Characterization of the causality between spike trains with permutation conditional mutual information

Abstract

Uncovering the causal relationship between spike train recordings from different neurons is a key issue for understanding the neural coding. This paper 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 Poisson point process model and 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 and causal entropy 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.

Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

“…Recently, several studies have proposed that these motifs can be used to build a method of association known as Permutation conditional mutual information [15][16][17]. Nevertheless, this method is too computationally expensive to be used within a TVG framework.…”

Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information

“…Using ordinal descriptors is helpful in the sense that it adds immunity to large artifacts occurring with low frequencies. PE is applicable for regular, chaotic, noisy or real-world time series and has been employed in the context of neural [4], electroencephalographic (EEG) [5][6][7][8], electrocardiographic (ECG) [9,10] and stock market time series [11]. In this paper, we suggest a modification that alters the way PE handles the patterns extracted from a given signal by incorporating amplitude information.…”

Analysis of bidirectional information transfer on drumming ensemble using robotic session system

“…We used the drumming time sequences from each trial and investigated the relationships. In this analysis, permutation conditional mutual information (PCMI) [12] was used to calculate the relationships between human and robot drumming sequences. The drumming time sequence of each trial was segmented equally into equal chunks of time length .…”