Psychology 190(4): "Neural Networks"
Winter quarter 1997
Efforts to understand the brain draw upon multiple scientific
methodologies and disciplines. Neurobiological studies focus on
characterizing various aspects of neural function and their relation
to behavior. Psychological studies attempt to characterize various
aspects of behavior or performance with the hope of revealing
underlying processes or mechanisms. More recently, there has been a
growing effort to draw upon principles from mathematics, physics,
engineering and computer science in order to formulate neural network
models capable of elucidating the link between neurons and behavior.
This course will focus on this latter approach, and will introduce
basic neural network paradigms that have been used as models of
sensory coding, pattern recognition, associative memory, motor
control, learning, and self-organization. Specific topics to be
covered include Adaline/perceptron learning, backpropagation, Hebbian
learning, competitive learning, redundancy reduction, self-organizing
maps, Hopfield networks, probabilistic modeling, Boltzmann machines,
Helmholtz machines, and Bayes nets. The emphasis will be on
understanding the key conceptual ideas behind these models and what
insights they lend into brain and behavior.
Instructor:
Bruno A. Olshausen, 268J
Young Hall, 757-8749
Units:
4
Time:
Monday, 6-9, in room 145 of Young Hall.
Prerequisites:
The course is aimed at advanced undergraduates or graduate students in
psychology and neuroscience, although enrollment is open to students
from all disciplines. Some familiarity with basic concepts of
calculus, linear algebra, or probability theory is desirable.
Texts:
"Parallel Distributed Processing," by J.L. McClelland and
D.A. Rumelhart. MIT Press.
"An Introduction to Neural Networks," by J.A. Anderson. MIT Press