Coding Capacity of Purkinje Cells with Different Schemes of Morphological Reduction

Abstract: The brain as a neuronal system has very complex structure with 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. For simulating a large-scale network with spiking neurons, the simple point neuron, such as 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 morphology reduction of detailed biophysical models is one of classic questions for systems neuroscience, much effort has been taken to simulate a neuron with a few compartments to include the interaction between soma and dendritic spines. Yet, novel reduction methods are still needed to deal with complex dendritic tree. Here by using ten 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 find 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.