Yang, Yaoru and Halliday, David M. orcid.org/0000-0001-9957-0983 (2018) A Unified Framework of Third Order Time and Frequency Domain Analysis for Neural Spike Trains. In: 2018 IEEE Statistical Signal Processing Workshop, SSP 2018. 20th IEEE Statistical Signal Processing Workshop, SSP 2018, 10-13 Jun 2018 2018 IEEE Statistical Signal Processing Workshop, SSP 2018 . IEEE , DEU , pp. 383-387.
Abstract
Third order time and frequency analysis has exhibited great potential for correlation analysis of multi-sensor datasets, but is usually presented as separate time domain and frequency domain approaches. A combined framework of both frequency domain and time domain has rarely been used. This paper proposes a non-parametric third order time and frequency domain framework which used two dimensional Fourier transforms to bridge the gap between time domain and frequency domain. A unified framework offers flexibility and efficiency to apply to data. In this paper we study neural spike train data treated as stochastic point processes. In time domain direct analysis, third order cumulant densities of spike trains are applied, which need all first-, secondand third order product densities to be calculated before constructing the third order cumulant density, which brings additional challenges. The novelty in this study is that a new framework is proposed which can offer an alternative approach without calculating lower order quantities and can reveal nonlinear relationship between neural recordings. The results show that the present framework provides a novel non-parametric method to estimate both time and frequency domain measurements which is applicable to neural spike trains.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Publisher Copyright: © 2018 IEEE. |
Keywords: | frequency domain,higher order spectrum,spike trains,time domain |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 25 Jan 2022 10:10 |
Last Modified: | 25 Jan 2025 00:04 |
Published Version: | https://doi.org/10.1109/SSP.2018.8450717 |
Status: | Published |
Publisher: | IEEE |
Series Name: | 2018 IEEE Statistical Signal Processing Workshop, SSP 2018 |
Identification Number: | 10.1109/SSP.2018.8450717 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182884 |