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

1996

@article{Bad1996,
	vgclass =	{refpap},
	author =	{Roland Baddeley},
	title =	{Searching for filters with `interesting' output
	distributions: an uninteresting direction to explore?},
	journal =	{Neural Computation},
	volume =	{7},
	number =	{2},
	pages =	{409--421},
	month =	{May},
	year =	{1996},
	abstract =	{It has been proposed that the receptive fields of neurons
	in V1 are optimised to generate ``sparse'', Kurtotic, or
	``interesting'' output probability distributions (Barlow 1992, Barlow
	1994, Field 1994, Intrator 1991, Intrator 1992). We investigate the
	empirical evidence for this further and argue that filters can produce
	``interesting'' output distributions simply because natural images have
	variable local intensity variance. If the proposed filters have zero
	D.C., then the probability distribution of filter outputs (and hence
	the output Kurtosis) is well predicted simply from these effects of
	variable local variance. This suggests that finding filters with high
	output Kurtosis does not necessarily signal interesting image
	structure.

	It is then argued that finding filters that maximise output Kurtosis
	generates filters that are incompatible with observed physiology. In
	particular the optimal difference-of-Gaussian (DOG) filter should have
	the smallest possible scale, an on-centre off-surround cell should have
	a negative D.C., and that the ratio of centre width to surround width
	should approach unity. This is incompatible with the physiology.
	Further, it is also predicted that oriented filters should always be
	oriented in the vertical direction, and of all the filters tested, the
	filter with the highest output Kurtosis has the lowest signal to noise
	(the filter is simply the difference of two neighbouring pixels).
	Whilst these observations are not incompatible with the brain using a
	sparse representation, it does argue that little significance should be
	placed on finding filters with highly Kurtotic output distributions. It
	is therefore argued that other constraints are required in order to
	understand the development of visual receptive fields.},
}