神经模型中的模糊微分包含

S. Tafazoli, M. Menhaj
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引用次数: 0

摘要

动力系统理论帮助大脑科学家更好地应对大脑的复杂性。在本文中,我们提出了一种新的方法来包含描述脑功能的动态系统中的不确定性,如单个神经元或耦合神经元。由一组模糊微分包含(FDI)表示的模糊动力系统是对各种不确定系统进行建模和仿真的一种非常方便的工具。我们使用模糊微分包含来模拟几种类型神经元的神经反应。我们表明,我们的结果与真实的实验数据非常相似,显示了神经反应的可变性。此外,我们已经表明,与随机微分方程(SDEs)的神经系统建模不确定性相比,FDI具有优势。
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Fuzzy differential inclusion in neural modeling
Dynamical systems theory has helped brain scientists to cope better with brain complexity. In this paper, we proposed a novel approach to include uncertainty in dynamical system describing brain function such as one neuron or coupled neurons. Fuzzy dynamical systems represented by a set of fuzzy differential inclusions (FDI) are very convenient tools for modeling and simulation of various uncertain systems. We used fuzzy differential inclusion in modeling neural responses in several types of neurons. We showed that our results are very similar to real experimental data showing variability in neural responses. Further, we have shown that FDI has advantage in comparison with modeling uncertainty in neural systems with Stochastic Differential Equations (SDEs).
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