Adaptive signal processing
From Chemotaxis to cooperativity: abstract exercises in neuronal learning strategies
The computing neuron
Sigma-Pi learning: on radial basis functions and cortical associative learning
Advances in neural information processing systems 2
Discrimination thresholds for channel-coded systems
Biological Cybernetics
Learning of visual modules from examples: a framework for understanding adaptive visual performance
CVGIP: Image Understanding - Special issue on purposive, qualitative, active vision
Perceptual Learning and Abstraction in Machine Learning
ICCI '03 Proceedings of the 2nd IEEE International Conference on Cognitive Informatics
Neural network models of perceptual learning of angle discrimination
Neural Computation
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Performance of human subjects in a wide variety of early visual processing tasks improves with practice. HyperBF networks (Poggio and Girosi 1990) constitute a mathematically well-founded framework for understanding such improvement in performance, or perceptual learning, in the class of tasks known as visual hyperacuity. The present article concentrates on two issues raised by the recent psychophysical and computational findings reported in Poggio et al. (1992b) and Fahle and Edelman (1992). First, we develop a biologically plausible extension of the HyperBF model that takes into account basic features of the functional architecture of early vision. Second, we explore various learning modes that can coexist within the HyperBF framework and focus on two unsupervised learning rules that may be involved in hyperacuity learning. Finally, we report results of psychophysical experiments that are consistent with the hypothesis that activity-dependent presynaptic amplification may be involved in perceptual learning in hyperacuity.