Abstract
In the cerebellum, long-term depression (LTD) plays a key function in sculpting neuronal circuits to store information, since motor learning and memory are thought to be associated with such long-term changes in synaptic efficacy. To better understand the principles of transmission of information in the cerebellum, we, in our model, distinguished different types of neurons (type 1- and type 2-like) to examine the neuronal excitability and analyze the interspike interval (ISI) bifurcation phenomenon in these units, and then built a Purkinje cell circuit to study the impact of external stimulation on LTD in this circuit. According to the results of computational analysis, both climbing fiber-Purkinje cell and granule cell-Purkinje cell circuits were found to manifest LTD; the external stimuli would influence LTD by changing both depression time and depression intensity. All of the simulated results showed that LTD is a very significant factor in the Purkinje circuit networks. Finally, to deliver the learning regularities, we simulated spike timing-dependent plasticity (STDP) by increasing the CaP conductance.
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