The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Slow feature analysis yields a rich repertoire of complex cell properties
Open accessAbstract
In this study we investigate temporal slowness as a learning principle for receptive fields using slow feature analysis, a new algorithm to determine functions that extract slowly varying signals from the input data. We find a good qualitative and quantitative match between the set of learned functions trained on image sequences and the population of complex cells in the primary visual cortex (V1). The functions show many properties found also experimentally in complex cells, such as direction selectivity, non-orthogonal inhibition, end-inhibition, and side-inhibition. Our results demonstrate that a single unsupervised learning principle can account for such a rich repertoire of receptive field properties.
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Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Jones, J. P., & Palmer, L. A. (1987) · Journal of Neurophysiology · doi:10.1152/jn.1987.58.6.1233 · Cited by 1,658 · cited 2×
“Simple cells respond best to oriented bars at a specific position in the visual field, and are well modeled by a linear Gabor filter (Jones & Palmer, 1987).”
Separation of a mixture of independent signals using time delayed correlations
Molgedey, L., & Schuster, H. G. (1994) · Physical Review Letters · doi:10.1103/physrevlett.72.3634 · Cited by 882
Schiller, P. H., Finlay, B. L., & Volman, S. F. (1976) · Journal of Neurophysiology · doi:10.1152/jn.1976.39.6.1288 · Cited by 458
How are complex cell properties adapted to the statistics of natural stimuli?
Körding, K. P., Kayser, C., Einhäuser, W., & König, P. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00149.2003 · Cited by 121
Cited by
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Building high-level features using large scale unsupervised learning
Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695
Le, Q. V., Zou, W. Y., Yeung, S. Y., & Ng, A. Y. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995496 · Cited by 960
Statistically optimal perception and learning: from behavior to neural representations
Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672
Self-citationDeep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?
Kruger, N., Janssen, P., Kalkan, S., Lappe, M., Leonardis, A., Piater, J., Rodriguez-Sanchez, A. J., & Wiskott, L. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.272 · Cited by 362
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Metadata
- Authors
- Berkes, P., & Wiskott, L.
- Venue
- Journal of Vision
- Published
- 2005-07-01
- Volume
- 5
- Issue
- 6
- Pages
- 9-9
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W2148553367
- Cited by
- 280
- References
- 6
- FWCI
- 5.45
- Type
- journal article
- Language
- en
- Open access
- gold
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