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A Topologically Distributed Encoding to Facilitate Learning

We describe how to solve linear-non-separable problems using simple feed-forward perceptrons without hidden layers. A biologically motivated topologically distributed encoding for input data is used. We point out advantages of neural networks compared to classic mathematical algorithms without loosing performance. The Iris-dataset from Fisher 1] is analyzed as a practical example.