Parametric Properties in Chaotic Neural Networks
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2016-08-25
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en
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Abstract
In the framework of reservoir computing a randomly constructed recurrent neural
network (RNN) is used to function as a reservoir containing input history and
its many random transformations. Output units transform whatever is inside
this reservoir into a meaningful output. However, it is di cult to appropriately
construct a random reservoir such that the complete model is able to learn and
perform a certain task. A model's performance depends on two things: the
parameters of the RNN and the task's complexity. The used network parameters
need to be ne-tuned to the task at hand for achieving the best results.
First we propose and investigate several measures of quantifying the di culty
of the chosen to-be-trained pattern. The used patters are visual. Hereafter we
investigate RNN performance after FORCE learning on tasks of di erent complexities,
selected with the apparently most appropriate of these measures, and
we investigate how an RNN's parameters determine its eventual capability to
learn. Under investigation are reservoir size, connectivity and weight scaling.
All ndings were done through simulation studies, with additional theoretical
explanations and references to relevant literature.
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Faculteit der Sociale Wetenschappen