Analysis of long-term depression in the Purkinje cell circuit (a model study)

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...

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Bibliographic Details
Date:2014
Main Authors: Zhang, X.C., Liu, Sh.Q., Ren, H., Zeng, Y.I., Zhan, G.X.
Format: Article
Language:English
Published: Інститут фізіології ім. О.О. Богомольця НАН України 2014
Series:Нейрофизиология
Online Access:https://nasplib.isofts.kiev.ua/handle/123456789/148249
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Journal Title:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Cite this:Analysis of long-term depression in the Purkinje cell circuit (a model study) / X.C. Zhang, Sh.Q. Liu, H. Ren, Y.I. Zeng, G.X. Zhan // Нейрофизиология. — 2014. — Т. 46, № 1. — С. 28-36. — Бібліогр.: 27 назв. — англ.

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Digital Library of Periodicals of National Academy of Sciences of Ukraine
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Summary: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.