Learning the invariance properties of complex cells from their responses to natural stimuli

Einhäuser, Wolfgang; Kayser, Christoph; König, Peter; Körding, Konrad P.

doi:10.1046/j.0953-816x.2001.01885.x

Cited by 91Visual perception and processing mechanismsNeural dynamics and brain functionNeural Networks and Applications

Learning the invariance properties of complex cells from their responses to natural stimuli

Einhäuser, Wolfgang, Kayser, Christoph, König, Peter, Körding, Konrad P.·2002·European Journal of Neuroscience

DOI 10.1046/j.0953-816x.2001.01885.x

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Abstract

Neurons in primary visual cortex are typically classified as either simple or complex. Whereas simple cells respond strongly to grating and bar stimuli displayed at a certain phase and visual field location, complex cell responses are insensitive to small translations of the stimulus within the receptive field [Hubel & Wiesel (1962) J. Physiol. (Lond.), 160, 106-154; Kjaer et al. (1997) J. Neurophysiol., 78, 3187-3197]. This constancy in the response to variations of the stimuli is commonly called invariance. Hubel and Wiesel's classical model of the primary visual cortex proposes a connectivity scheme which successfully describes simple and complex cell response properties. However, the question as to how this connectivity arises during normal development is left open. Based on their work and inspired by recent physiological findings we suggest a network model capable of learning from natural stimuli and developing receptive field properties which match those of cortical simple and complex cells. Stimuli are drawn from videos obtained by a camera mounted to a cat's head, so they should approximate the natural input to the cat's visual system. The network uses a competitive scheme to learn simple and complex cell response properties. Employing delayed signals to learn connections between simple and complex cells enables the model to utilize temporal properties of the input. We show that the temporal structure of the input gives rise to the emergence and refinement of complex cell receptive fields, whereas removing temporal continuity prevents this processes. This model lends a physiologically based explanation of the development of complex cell invariance response properties.

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“IT neurons have vast receptive fields and respond selectively to certain objects or parts of objects across large changes in size, position and illumination [39,40].”
From The visual word form area: expertise for reading in the fusiform gyrus (2003) · cites this paper as [40]
IT cortex is well known for its role in invariant object recognition.
“For example, inferotemporal (IT) cortex is well known for its role in invariant object recognition.”
From The visual word form area: expertise for reading in the fusiform gyrus (2003) · cites this paper as [40]

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Authors: Einhäuser, W., Kayser, C., König, P., & Körding, K. P.
Affiliations: Max Planck Society (DE)
ORCID: Kayser, Christoph
Venue: European Journal of Neuroscience
Publisher: Wiley
Published: 2002-02
Volume: 15
Issue: 3
Pages: 475-486
ISSN: 0953-816X (ISSN-L)
1460-9568
DOI: 10.1046/j.0953-816x.2001.01885.x
OpenAlex: W2134309959
Cited by: 91
References: 57
Citations by year: 2026: 1 · 2025: 4 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 3 · 2020: 2 · 2019: 3 · 2016: 4 · 2015: 3 · 2014: 6 · 2013: 11 · 2012: 4
FWCI: 3.67
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 38 d)
Locations: European Journal of Neuroscience (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)
Funders: Boehringer Ingelheim Fonds
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords: complex cells, simple cells, primary visual cortex, invariance, receptive field development, naturalistic stimuli, competitive learning, cat visual system, network models
MeSH terms: Animals, Cats, Evoked Potentials, Visual, Learning, Models, Neurological, Nerve Net, Synaptic Transmission, Neurons, Pattern Recognition, Visual, Photic Stimulation, Video Recording, Visual Cortex, Visual Fields, Neural Networks, Computer

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Learning the invariance properties of complex cells from their responses to natural stimuli

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