Causality of Spike Trains Based on Entropy



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.


Spike train Causality Permutation Conditional mutual information 


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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
  3. 3.Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijingChina

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