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Teaching Basic Principles of Neuroscience with Computer Simulations

机译:用计算机模拟教学神经科学的基本原理

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摘要

It is generally believed that students learn best through activities that require their direct participation. By using simulations as a tool for learning neuroscience, students are directly engaged in the activity and obtain immediate feedback and reinforcement. This paper describes a series of biophysical models and computer simulations that can be used by educators and students to explore a variety of basic principles in neuroscience. The paper also suggests ‘virtual laboratory’ exercises that students may conduct to further examine biophysical processes underlying neural function. First, the Hodgkin and Huxley (HH) model is presented. The HH model is used to illustrate the action potential, threshold phenomena, and nonlinear dynamical properties of neurons (e.g., oscillations, postinhibitory rebound excitation). Second, the Morris-Lecar (ML) model is presented. The ML model is used to develop a model of a bursting neuron and to illustrate modulation of neuronal activity by intracellular ions. Lastly, principles of synaptic transmission are presented in small neural networks, which illustrate oscillatory behavior, excitatory and inhibitory postsynaptic potentials, and temporal summation.
机译:一般认为,学生通过需要他们直接参与的活动才能学习得最好。通过使用模拟作为学习神经科学的工具,学生可以直接参与活动并获得即时反馈和强化。本文介绍了一系列的生物物理模型和计算机模拟,教育者和学生可以使用它们来探索神经科学的各种基本原理。该论文还建议学生进行“虚拟实验室”练习,以进一步检查神经功能背后的生物物理过程。首先,提出了霍奇金和赫x黎(HH)模型。 HH模型用于说明神经元的动作电位,阈值现象和非线性动力学特性(例如,振荡,抑制后反弹激励)。其次,提出了莫里斯-雷卡尔(ML)模型。 ML模型用于建立神经元爆发的模型,并说明细胞内离子对神经元活性的调节。最后,在小型神经网络中介绍了突触传递的原理,这些原理说明了振荡行为,兴奋性和抑制性突触后电位以及时间累加。

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