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• W3146232039 abstract "We show that spatial continuity can enable a network to learn translation invariant representations of objects by self-organization in a hierarchical model of cortical processing in the ventral visual system. During 'continuous transformation learning', the active synapses from each overlapping transform are associatively modi- fied onto the set of postsynaptic neurons. Because other transforms of the same object overlap with previously learned exemplars, a common set of postsynaptic neurons is activated by the new transforms, and learning of the new active inputs onto the same postsynaptic neurons is facili- tated. We show that the transforms must be close for this to occur; that the temporal order of presentation of each transformed image during training is not crucial for learning to occur; that relatively large numbers of trans- forms can be learned; and that such continuous transfor- mation learning can be usefully combined with temporal trace training." @default.
• W3146232039 created "2021-04-13" @default.
• W3146232039 creator A5041263633 @default.
• W3146232039 date "2010-01-01" @default.
• W3146232039 modified "2023-09-27" @default.
• W3146232039 title "Continuous transformation learning of translation invariant representations" @default.
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• W3146232039 hasPublicationYear "2010" @default.
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