“Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale. They have advantages over traditional Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes.”

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An excellent tutorial on Wavelet Analysis covering also the basic concepts of mathematical transformations, time-frequency representations and non-stationary signal properties.
wavelet transform

By Robi Polikar
Dept. of Electrical and Computer Engineering
Rowan University

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Wave_clus is a fast and unsupervised algorithm for spike detection and sorting. Although it gives a first unsupervised solution, this can be further modified according to the experimenter’s preference (semi-automatic sorting).
Wave_clus screenshot

By Rodrigo Quian Quiroga,
Reader in Bioengineering,
Dept. Engineering. University of Leicester, UK.

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BrainML is a developing initiative to provide a standard XML metaformat for exchanging neuroscience data. It focuses on layered definitions built over a common core in order to support community-driven extension.
One such extension is provided by the new NIH-supported neuroinformatics initiative of the Society for Neuroscience, which supports the development of expert-derived terminology sets for several areas of neuroscience.
BrainML
The BrainML project is funded by the Human Brain Project-Informatics initiative via MH/NS57153 from the NIMH and NINDS and is directed by:
Daniel Gardner
Weill Medical College of Cornell University

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