Topographic independent component analysis as a model of V1 organization and receptive fields

Neurocomputing

Volumn 38-40, Issue , 2001, Pages 1307-1315

  Hyvärinen, Aapo ^a   Hoyer, Patrik O ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Complex cells; Independent component analysis; Natural image statistics; Topography; V1

Indexed keywords

CELLS; CORRELATION METHODS; IMAGE ANALYSIS; INDEPENDENT COMPONENT ANALYSIS;

TOPOGRAPHIC ORGANIZATIONS;

NEUROLOGY;

ANALYTIC METHOD; ARTICLE; BRAIN CELL; CONTROLLED STUDY; CORRELATION ANALYSIS; IMAGE ANALYSIS; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PRIORITY JOURNAL; RECEPTIVE FIELD; STATISTICAL ANALYSIS; TOPOGRAPHY; VISUAL CORTEX;

EID: 17144449542     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(01)00490-8     Document Type: Article

References (15)

1. A.J. Bell, T.J. Sejnowski, The 'independent components' of natural scenes are edge filters, Vision Res. 37 (1997) 3327-3338.
2. J.-F. Cardoso, Multidimensional independent component analysis, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '98), Seattle, WA, 1998.
3. P. Comon, Independent component analysis - a new concept?, Signal Processing 36 (1994) 287-314.
4. G.C. DeAngelis, G.M. Ghose, I. Ohzawa, R.D. Freeman, Functional micro-organization of primary visual cortex: receptive field analysis of nearby neurons, J. Neurosci. 19 (10) (1999) 4046-4064.
5. G.J. Goodhill, T.J. Sejnowski, A unifying objective function for topographic mappings, Neural Comput. 9 (6) (1997) 1291-1303.
6. A. Hyvärinen, Fast and robust fixed-point algorithms for independent component analysis, IEEE Trans. Neural Networks 10 (3) (1999) 626-634.
7. A. Hyvärinen, P.O. Hoyer, Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces, Neural Computat. 12 (7) (2000) 1705-1720.
8. A. Hyvärinen, P.O. Hoyer, M. Inki, Topographic independent component analysis, Neural Comput. 2001, in press.
9. A. Hyvärinen and E. Oja, A fast fixed-point algorithm for independent component analysis, Neural Comput. 9 (7) (1997) 1483-1492.
10. T. Kohonen, Self-Organizing Maps, Springer, Berlin, 1995.
11. J.K. Lin, Factorizing multivariate function classes, in: Advances in Neural Information Processing Systems, Vol. 10, MIT Press, Cambridge, MA, 1998, pp. 563-569.
12. B.A. Olshausen, D.J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature 381 (1996) 607-609.
13. E.P. Simoncelli, O. Schwartz, Modeling surround suppression in V1 neurons with a statistically-derived normalization model, in: Advances in Neural Information Processing Systems 11, MIT Press, Cambridge, MA, 1999, pp. 153-159.
14. N.V. Swindale, The development of topography in the visual cortex: a review of models, Network 7 (2) (1996) 161-247.
15. C. Zetzsche, G. Krieger, Nonlinear neurons and high-order statistics: new approaches to human vision and electronic image processing, in: B. Rogowitz, T.V. Pappas (Eds.), Human Vision and Electronic Imaging IV (Proc. SPIE Vol. 3644), SPIE, Bellingham, WA, USA, 1999, pp. 2-33.