@proceedings {4528,
	title = {Supervised learning of image restoration with convolutional networks.},
	volume = {2},
	year = {2007},
	month = {2007 Oct 14},
	pages = {1-8},
	publisher = {IEEE},
	edition = {ICCV},
	author = {Jain, Viren and Murray, Joseph F and Roth, Fabian and Turaga, Srinivas and Zhigulin, Valentin and Briggman, Kevin L and Helmstaedter, Moritz N. and Denk, Winfried and Seung, H Sebastian}
}
@proceedings {4532,
	title = {Representing part-whole relationships in recurrent neural networks.},
	volume = {18},
	year = {2005},
	month = {2005},
	pages = {563-70},
	address = {Whistler, B.C.},
	abstract = {<p>There is little consensus about the computational function of top-down synaptic connections in the visual system. Here we explore the hypothesis that top-down connections, like bottom-up connections, reflect partwhole relationships. We analyze a recurrent network with bidirectional synaptic interactions between a layer of neurons representing parts and a layer of neurons representing wholes. Within each layer, there is lateral inhibition. When the network detects a whole, it can rigorously enforce part-whole relationships by ignoring parts that do not belong. The network can complete the whole by filling in missing parts. The network can refuse to recognize a whole, if the activated parts do not conform to a stored part-whole relationship. Parameter regimes in which these behaviors happen are identified using the theory of permitted and forbidden sets [3, 4]. The network behaviors are illustrated by recreating Rumelhart and McClelland{\textquoteright}s {\textquotedblleft}interactive activation{\textquotedblright} model [7].</p>
},
	author = {Jain, Viren and Zhigulin, Valentin and Seung, Hyunjune Sebastian}
}
