Effects of dendritic properties on spike train correlations in biophysically-based model neurons

Author:

Liu Shan1,Wang Jiang1,Fan Yaqin1,Yi Guosheng1ORCID

Affiliation:

1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P. R. China

Abstract

A large number of studies have shown that the correlation of spike trains in paired neurons is a key indicator of functional connectivity. Acting as the important components of neurons, dendrites affect the generation of spike train and the transmission of signals by changing the transfer function of neurons. However, it is not clear how the intrinsic properties of dendrites influence the degree of correlation of neuronal spike trains. This paper used a biophysically-based two-compartment model to simulate the firing output of neurons that receive correlated input currents, quantify the relationship between the active and passive properties of dendrites and the pairwise spike train correlations. We found that increasing the proportion of dendritic area or the coupling conductance between two compartments increases the output correlation. It is also shown that decreasing the intracellular calcium concentration and the conductance of calcium activated outward potassium channel or increasing the conductance of sodium and potassium channel in dendritic compartment increases the output correlation. This results from the increase of the number of dendritic spikes due to changing dendritic intrinsic properties, which causes more internal current flowing into the soma from dendrites, thereby leading to an increase of somatic firing rate. Our results reveal the influence of the intrinsic characteristics of dendrites on firing output of individual neuron, which further influences the correlation between the spike trains of a pairs or population of neurons. This study is of great significance for understanding how neuronal intrinsic properties affect neural network coding.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Condensed Matter Physics,Statistical and Nonlinear Physics

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