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

1983

@article{FMI1983,
	vgclass =	{refpap},
	vgproject =	{nn},
	author =	{Kunihiko Fukushima and Sei Miyake and Takayuki Ito},
	title =	{{N}eocognitron: A Neural Network Model for a Mechanism of
	Visual Pattern Recognition},
	journal =	{IEEE Transactions on Systems, Man and Cybernetics},
	volume =	{13},
	number =	{5},
	pages =	{826--834},
	year =	{1983},
	abstract =	{A neural network model, called a ``neocognitron'', for a
	mechanism of visual pattern recognition was proposed earlier, and the
	result of computer simulation for a small-scale network was shown. A
	neocognitron with a larger-scale network is now simulated on a digital
	computer and is shown to have a great capacity for visual pattern
	recognition: The neocognitron is a multilayered network consisting of a
	cascaded connection of many layers of cells. The information of the
	stimulus pattern given to the input layer is processed step by step in
	each stage of the multilayered network. A cell in a deeper layer
	generally has a tendency to respond selectively to a more complicated
	feature of the stimulus patterns and, at the same time, has a larger
	receptive field and is less sensitive to shifts in position of the
	stimulus patterns. Thus each cell of the deepest layer of the network
	responds selectively to a specific stimulus pattern and is not affected
	by the distortion in shape or the shift in position of the pattern. The
	synapses between the cells in the network are modifiable, and the
	neocognitron has a function of learning. A learning-with-a-teacher
	process is used to reinforce these modifiable synapses in the new
	model, instead of the learning-without-a-teacher process which was
	applied to the previous small-scale model.},
}