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Published in: Cognitive Neurodynamics 6/2022

09-02-2022 | Research Article

Spike Sorting of Non-Stationary Data in Successive Intervals Based on Dirichlet Process Mixtures

Authors: Foozie Foroozmehr, Behzad Nazari, Saeed Sadri, Reyhaneh Rikhtehgaran

Published in: Cognitive Neurodynamics | Issue 6/2022

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Abstract

This paper proposes a new automatic method for spike sorting and tracking non-stationary data based on the Dirichlet Process Mixture (DPM). Data is divided into non-overlapping intervals and mixtures are applied to individual frames rather than to the whole data. In this paper, we have used the information of the previous frame to estimate the cluster parameters of the current interval. Specifically, the means of the clusters in the previous frame are used for estimating the cluster means of the current one, and other parameters are estimated via noninformative priors. The proposed method is capable to track variations in size, shape, or location of clusters as well as detecting the appearance and disappearance of them. We present results in two-dimensional space of first and second principal components (PC1-PC2), but any other feature extraction method leading to the ability of modeling spikes with Normal or t-Student distributions can also be applied. Application of this approach to simulated data and the recordings from anesthetized rat hippocampus confirms its superior performance in comparison to a standard DPM that uses no information from previous frames.

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Metadata
Title
Spike Sorting of Non-Stationary Data in Successive Intervals Based on Dirichlet Process Mixtures
Authors
Foozie Foroozmehr
Behzad Nazari
Saeed Sadri
Reyhaneh Rikhtehgaran
Publication date
09-02-2022
Publisher
Springer Netherlands
Published in
Cognitive Neurodynamics / Issue 6/2022
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-022-09781-7

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