TY - GEN
T1 - Learning continuous attractors in recurrent networks
AU - Seung, H. Sebastian
PY - 1998
Y1 - 1998
N2 - One approach to invariant object recognition employs a recurrent neural network as an associative memory. In the standard depiction of the network's state space, memories of objects are stored as attractive fixed points of the dynamics. I argue for a modification of this picture: if an object has a continuous family of instantiations, it should be represented by a continuous attractor. This idea is illustrated with a network that learns to complete patterns. To perform the task of filling in missing information, the network develops a continuous attractor that models the manifold from which the patterns are drawn. Prom a statistical viewpoint, the pattern completion task allows a formulation of unsupervised learning in terms of regression rather than density estimation.
AB - One approach to invariant object recognition employs a recurrent neural network as an associative memory. In the standard depiction of the network's state space, memories of objects are stored as attractive fixed points of the dynamics. I argue for a modification of this picture: if an object has a continuous family of instantiations, it should be represented by a continuous attractor. This idea is illustrated with a network that learns to complete patterns. To perform the task of filling in missing information, the network develops a continuous attractor that models the manifold from which the patterns are drawn. Prom a statistical viewpoint, the pattern completion task allows a formulation of unsupervised learning in terms of regression rather than density estimation.
UR - http://www.scopus.com/inward/record.url?scp=0000938157&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0000938157&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0000938157
SN - 0262100762
SN - 9780262100762
T3 - Advances in Neural Information Processing Systems
SP - 654
EP - 660
BT - Advances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997
PB - Neural information processing systems foundation
T2 - 11th Annual Conference on Neural Information Processing Systems, NIPS 1997
Y2 - 1 December 1997 through 6 December 1997
ER -