9,975 research outputs found
Spike sorting for large, dense electrode arrays
Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%
EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
EnsembleSVM is a free software package containing efficient routines to
perform ensemble learning with support vector machine (SVM) base models. It
currently offers ensemble methods based on binary SVM models. Our
implementation avoids duplicate storage and evaluation of support vectors which
are shared between constituent models. Experimental results show that using
ensemble approaches can drastically reduce training complexity while
maintaining high predictive accuracy. The EnsembleSVM software package is
freely available online at http://esat.kuleuven.be/stadius/ensemblesvm.Comment: 5 pages, 1 tabl
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