Phenomenological incorporation of nonlinear dendritic integration using integrate-and-fire neuronal frameworks.
Phenomenological incorporation of nonlinear dendritic integration using integrate-and-fire neuronal frameworks.
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DOI：
10.1371/journal.pone.0053508
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Cai D
中科院分区：
文献类型：
--
作者：
Zhou D;Li S;Zhang XH;Cai D
最近在实验中发现，兴奋性突触后电位和抑制性GABA能电位在海马CA1区锥体神经元中的树突整合遵循一个简单的算术规则，其中，和分别是EPSP达到峰值时测量的躯体电位、兴奋性突触后电位（EPSP）和抑制性突触后电位的电压值。此外，该规则中的分流系数仅取决于树突上的兴奋性或抑制性输入的空间位置，而不取决于树突上的兴奋性或抑制性输入的幅度。在这项工作中，我们解决的理论问题，有多少上述树突状细胞的整合规则可以占使用阈下膜电位动态的索马的特点是由基于电导的整合和消防（I&F）模型。然后，我们提出了一个简单的I&F神经元模型，采用了空间依赖的分流系数的现象学参数化。我们的分析和数值计算结果表明，这种树枝状积分规则为基础的I&F（DIF）模型是能够捕捉到许多实验观察，它也产生的预测，可用于验证DIF模型的有效性实验。此外，DIF模型动态地结合了树突整合效应，并且适用于比兴奋性和抑制性输入在时间上同时发生的实验中更一般的情况。最后，我们将DIF神经元模型推广到包含多个输入，并获得类似的树突整合规则，该规则与使用具有多个隔室的现实神经元模型获得的结果一致。这种广义DIF模型可以潜在地用于研究网络动力学，可能涉及的树突状整合所产生的影响。
It has been discovered recently in experiments that the dendritic integration of excitatory glutamatergic inputs and inhibitory GABAergic inputs in hippocampus CA1 pyramidal neurons obeys a simple arithmetic rule as , where , and are the respective voltage values of the summed somatic potential, the excitatory postsynaptic potential (EPSP) and the inhibitory postsynaptic potential measured at the time when the EPSP reaches its peak value. Moreover, the shunting coefficient in this rule only depends on the spatial location but not the amplitude of the excitatory or inhibitory input on the dendrite. In this work, we address the theoretical issue of how much the above dendritic integration rule can be accounted for using subthreshold membrane potential dynamics in the soma as characterized by the conductance-based integrate-and-fire (I&F) model. Then, we propose a simple I&F neuron model that incorporates the spatial dependence of the shunting coefficient by a phenomenological parametrization. Our analytical and numerical results show that this dendritic-integration-rule-based I&F (DIF) model is able to capture many experimental observations and it also yields predictions that can be used to verify the validity of the DIF model experimentally. In addition, the DIF model incorporates the dendritic integration effects dynamically and is applicable to more general situations than those in experiments in which excitatory and inhibitory inputs occur simultaneously in time. Finally, we generalize the DIF neuronal model to incorporate multiple inputs and obtain a similar dendritic integration rule that is consistent with the results obtained by using a realistic neuronal model with multiple compartments. This generalized DIF model can potentially be used to study network dynamics that may involve effects arising from dendritic integrations.
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