining operations such as clustering (mixture models, k-means and hierarchical), Bayesian networks and Reinforcement Learning.

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NeuroMAX is a MATLAB-based software toolbox for the analysis of spike train data. NeuroMAX creates a chain of powerful analysis tools that meet your specific research goals. This tool chain, or Workspace, can be any combination of packaged NeuroMAX tools and your own custom MATLAB-based tools.

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