A Unified Framework of Third Order Time and Frequency Domain Analysis for Neural Spike Trains

TY - GEN

T1 - A Unified Framework of Third Order Time and Frequency Domain Analysis for Neural Spike Trains

AU - Yang, Yaoru

AU - Halliday, David M.

N1 - Publisher Copyright: © 2018 IEEE.

PY - 2018/8/29

Y1 - 2018/8/29

N2 - 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.

AB - 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.

KW - frequency domain

KW - higher order spectrum

KW - spike trains

KW - time domain

UR - http://www.scopus.com/inward/record.url?scp=85053851706&partnerID=8YFLogxK

U2 - 10.1109/SSP.2018.8450717

DO - 10.1109/SSP.2018.8450717

M3 - Conference contribution

AN - SCOPUS:85053851706

SN - 9781538615706

T3 - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

SP - 383

EP - 387

BT - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

PB - IEEE

T2 - 20th IEEE Statistical Signal Processing Workshop, SSP 2018

Y2 - 10 June 2018 through 13 June 2018

ER -