The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
How Are Complex Cell Properties Adapted to the Statistics of Natural Stimuli?
Abstract
Sensory areas should be adapted to the properties of their natural stimuli. What are the underlying rules that match the properties of complex cells in primary visual cortex to their natural stimuli? To address this issue, we sampled movies from a camera carried by a freely moving cat, capturing the dynamics of image motion as the animal explores an outdoor environment. We use these movie sequences as input to simulated neurons. Following the intuition that many meaningful high-level variables, e.g., identities of visible objects, do not change rapidly in natural visual stimuli, we adapt the neurons to exhibit firing rates that are stable over time. We find that simulated neurons, which have optimally stable activity, display many properties that are observed for cortical complex cells. Their response is invariant with respect to stimulus translation and reversal of contrast polarity. Furthermore, spatial frequency selectivity and the aspect ratio of the receptive field quantitatively match the experimentally observed characteristics of complex cells. Hence, the population of complex cells in the primary visual cortex can be described as forming an optimally stable representation of natural stimuli.
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“Imposing continuity within certain models gives rise to aspects of the visual system including complex cells (Körding et al., 2004), specific properties of visual invariance (Isik et al., 2012), and even other representational properties such as the existence of place cells (Wyss et al., 2006; Franzius et al., 2007).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Körding et al., 2004
Citations
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
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
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
Spatiotemporal energy models for the perception of motion
Adelson, E. H., & Bergen, J. R. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.000284 · Cited by 3,442
The “independent components” of natural scenes are edge filters
Bell, A. J., & Sejnowski, T. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00121-1 · Cited by 2,201
Slow Feature Analysis: Unsupervised Learning of Invariances
Wiskott, L., & B, T. J. S. A. (2002) · Neural Computation · doi:10.1162/089976602317318938 · Cited by 1,358
Sparse Coding and Decorrelation in Primary Visual Cortex During Natural Vision
Vinje, W. E., & Gallant, J. L. (2000) · Science · doi:10.1126/science.287.5456.1273 · Cited by 1,286
Independent component filters of natural images compared with simple cells in primary visual cortex
van Hateren, J. H., & van der Schaaf, A. (1998) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rspb.1998.0303 · Cited by 1,106
Learning invariance from transformation sequences
Földiák, P. (1991) · Neural Computation · doi:10.1162/neco.1991.3.2.194 · Cited by 661 · cited 4×
“These studies have successfully applied this criterion to the representations of artificial stimuli such as moving bars to establish that such a mechanism could lead to complex-type neurons (Földiak 1991; Wiskott and Sejnowski 2002).”
BackgroundInfluentialReceptive field organization of complex cells in the cat's striate cortex.
Movshon, J. A., Thompson, I. D., & Tolhurst, D. J. (1978) · The Journal of Physiology · doi:10.1113/jphysiol.1978.sp012489 · Cited by 623
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Research works printed but not linked: 1
- Barlow HB. Possible principles underlying the transformation of sensory messages. In: Sensory Communication, edited by Rosenblith W. Cambridge, MA: MIT Press, 1961, p. 336–360.
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Tovar, M. O., Corchuelo, S., Cortés, D. C. C., & Giraldo, H. H. (2009) · Revista Facultad de Ciencias Básicas · doi:10.18359/rfcb.1948 · Cited by 2
Jia, X., Hong, H., & DiCarlo, J. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.13.900837 · Cited by 2
The neuroscience of transformers
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Monitoring of Operating Condition and Process Dynamics with Slow Feature Analysis
Shang, C. (2018) · Springer theses · doi:10.1007/978-981-10-6677-1_2 · Cited by 1
Complex Cells and the Representation of Local Image-Structure
Hansard, M. E., & Horaud, R. (2010) · Cited by 1
A Probabilistic Approach to the Primary Visual Cortex
Köster, U. (2009) · Työväentutkimus Vuosikirja
Learning Sensorimotor Abstractions
Marcos, C., & Martin, M. P. (2011) · UPCommons institutional repository (Universitat Politècnica de Catalunya)
Statistical modeling of spiking activity in large scale neuronal networks
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Berkes, P., Lengyel, M., & Fiser, J. (2011)
Causal adaptation to visual input dynamics governs the development of complex cells in V1Preprint
Matteucci, G., & Zoccolan, D. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/756668
Scott, D. (2020) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-v8yd-xw60
STDP-based Visual Sensing and Timing-based Learning using Vision SensorsPreprint
Maronga, A., Nelson, A., Ragin, M. J., & Subramaniam, A. (2024) · doi:10.31219/osf.io/4t5xv
Su, Y., Hwang, D., & Wang, B. (2024) · doi:10.31219/osf.io/49qzv
Varetti, S., Goldt, S., & Piasini, E. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014653
Metadata
- Authors
- Körding, K. P., Kayser, C., Einhäuser, W., & König, P.
- Affiliations
- Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom
- Max Planck Society (DE)
- Institute of Neuroinformatics, University of Zürich and ETH Zürich, 8057 Zürich, Switzerland
- ORCID
- Kayser, Christoph
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2004-01
- Volume
- 91
- Issue
- 1
- Pages
- 206-212
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W2124237441
- Cited by
- 121
- References
- 32
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 6 · 2022: 3 · 2021: 5 · 2020: 4 · 2019: 9 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 2 · 2014: 5 · 2013: 6 · 2012: 7
- FWCI
- 4.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- complex cells, primary visual cortex, neural adaptation, spatial frequency selectivity, translation invariance, cat visual system, image motion
- MeSH terms
- Animals, Cats, Computer Simulation, Models, Neurological, Neurons, Orientation, Photic Stimulation, Space Perception, Visual Cortex, Visual Fields, Visual Pathways, Neural Networks, Computer
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