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ArticleFrontiers in computational neuroscience2019

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

Lingling An, Yuanhong Tang, Quan Wang, Qingqi Pei, Ran Wei, Huiyuan Duan, Jian K Liu

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Article in Frontiers in computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Lingling AnSchool of Computer Science and Technology, Xidian University, Xi'an, China.
Yuanhong TangSchool of Computer Science and Technology, Xidian University, Xi'an, China.
Quan WangSchool of Computer Science and Technology, Xidian University, Xi'an, China.
Qingqi PeiSchool of Computer Science and Technology, Xidian University, Xi'an, China.
Ran WeiSchool of Computer Science and Technology, Xidian University, Xi'an, China.
Huiyuan DuanSchool of Computer Science and Technology, Xidian University, Xi'an, China.
Jian K LiuDepartment of Neuroscience, Psychology and Behaviour, Centre for Systems Neuroscience, University of Leicester, Leicester, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

dendritic modelmulti-compartmental modelsneuronal morphologyPurkinje cellrate codingtemporal coding

Identifiers

PMID31156415
PMCPMC6530636

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