A competitive network is described which learns to classify objects on the basis of temporal as well as spatial correlations. This is achieved by using a Hebb-Iike learning rule which is dependent upon prior as well as current neural activity. The rule is shown to be capable ofoutperforming a superv
Learning invariant object recognition from temporal correlation in a hierarchical network
✍ Scribed by Lessmann, Markus; Würtz, Rolf P.
- Book ID
- 122122550
- Publisher
- Elsevier Science
- Year
- 2014
- Tongue
- English
- Weight
- 796 KB
- Volume
- 54
- Category
- Article
- ISSN
- 0893-6080
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