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Demonstrations of Neural Network Computations Involving Students |
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Demonstrations of Neural Network Computations Involving Students Christopher J. May
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.”
Key words: neural network; algorithmic level; demonstration; teaching; physiology; undergraduate
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Editors
Eric Wiertelak Editor-in-Chief Macalester College
Gary Dunbar Senior Editor Central Michigan University
Bruce Johnson Associate Editor Cornell University
William Grisham UCLA
Jean Hardwick Ithaca College
James Kalat North Carolina State
Barbara Lom Davidson College
Kristina Mead Denison University
Michelle Mynlieff Marquette University
Carol Ann Paul Wellesley College
Julio Ramirez Davidson College
Raddy L. Ramos NY College of Osteopathic Medicine/NY Institute of Technology
Amy Jo Stavnezer College of Wooster
Bob Wyttenbach Cornell University
Michael Zigmond University of Pittsburgh
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