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NECO
2006
103views more  NECO 2006»
13 years 9 months ago
Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning
In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimi...
Jean-Pascal Pfister, Taro Toyoizumi, David Barber,...
TCBB
2008
120views more  TCBB 2008»
13 years 9 months ago
Learning Scoring Schemes for Sequence Alignment from Partial Examples
When aligning biological sequences, the choice of scoring scheme is critical. Even small changes in gap penalties, for example, can yield radically different alignments. A rigorous...
Eagu Kim, John D. Kececioglu
BC
2002
85views more  BC 2002»
13 years 9 months ago
Spike propagation synchronized by temporally asymmetric Hebbian learning
Synchronously spiking neurons have been observed in the cerebral cortex and the hippocampus. In computer models, synchronous spike volleys may be propagated across appropriately co...
Roland E. Suri, Terrence J. Sejnowski
ICCV
1998
IEEE
14 years 11 months ago
Sectored Snakes: Evaluating Learned-Energy Segmentations
We describe how to teach deformable models to maximize image segmentation correctness based on user-specified criteria, and we present a method for evaluating which criteria work ...
Samuel D. Fenster, John R. Kender
ECCV
2008
Springer
14 years 11 months ago
Unsupervised Learning of Skeletons from Motion
Abstract. Humans demonstrate a remarkable ability to parse complicated motion sequences into their constituent structures and motions. We investigate this problem, attempting to le...
David A. Ross, Daniel Tarlow, Richard S. Zemel