INVESTIGADORES
MATO German
artículos
Título:
Neural network models of perceptual learning in angle discrimination
Autor/es:
G. MATO AND H. SOMPOLINSKY
Revista:
NEURAL COMPUTATION
Editorial:
MIT Press
Referencias:
Lugar: Cambridge; Año: 1996 p. 270 - 299
ISSN:
0899-7667
Resumen:
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 θ and δθ. Perceptual transfer is studied by measuring the performance of the network on stimuli with θ′ ≠ θ. The two-layer perceptron shows a partial transfer for angles that are within a distance a from θ, where a is the angular width of the input tuning curves. The change in performance due to learning is positive for angles close to θ, but for |θ − θ′| ≈ a it is negative, i.e., its performance after training is worse than before. In contrast, negative transfer
can be avoided in the gating network by limiting the effects of
learning to hidden units that are optimized for angles that are close
to the trained angle.