摘自维基百科
Computational neuroscience is the study of brain function in terms of the information processing properties of the structures that make up the nervous system.[1] It is an interdisciplinary science that links the diverse fields of neuroscience, cognitive science and psychology with electrical engineering, computer science, mathematics and physics.
计算神经科学是研究大脑神经系统中信息处理架构属性的一门学科。这是一门涉及神经学，认知科学，心理学与电气工程，计算机科学，数学以及物理学的跨学科科学。
Computational neuroscience is distinct from psychological connectionism and from learning theories of disciplines such as machine learning, neural networks and computational learning theory in that it emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein, and chemical coupling to network oscillations, columnar and topographic architecture, and learning and memory. These computational models are used to frame hypotheses that can be directly tested by current or future biological and/or psychological experiments.
计算神经科学是与心理联结学习理论以及学习训练理论，如机器学习，神经网络和计算学习理论不同，它强调功能描述和生物现实神经元（神经系统）和他们的生理和动力学。这些模型捕捉多时空尺度的生物系统的本质特征，从膜电流，蛋白质和化学耦合网络振荡，柱状和地形结构，学习和记忆。使用这些计算模式帧的假设，可以直接测试当前或未来的生物和/或心理实验
Computational neuroscience is the study of brain function in terms of the information processing properties of the structures that make up the nervous system.[1] It is an interdisciplinary science that links the diverse fields of neuroscience, cognitive science and psychology with electrical engineering, computer science, mathematics and physics.
计算神经科学是研究大脑神经系统中信息处理架构属性的一门学科。这是一门涉及神经学，认知科学，心理学与电气工程，计算机科学，数学以及物理学的跨学科科学。
Computational neuroscience is distinct from psychological connectionism and from learning theories of disciplines such as machine learning, neural networks and computational learning theory in that it emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein, and chemical coupling to network oscillations, columnar and topographic architecture, and learning and memory. These computational models are used to frame hypotheses that can be directly tested by current or future biological and/or psychological experiments.
计算神经科学是与心理联结学习理论以及学习训练理论，如机器学习，神经网络和计算学习理论不同，它强调功能描述和生物现实神经元（神经系统）和他们的生理和动力学。这些模型捕捉多时空尺度的生物系统的本质特征，从膜电流，蛋白质和化学耦合网络振荡，柱状和地形结构，学习和记忆。使用这些计算模式帧的假设，可以直接测试当前或未来的生物和/或心理实验
