Refereed full papers (journals, book chapters, international conferences)

1996

@article{Bec1996,
	vgclass =	{refpap},
	vgproject =	{nn},
	author =	{Suzanna Becker},
	title =	{Mutual Information Maximization: {M}odels of Cortical
	Self-Organization},
	journal =	{Neural Computation},
	volume =	{7},
	number =	{1},
	pages =	{7--31},
	month =	{February},
	year =	{1996},
	abstract =	{Unsupervised learning procedures based on Hebbian
	principles alone are successful at modelling low level feature
	extraction, but are insufficient for learning to recognize higher order
	features and complex objects. In this paper we explore a class of
	unsupervised learning algorithms called Imax (Becker \& Hinton, 1992)
	that are derived from information-theoretic principles.  The Imax
	algorithms are based on the idea of maximizing the mutual information
	between the outputs of different network modules, and are capable of
	extracting higher order features from data. They are therefore well
	suited to modelling intermediate to high level perceptual processing
	stages. We substantiate this claim with some novel results for two
	signal classification problems, as well as by reviewing some previously
	published results. Finally, Imax and several related approaches are
	evaluated with respect to their computational costs and biological
	plausibility.},
}