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Computational geometry.Algorithms and applications by De Berg M.

By De Berg M.

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4, top left). 4. Illustration of three hypotheses about how learning might change object representations at the level of single neurons. Five example neurons are shown. Their preferred stimulus is displayed in the cell body. When a subject acquires expertise in individuating exemplars from a new class of objects (top right), the different hypotheses make different predictions about which neurons will show how much training-induced increase in selectivity and responsiveness. Neurons that do not change are displayed in light gray, neurons that change strongly in black.

Op de Beeck and Baker (2010) suggested that the informativeness hypothesis might apply equally to high-level object representations as it does to orientationselective neurons. The perfect experiment to test this hypothesis would involve obtaining a tuning curve for each neuron before and after learning and relating the strength of the learning effect to the initial tuning curve prior to learning. However, such an experiment is technically challenging to implement, if not impossible. Actually, even the studies of orientation discrimination did not implement this design because they did not measure tuning curves prior to learning.

J. (2005). “Breaking” position-invariant object recognition. Nat. , 8: 1145–1147. , Wagemans, J. and Vogels, R. (2008). Effects of category learning on the stimulus selectivity of macaque inferior temporal neurons. Learn. , 15: 717–727. De Baene, W. and Vogels, R. (2010). Effects of adaptation on the stimulus selectivity of macaque inferior temporal spiking activity and local field potentials. Cereb. Cortex, 20: 2145–2165. Dehaene, S. and Cohen, L. (2007). Cultural recycling of cortical maps. Neuron, 56: 384– 398.

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