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Volumn 15, Issue 9, 2003, Pages 2147-2177

Slow feature analysis: A theoretical analysis of optimal free responses

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; COMPUTER SIMULATION; CONDITIONING; CYTOLOGY; PATTERN RECOGNITION; PHYSIOLOGY; RETINA; SENSORY NERVE CELL; VISUAL SYSTEM;

EID: 0041324871     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976603322297331     Document Type: Article
Times cited : (71)

References (11)
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    • Becker, S.1    Hinton, G.E.2
  • 2
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    • Applying slow feature analysis to image sequences yields a rich repertoire of complex cell properties
    • J. R. Dorronsoro (Ed.). Berlin: Springer-Verlag
    • Berkes, P., & Wiskott, L. (2002). Applying slow feature analysis to image sequences yields a rich repertoire of complex cell properties. In J. R. Dorronsoro (Ed.), Proc. Intl. Conf. on Artificial Neural Networks - ICANN'02 (pp. 81-86). Berlin: Springer-Verlag.
    • (2002) Proc. Intl. Conf. on Artificial Neural Networks - ICANN'02 , pp. 81-86
    • Berkes, P.1    Wiskott, L.2
  • 3
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    • Learning invariance from transformation sequences
    • Földiák, P. (1991). Learning invariance from transformation sequences. Neural Computation, 3(2), 194-200.
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    • Földiák, P.1
  • 4
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    • Connectionist learning procedures
    • Hinton, G. E. (1989). Connectionist learning procedures. Artificial Intelligence, 40(1-3), 185-234.
    • (1989) Artificial Intelligence , vol.40 , Issue.1-3 , pp. 185-234
    • Hinton, G.E.1
  • 5
    • 84958967351 scopus 로고    scopus 로고
    • Extracting slow subspaces from natural videos leads to complex cells
    • G. Dorffner, H. Bischoff, & K. Hornik (Eds.). Berlin: Springer-Verlag
    • Kayser, C., Einhäuser, W., Dümmer, O., König, P., & Körding, K. (2001). Extracting slow subspaces from natural videos leads to complex cells. In G. Dorffner, H. Bischoff, & K. Hornik (Eds.), Proc. Intl. Conf. on Artificial Neural Networks - ICANN'01 (pp. 1075-1080). Berlin: Springer-Verlag.
    • (2001) Proc. Intl. Conf. on Artificial Neural Networks - ICANN'01 , pp. 1075-1080
    • Kayser, C.1    Einhäuser, W.2    Dümmer, O.3    König, P.4    Körding, K.5
  • 6
    • 0039765605 scopus 로고
    • Removing time variation with the anti-Hebbian differential synapse
    • Mitchison, G. (1991). Removing time variation with the anti-Hebbian differential synapse. Neural Computation, 3(3), 312-320.
    • (1991) Neural Computation , vol.3 , Issue.3 , pp. 312-320
    • Mitchison, G.1
  • 7
    • 0000805239 scopus 로고
    • Object recognition and sensitive periods: A computation analysis of visual imprinting
    • O'Reilly, R. C., & Johnson, M. H. (1994). Object recognition and sensitive periods: A computation analysis of visual imprinting. Neural Computation, 6(3), 357-389.
    • (1994) Neural Computation , vol.6 , Issue.3 , pp. 357-389
    • O'Reilly, R.C.1    Johnson, M.H.2
  • 8
    • 0032484858 scopus 로고    scopus 로고
    • Energy function for learning invariance in multilayer perceptron
    • Peng, H. C., Sha, L. E. Gan, Q., & Wei, Y. (1998). Energy function for learning invariance in multilayer perceptron. Electronics Letters, 34(3), 292-294.
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    • Peng, H.C.1    Sha, L.E.2    Gan, Q.3    Wei, Y.4
  • 9
    • 0040357675 scopus 로고
    • A learning rule for extracting spatiotemporal invariances
    • Stone, J. V., & Bray, A. J. (1995). A learning rule for extracting spatiotemporal invariances. Network: Computation in Neural Systems, 6(3), 429-436.
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    • Stone, J.V.1    Bray, A.J.2
  • 10
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    • Invariant face and object recognition in the visual system
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  • 11
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    • Slow feature analysis: Unsupervised learning of invariances
    • Wiskott, L. & Sejnowski, T. J. (2002). Slow feature analysis: Unsupervised learning of invariances. Neural Computation, 14(4), 715-770.
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    • Wiskott, L.1    Sejnowski, T.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.