19_fncom-13-00029.pdf (6.81 MB)
Download file

Coding Capacity of Purkinje Cells With Different Schemes of Morphological Reduction.

Download (6.81 MB)
journal contribution
posted on 10.06.2019, 09:42 by L An, Y Tang, Q Wang, Q Pei, R Wei, H Duan, JK Liu
The brain as a neuronal system has very complex structures with a large diversity of neuronal types. The most basic complexity is seen from the structure of neuronal morphology, which usually has a complex tree-like structure with dendritic spines distributed in branches. To simulate a large-scale network with spiking neurons, the simple point neuron, such as the integrate-and-fire neuron, is often used. However, recent experimental evidence suggests that the computational ability of a single neuron is largely enhanced by its morphological structure, in particular, by various types of dendritic dynamics. As the morphology reduction of detailed biophysical models is a classic question in systems neuroscience, much effort has been taken to simulate a neuron with a few compartments to include the interaction between the soma and dendritic spines. Yet, novel reduction methods are still needed to deal with the complex dendritic tree. Here, using 10 individual Purkinje cells of the cerebellum from three species of guinea-pig, mouse and rat, we consider four types of reduction methods and study their effects on the coding capacity of Purkinje cells in terms of firing rate, timing coding, spiking pattern, and modulated firing under different stimulation protocols. We found that there is a variation of reduction performance depending on individual cells and species, however, all reduction methods can preserve, to some degree, firing activity of the full model of Purkinje cell. Therefore, when stimulating large-scale network of neurons, one has to choose a proper type of reduced neuronal model depending on the questions addressed. Among these reduction schemes, Branch method, that preserves the geometrical volume of neurons, can achieve the best balance among different performance measures of accuracy, simplification, and computational efficiency, and reproduce various phenomena shown in the full morphology model of Purkinje cells. Altogether, these results suggest that the Branch reduction scheme seems to provide a general guideline for reducing complex morphology into a few compartments without the loss of basic characteristics of the firing properties of neurons.


This work is supported by the National Key Research and Development Program of China (Grant No. 2016YFB0800601), the National Natural Science Foundation of China (Grant Nos. 61572385 and 6161101371), the Fundamental Research Funds for the Central Universities (Grant No. JB170309), the Science and Technology Projects of Xi'an, China (Grant No. 201809170CX11JC12), the International Talent Exchange Program of Beijing Municipal Commission of Science and Technology (Grant No. Z181100001018026), and the Royal Society Newton Advanced Fellowship (Grant No. NAF-R1-191082).



Frontiers in Computational Neuroscience, 2019, 13:29

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/Biological Sciences/Neuroscience, Psychology and Behaviour


VoR (Version of Record)

Published in

Frontiers in Computational Neuroscience


Frontiers Media



Acceptance date


Copyright date


Available date


Publisher version



The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fncom.2019.00029/full#supplementary-material