Spike Sorting of Non-Stationary Data in Successive Intervals Based on Dirichlet Process Mixtures-Reference-Cited by-同舟云学术

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

Published:2022-02-09 Issue:6 Volume:16 Page:1393-1405
ISSN:1871-4080
Container-title:Cognitive Neurodynamics
language:en
Short-container-title:Cogn Neurodyn

Author:

Foroozmehr Foozie,Nazari Behzad^ORCID,Sadri Saeed,Rikhtehgaran Reyhaneh

Funder

Cognitive Sciences and Technologies Council

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience

Link

https://link.springer.com/content/pdf/10.1007/s11571-022-09781-7.pdf

Reference34 articles.

1. Bar-Hillel A, Spiro A, Stark E (2006) Spike sorting: Bayesian clustering of non-stationary data. J Neurosci Methods 157(2):303–316

2. Brown EN, Kass RE, Mitra PP (2004) Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci 7(5):456

3. Buccino AP, Einevoll GT (2021) Mearec: a fast and customizable testbench simulator for ground-truth extracellular spiking activity. Neuroinformatics 19(1):185–204

4. Calabrese A, Paninski L (2011) Kalman filter mixture model for spike sorting of non-stationary data. J Neurosci Methods 196(1):159–169

5. Garcia S, Pouzat C (2019) Tridesclous (software). Github. https://github.com/tridesclous/tridesclous

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1. Correction to: Spike Sorting of Non-Stationary Data in Successive Intervals Based on Dirichlet Process Mixtures;Cognitive Neurodynamics;2022-03-17

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