Connectionist Robot Motion Planning by Bartlett W. Mel

By Bartlett W. Mel

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2: An instance of the standard connectionist associative learning problem. Input population X projects to output population Y via a modi­ fiable associational pathway; during learning population Y is "clamped" by unconditioned teacher input vector t = / ( x ) as population X moves through its state space. The task of each individual output unit yi is to learn its un­ conditioned teacher activation function y ι — / ( x ) given the training data, which consists of a set of (x, yi) pairs. In short, each of the yi must learn to predict the intensity of its teacher signal solely on the basis of the con­ ditioned input vector from X; the graph above illustrates an unconditioned teacher function plotted over two input variables.

In the following, we sketch the historical progression in the study of synaptic learning rules, both from the biological and computational perspectives, and motivate a set of assumptions needed to strike a delicate balance between learning power and biological plausibility. A Historical Q u a n d a r y In the past two decades, experimental neuroscientists have begun to work out the rules that govern synaptic plasticity during learning in the vertebrate central nervous system [27, 132, 122, 101, 7, 95].

For a detailed review of these issues from a neurobiological perspective, see [138]. Having motivated multiplication as an elemental input operation for neural learning, we now introduce the sigma-pi unit, and relate it to the lookup-table abstraction discussed above. 1) i i ( · ) 3 where k cj = Π v x 3 2 2=1 is the product of k inputs X{ with their weights V{ within cluster j , and Wj is the weight on cluster j as a whole. During learning, the output may be clamped by a teacher input, y = t (fig.

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