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A cerebellum-like learning machine

University of British Columbia

This thesis derives a new learning system which is presented as both an improved cerebellar model and as a general purpose learning machine. It is based on a summary of recent publications concerning the operating characteristics and structure of the mammalian cerebellum and on standard interpolating and surface fitting techniques for functions of one and several variables. The system approximates functions as weighted sums of continuous basis functions. Learning, which takes place in an iterative manner, is accomplished by presenting the system with arbitrary training points (function input variables) and associated function values. The system is shown to be capable of minimizing the estimation error in the mean-square-error sense. The system is also shown to minimize the expectation of the interference, which results from learning at a single point, on all other points in the input space. In this sense, the system maximizes the rate at which arbitrary functions are learned.

Text

2010-03-11

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http://hdl.handle.net/2429/21768

Electrical and Computer Engineering

University of British Columbia

Graduate