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Nedungadi, AG and Rangarajan, G and Jain, N and Ding, M (2009) Analyzing multiple spike trains with nonparametric granger causality. Journal of Computational Neuroscience, 27 (1). pp. 55-64.

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Abstract

Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

Item Type: Article
Subjects: Neurodegenerative Disorders
Neuro-Oncological Disorders
Neurocognitive Processes
Neuronal Development and Regeneration
Informatics and Imaging
Genetics and Molecular Biology
Depositing User: Dr. D.D. Lal
Date Deposited: 23 Oct 2017 04:45
Last Modified: 02 Jan 2018 09:51
URI: http://nbrc.sciencecentral.in/id/eprint/268

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