网络上突触电位的平均变换
Averaging Transformations of Synaptic Potentials on Networks
摘要：
微观信息到宏观层面的转换问题是计算神经科学中一个有趣的挑战，但也具有普遍的数学重要性。在这里，现象学的数学模型介绍，模拟内部信息处理的大脑隔间。突触电位在少量实际耦合的神经元上积分以获得宏观量。纹状体复合体是大脑中调节运动活动的基底神经节回路的重要组成部分，已被研究作为验证模型的一个例子。
英文摘要：
The problem of the transformation of microscopic information to the macroscopic level is an intriguing challenge in computational neuroscience, but also of general mathematical importance. Here, a phenomenological mathematical model is introduced that simulates the internal information processing of brain compartments. Synaptic potentials are integrated over small number of realistically coupled neurons to obtain macroscopic quantities. The striatal complex, an important part of the basal ganglia circuit in the brain for regulating motor activity, has been investigated as an example for the validation of the model.
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