The Computing Dendrite - From Structure to Function
The Computing Dendrite - From Structure to Function
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计算树突 - 从结构到功能
DOI：
10.1007/978-1-4614-8094-5_26
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
De Sousa G
中科院分区：
文献类型：
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作者：
De Sousa G
许多小脑学习理论认为，平行纤维（PF）和浦肯野细胞之间突触的长期抑制（LTD）是小脑模式识别的基础。在这里，我们描述了一系列的计算机模拟，使用一个形态逼真的电导为基础的模型小脑浦肯野细胞研究模式识别的基础上PF有限公司。我们的模拟结果，这是支持在体外和体内的电生理记录，表明浦肯野细胞可以使用一种新的神经代码，是基于沉默期的持续时间在他们的活动。的生物学详细浦肯野细胞模型的模拟进行了比较与相应的人工神经网络（ANN）模型的模拟。我们发现，这两个模型的预测在很大程度上不同。浦肯野细胞模型是非常敏感的LTD诱导的量，而人工神经网络不是。此外，ANN的模式识别性能随着模式变得稀疏而增加，而浦肯野细胞模型无法识别非常稀疏的模式。这些结果突出表明，重要的是要选择一个模型的生物细节的水平，适合正在解决的研究问题。
Many theories of cerebellar learning assume that long-term depression (LTD) of synapses between parallel fibres (PFs) and Purkinje cells is the basis for pattern recognition in the cerebellum. Here we describe a series of computer simulations that use a morphologically realistic conductance-based model of a cerebellar Purkinje cell to study pattern recognition based on PF LTD. Our simulation results, which are supported by electrophysiological recordings in vitro and in vivo, suggest that Purkinje cells can use a novel neural code that is based on the duration of silent periods in their activity. The simulations of the biologically detailed Purkinje cell model are compared with simulations of a corresponding artificial neural network (ANN) model. We find that the predictions of the two models differ to a large extent. The Purkinje cell model is very sensitive to the amount of LTD induced, whereas the ANN is not. Moreover, the pattern recognition performance of the ANN increases as the patterns become sparser, while the Purkinje cell model is unable to recognise very sparse patterns. These results highlight that it is important to choose a model at a level of biological detail that fits the research question that is being addressed.