Stochastic Synchronization in Purkinje Cells with Feedforward Inhibition Could Be Studied with Equivalent Phase-Response Curves.

IF 2.3 4区 医学 Q1 Neuroscience Journal of Mathematical Neuroscience Pub Date : 2015-12-01 Epub Date: 2015-06-19 DOI:10.1186/s13408-015-0025-6
Sergio Verduzco-Flores
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引用次数: 2

Abstract

Simple-spike synchrony between Purkinje cells projecting to a common neuron in the deep cerebellar nucleus is emerging as an important factor in the encoding of output information from cerebellar cortex. A phenomenon known as stochastic synchronization happens when uncoupled oscillators synchronize due to correlated inputs. Stochastic synchronization is a viable mechanism through which simple-spike synchrony could be generated, but it has received scarce attention, perhaps because the presence of feedforward inhibition in the input to Purkinje cells makes insights difficult. This paper presents a method to account for feedforward inhibition so the usual mathematical approaches to stochastic synchronization can be applied. The method consists in finding a single Phase Response Curve, called the equivalent PRC, that accounts for the effects of both excitatory inputs and delayed feedforward inhibition from molecular layer interneurons. The results suggest that a theory of stochastic synchronization for the case of feedforward inhibition may not be necessary, since this case can be approximately reduced to the case of inputs characterized by a single PRC. Moreover, feedforward inhibition could in many situations increase the level of synchrony experienced by Purkinje cells.

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用等效相位响应曲线研究具有前馈抑制的浦肯野细胞的随机同步。
浦肯野细胞与小脑深部核共同神经元之间的单峰同步是编码小脑皮层输出信息的重要因素。当非耦合振荡器由于相关输入而同步时,就会发生随机同步现象。随机同步是一种可行的机制,通过它可以产生简单的脉冲同步,但它很少受到关注,也许是因为在浦肯野细胞的输入中存在前馈抑制,这使得人们很难深入了解。本文提出了一种考虑前馈抑制的方法,从而可以应用通常的随机同步数学方法。该方法包括找到一个称为等效PRC的单相响应曲线,该曲线可以解释来自分子层中间神经元的兴奋性输入和延迟前馈抑制的影响。结果表明,前馈抑制情况下的随机同步理论可能不是必要的,因为这种情况可以近似地简化为以单个PRC为特征的输入情况。此外,前馈抑制可以在许多情况下增加浦肯野细胞的同步性水平。
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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
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审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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