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We show in a 4-layer competitive neuronal network that continuous transformation learning, which uses spatial correlations and a purely associative (Hebbian) synaptic modification rule, can build view invariant representations of complex 3D objects. This occurs even when views of the different objects are interleaved, a condition where temporal trace learning fails. Human psychophysical experiments showed that view invariant object learning can occur when spatial but not temporal continuity applies because of interleaving of stimuli, although sequential presentation, which produces temporal continuity, can facilitate learning. Thus continuous transformation learning is an important principle that may contribute to view invariant object recognition.

Original publication




Journal article


Vision Res

Publication Date





3994 - 4006


Computer Simulation, Form Perception, Humans, Learning, Models, Neurological, Neural Networks (Computer), Photic Stimulation, Psychophysics, Retention (Psychology), Time, Visual Cortex, Visual Pathways