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 -