Electrical activity of ON and OFF retinal ganglion cells: a modelling study
Electrical activity of ON and OFF retinal ganglion cells: a modelling study
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DOI：
10.1088/1741-2560/13/2/025005
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发表时间：
2016-04-01
影响因子：
4
通讯作者：
Dokos, Socrates
中科院分区：
文献类型：
--
作者：
Guo, Tianruo;Tsai, David;Dokos, Socrates
Objective.视网膜神经节细胞（RGC）的离子通道特性和形态具有很大的差异。RGC处理突触输入的独特方式，以及来自视觉假体的人工电信号，都是由细胞特异性决定的。细胞特异性的计算建模方法使我们能够检查区域膜通道表达和细胞形态的功能意义。Approach.在这项研究中，现有的RGC离子模型进行了扩展，包括超极化激活的非选择性阳离子电流以及T型钙电流在最近的实验结果中确定。生物病理学定义的模型参数，同时优化多个实验记录从ON和OFF RGC。主要结果。通过明确定义的细胞特异性模型参数和详细的细胞形态的结合，这些模型能够紧密地重建和预测实验记录的ON和OFF RGC响应特性。意义由此产生的模型被用来研究不同的离子通道特性和神经元的空间结构对RGC激活的贡献。本研究的技术一般适用于其他可兴奋细胞模型，提高了理论模型在准确预测真实的生物神经元反应中的实用性。
Objective. Retinal ganglion cells (RGCs) demonstrate a large range of variation in their ionic channel properties and morphologies. Cell-specific properties are responsible for the unique way RGCs process synaptic inputs, as well as artificial electrical signals such as that from a visual prosthesis. A cell-specific computational modelling approach allows us to examine the functional significance of regional membrane channel expression and cell morphology. Approach. In this study, an existing RGC ionic model was extended by including a hyperpolarization activated non-selective cationic current as well as a T-type calcium current identified in recent experimental findings. Biophysically-defined model parameters were simultaneously optimized against multiple experimental recordings from ON and OFF RGCs. Main results. With well-defined cell-specific model parameters and the incorporation of detailed cell morphologies, these models were able to closely reconstruct and predict ON and OFF RGC response properties recorded experimentally. Significance. The resulting models were used to study the contribution of different ion channel properties and spatial structure of neurons to RGC activation. The techniques of this study are generally applicable to other excitable cell models, increasing the utility of theoretical models in accurately predicting the response of real biological neurons.