PMID: 8581884Feb 15, 1996Paper

Neural network models of perceptual learning of angle discrimination

Neural Computation
G Mato, H Sompolinsky

Abstract

We study neural network models of discriminating between stimuli with two similar angles, using the two-alternative forced choice (2AFC) paradigm. Two network architectures are investigated: a two-layer perceptron network and a gating network. In the two-layer network all hidden units contribute to the decision at all angles, while in the other architecture the gating units select, for each stimulus, the appropriate hidden units that will dominate the decision. We find that both architectures can perform the task reasonably well for all angles. Perceptual learning has been modeled by training the networks to perform the task, using unsupervised Hebb learning algorithms with pairs of stimuli at fixed angles theta and delta theta. Perceptual transfer is studied by measuring the performance of the network on stimuli with theta' not equal to theta. The two-layer perceptron shows a partial transfer for angles that are within a distance a from theta, where a is the angular width of the input tuning curves. The change in performance due to learning is positive for angles close to theta, but for magnitude of theta-theta' approximately a it is negative, i.e., its performance after training is worse than before. In contrast, negative tra...Continue Reading

References

May 15, 1992·Science·T PoggioS Edelman
Jan 1, 1987·Vision Research·K Ball, R Sekuler
Nov 15, 1993·Proceedings of the National Academy of Sciences of the United States of America·H S Seung, H Sompolinsky
Jun 1, 1994·Journal of Computational Neuroscience·E Salinas, L F Abbott

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Citations

Oct 16, 2004·Nature·Misha Tsodyks, Charles Gilbert
Feb 24, 1998·Neural Computation·A PougetP E Latham
Aug 25, 2007·PLoS Computational Biology·Roland SchäferWalter Senn
Feb 15, 2011·Vision Research·Grigorios SotiropoulosPeggy Seriès
Apr 4, 2002·Journal of Neurophysiology·Geoffrey M GhoseJohn H R Maunsell
Nov 4, 2000·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·L IttiJ Braun

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