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Causality of Spike Trains Based on Entropy

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Abstract

Uncovering the causal relationship between spike train recordings from different neurons is a key issue for understanding the neural coding. This chapter presents a method, called permutation conditional mutual information (PCMI), for characterizing the causality between a pair of neurons. The performance of this method is demonstrated with the spike trains generated by the Izhikevich neuronal model, including estimation of the directionality index and detection of the temporal dynamics of the causal link. Simulations show that the PCMI method is superior to the transfer entropy (TE) and causal entropy (CE) methods at identifying the coupling direction between the spike trains. The advantages of PCMI are twofold: it is able to estimate the directionality index under the weak coupling and against the missing and extra spikes.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Zhaohui Li

  2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China

    Xiaoli Li

  3. Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875, China

    Xiaoli Li

Authors
  1. Zhaohui Li
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  2. Xiaoli Li
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Correspondence to Zhaohui Li or Xiaoli Li .

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Editors and Affiliations

  1. Beijing Normal University, State Key Lab Cognit Neurosci & Learning, Beijing, Beijing, China

    Xiaoli Li

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© 2016 Springer Science+Business Media Singapore

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Li, Z., Li, X. (2016). Causality of Spike Trains Based on Entropy. In: Li, X. (eds) Signal Processing in Neuroscience. Springer, Singapore. https://doi.org/10.1007/978-981-10-1822-0_3

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