  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.
