An Extension of epsilon-Insensitive Hebbian Learning to Form a Non-Interfering Basis
We review an extension of Hebbian learning which has been called e-Insensitive Hebbian Learning (Fyfe and MacDonald, 2001) and derive lateral connections from a probability density function. We use these lateral connections to move outputs towards the mode of the pdf and use the resulting outputs to train the feedforward connections. We show that the resulting network is able to identify a single orientation of bars from a mixture of horizontal and vertical bars.