Demonstrations of Neural Network Computations Involving Students

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