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Analyzing multiple spike trains with nonparametric granger causality

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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.

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Acknowledgements

AN was supported by Council of Scientific and Industrial Research. GR was supported by research grants from Defence Research and Development Organization (DRDO), Department of Science and Technology (MS:419/07) and University Grants Commission (under SAP-Phase IV). GR is also associated with the Jawaharlal Nehru Centre for Advanced Scientific Research as an Honorary Faculty Member. NJ is a Wellcome Trust International Senior Research Fellow (Grant No. 063259/Z/00/Z). NJ is also supported by a grant from DRDO. MD was supported by the US National Institute of Mental Health under Grants MH071620, MH070498, and MH079388. The Matlab code used for this study is available to interested readers upon request.

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Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Science, Bangalore, 560 012, India

    Aatira G. Nedungadi & Govindan Rangarajan

  2. National Brain Research Centre, Manesar, Haryana, 122050, India

    Neeraj Jain

  3. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA

    Mingzhou Ding

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  1. Aatira G. Nedungadi
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Correspondence to Govindan Rangarajan.

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Nedungadi, A.G., Rangarajan, G., Jain, N. et al. Analyzing multiple spike trains with nonparametric granger causality. J Comput Neurosci 27, 55–64 (2009). https://doi.org/10.1007/s10827-008-0126-2

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