A New Template Matching Method using Variance Estimation for Spike Sorting

Cho, Hansang and Corina, David P and Brinkley, James F and Ojemann, George A and Shapiro, Linda G (2005) A New Template Matching Method using Variance Estimation for Spike Sorting. In: IEEE EMBS Second International Neural Conference on Engineering.

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The analysis of single unit recording data requires a spike sorting method to separate blended neuronal spikes into separate neuron classes. A new template matching method for spike sorting based on shape distributions and a weighted Euclidean metric is proposed. The data is first roughly clustered using a Euclidean distance metric. Then the Levenberg-Marquardt method is used to estimate the variances of the neuron classes using curve fitting on the clustered data. Finally, the weighted Euclidean distance method is applied to minimize errors caused by different variances. This method provides optimized template matching results when the neuron variances are considerably different.

Item Type: Book Section
Uncontrolled Keywords: action potential neural spike discrimination
Subjects: All Projects > Brain Mapping
Depositing User: Jim Brinkley
Date Deposited: 15 Mar 2005
Last Modified: 25 Jul 2017 22:29
URI: http://sigpubs.si.washington.edu/id/eprint/169

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