Non-parametric methods for the analysis of neurobiological time-series data

Bokil, H. S., Mitra, P. P. (2007) Non-parametric methods for the analysis of neurobiological time-series data. 46th IEEE Conference on Decision and Control, 2007. pp. 5833-5838.

DOI: 10.1109/CDC.2007.4434570


Recent technological advances have led to a large increase in the volume and quality of recordings from the brain. For example, while traditional electrophysiological recordings relied on painstaking observations of single neurons, it is now increasingly possible to record from tens or even a hundred neurons simultaneously. Similarly, electro and magnetoencephalographic recordings are routinely performed with upto three hundred sensors. This increase in data has also led to the need for bringing advanced time series analysis tools to bear on the problems of interpreting this data. In this paper, we illustrate the use of contemporary non-parametric smoothing and spectral estimation techniques in the analysis of data acquired in electrophysiological experiments. In particular, we discuss how local likelihood based methods have been used to model firing rates and how spectra and coherences can be used to assess degrees of association within and between spike trains and local field potentials. ©2007 IEEE.

Item Type: Paper
Additional Information: Meeting Abstract
Uncontrolled Keywords: Brain Data reduction Electrophysiology Magnetoencephalography Time series analysis Firing rates Spectral estimation techniques Spike trains Neurology
Subjects: physics > biophysics
organs, tissues, organelles, cell types and functions > organs types and functions > brain
Publication Type > Meeting Abstract
organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks
physics > neurophysics
CSHL Authors:
Communities: CSHL labs > Mitra lab
Depositing User: CSHL Librarian
Date: 2007
Date Deposited: 10 Apr 2012 15:17
Last Modified: 21 Mar 2018 15:47

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