May Christopher J
Life Sciences Department, Carroll University, Waukesha, WI 53186.
J Undergrad Neurosci Educ. 2010 Spring;8(2):A116-21. Epub 2010 Mar 15.
David Marr famously proposed three levels of analysis (implementational, algorithmic, and computational) for understanding information processing systems such as the brain. While two of these levels are commonly taught in neuroscience courses (the implementational level through neurophysiology and the computational level through systems/cognitive neuroscience), the algorithmic level is typically neglected. This leaves an explanatory gap in students' understanding of how, for example, the flow of sodium ions enables cognition. Neural networks bridge these two levels by demonstrating how collections of interacting neuron-like units can give rise to more overtly cognitive phenomena. The demonstrations in this paper are intended to facilitate instructors' introduction and exploration of how neurons "process information."
大卫·马尔著名地提出了三个分析层次(实现层次、算法层次和计算层次),用于理解诸如大脑这样的信息处理系统。虽然神经科学课程中通常会讲授其中两个层次(通过神经生理学讲授实现层次,通过系统/认知神经科学讲授计算层次),但算法层次通常被忽视。这在学生对例如钠离子流动如何实现认知的理解上留下了一个解释空白。神经网络通过展示相互作用的类神经元单元集合如何产生更明显的认知现象,弥合了这两个层次之间的差距。本文中的演示旨在促进教师引入和探讨神经元如何“处理信息”。