Jim Anderson does research in the areas of cognition and cognitive development; theoretical and computational models; computational models of learning, memory and neural development; theory of computation; and artificial intelligence and robotics.
I study how brains and computers are different in the way they compute. These differences arise in large part because of basic physical differences in their hardware. I research brain-like computation using "neural networks," simplifications of the complexities of real brains. Besides offering clues to brain functions as models, neural networks perform some practical applications. For example, I applied a simple neural network model, originally derived to explain how humans form concepts based on experience, to the problem of "understanding" a complex radar environment. More recently, I have been working on a set of models for the intermediate-level organization of the nervous system. Scientists know a great deal about individual neurons. We also know a good deal about behavior and the functioning of very large groups of neurons. But we know almost nothing about how groups of a hundred or a thousand or even a hundred thousand neurons cooperate to compute, or perceive, or think, or behave. One question I am now trying to answer concerns scaling. Under what conditions can similar computational functions be performed by networks of greatly differing size?
JAMES ANDERSON, SB Physics, PhD Physiology
On The Web:
A Brain Like Computer for Cognitive Applications: The Ersatz Brain Project
Seed Fund Awards go to six multidisciplinary research projects
Ersatz Brain Home Page
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