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.
Highlights
A single neuron is thought to be the basic computation unit in the complex neuronal system
We considered four different reduction schemes to simplify the morphology of Purkinje cells (PCs) from three species, studied their effects on PC responses to synaptic inputs
To investigate how the coding capacity of PCs is affected by morphological structure reductions, we evaluated the performance of the reduced model with four measures: simplification, accuracy, efficiency, and spike shape change
Summary
A single neuron is thought to be the basic computation unit in the complex neuronal system. Understanding the computations and dynamics of single neurons is an integral part of explaining their functional roles within a large neural network. The question is, what are the key properties of neuronal morphology for understanding a complex process such as information processing and dynamic behavior that emerges from a network of neurons? The IAF model of neuronal dynamics is considered to be a simple way to explain certain aspects of neuronal behaviors (Burkitt, 2006a,b). It has been used mostly for the simulation of large-scale neuronal networks. The detailed model can describe the dynamics of individual neurons very well, their high dimensionality and complex spatial structure makes the calculation expensive and unsuitable for large-scale network simulations
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