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首页> 外文期刊>Journal of Computational Neuroscience >Structure-preserving model reduction of passive and quasi-active neurons
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Structure-preserving model reduction of passive and quasi-active neurons

机译：被动和准主动神经元的结构保留模型约简

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摘要

The spatial component of input signals often carries information crucial to a neuron's function, but models mapping synaptic inputs to the transmembrane potential can be computationally expensive. Existing reduced models of the neuron either merge compartments, thereby sacrificing the spatial specificity of inputs, or apply model reduction techniques that sacrifice the underlying electrophysiology of the model. We use Krylov subspace projection methods to construct reduced models of passive and quasi-active neurons that preserve both the spatial specificity of inputs and the electrophysiological interpretation as an RC and RLC circuit, respectively. Each reduced model accurately computes the potential at the spike initiation zone (SIZ) given a much smaller dimension and simulation time, as we show numerically and theoretically. The structure is preserved through the similarity in the circuit representations, for which we provide circuit diagrams and mathematical expressions for the circuit elements. Furthermore, the transformation from the full to the reduced system is straightforward and depends on intrinsic properties of the dendrite. As each reduced model is accurate and has a clear electrophysiological interpretation, the reduced models can be used not only to simulate morphologically accurate neurons but also to examine computations performed in dendrites.
机译：输入信号的空间成分通常携带着对神经元功能至关重要的信息，但是将突触输入映射到跨膜电位的模型在计算上可能会很昂贵。现有的神经元简化模型要么合并隔室，从而牺牲输入的空间特异性，要么应用牺牲模型潜在电生理的模型简化技术。我们使用Krylov子空间投影方法来构造被动和准主动神经元的简化模型，这些模型分别保留输入的空间特异性和分别作为RC和RLC电路的电生理学解释。每个缩小的模型在给定较小的尺寸和模拟时间的情况下，都会准确计算出尖峰起始区（SIZ）的电势，如数值和理论上所示。通过电路表示中的相似性来保留结构，为此我们提供了电路图和电路元件的数学表达式。此外，从完整系统到还原系统的转换非常简单，并且取决于枝晶的固有属性。由于每个简化的模型都是准确的并且具有清晰的电生理学解释，因此简化的模型不仅可以用于模拟形态精确的神经元，还可以检查在树枝状结构中执行的计算。

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