Effect of the internal factors on the spike train correlation of pairwise directly connected neurons in a simplified micro-column model

The spike train correlation of pairwise neurons in the cerebral cortex is tightly related to cognitive abilities. Nowadays, more clarity is needed on the internal influencing mechanism of spike train correlation, especially in pairwise directly connected neurons. To this end, this paper explores effect of the internal factors on the spike train correlation of pairwise directly connected neurons in a micro-column (MC) model. In detail, a simplified MC model with morphology is constructed, then two groups of parameters are set to obtain two specific MCs called MC1 and MC2 respectively, where two output neurons (named as PN4 and PN5 separately) in the MCs are focused on. What is more, a spike count similarity (SCS) metric and a spike time correlation (STC) metric are proposed in this paper to quantify specific spike train correlation patterns with zero time lag. The main simulation results demonstrate that variations of the internal factors (including the synaptic strengths, the synaptic delay, as well as volumes of the soma and axon hillock) affect the spike train correlation to different extents, from which it is inferred that the spike train correlation of directly connected neurons could be effectively modulated by these internal factors. It is further shown that, compared to the inhibitory synapse, existence of the excitatory synapse may be necessary for the appearance of extremely high correlation for spike trains. In a specific range, the SCS metric is a decreasing function concerning the strength of the inhibitory synapse and an increasing function with respect to the strength of the excitatory synapse. In addition, a larger soma volume of PN4 corresponds to a weaker STC. Compared with variations of the volumes concerning the soma and axon hillock, the STC metric is more sensitive to the changes in the synaptic strengths as well as synaptic delays. Unlike the existing ones, the MC model constructed in this paper fully considers the neuronal morphology (such as the dendritic branches) which shapes the intrinsic/dynamical behaviors of the MCs. Furthermore, this established MC model would provide a foundation for investigating the higher-order spike train correlations among multiple neurons. The two metrics proposed here present a novel perspective for analyzing the correlation strength of pairwise spike trains. Also, they can be used to identify certain special spike train correlation patterns that might provide some helpful decoding strategies in the brain computer interface in the future.