PMID-22448159 Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes.
- Cutting edge windowing-then-sorting method.
- projection multimodality-weighted principal component analysis (mPCA, novel).
- Multimodality of a feature is by checking the informativeness using the KS test of a given feature.
- Also investigate graph laplacian features (GLF), which projects high-dimensional data onto a low-dimensional space while preserving topological structure.
- Clustering based on variational Bayes for Student's T mixture model (SVB).
- Does not rely on MAP inference and works reliably over difficult-to sort data, e.g. bursting neurons and sparsely firing neurons.
- Wavelet preprocessing improves spike separation.
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- open-source, available at http://etos.sourceforge.net/
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