Contrast normalization contributes to a biologically-plausible model of receptive-field development in primary visual cortex (V1)
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Neuronal populations in the primary visual cortex (V1) of mammals exhibit contrast normalization. Neurons that respond strongly to simple visual stimuli - such as sinusoidal gratings - respond less well to the same stimuli when they are presented as part of a more complex stimulus which also excites other, neighboring neurons. This phenomenon is generally attributed to generalized patterns of inhibitory connections between nearby V1 neurons. The Bienenstock, Cooper and Munro (BCM) rule is a neural network learning rule that, when trained on natural images, produces model neurons which, individually, have many tuning properties in common with real V1 neurons. However, when viewed as a population, a BCM network is very different from V1 - each member of the BCM population tends to respond to the same dominant features of visual input, producing an incomplete, highly redundant code for visual information. Here, we demonstrate that, by adding contrast normalization into the BCM rule, we arrive at a neurally-plausible Hebbian learning rule that can learn an efficient sparse, overcomplete representation that is a better model for stimulus selectivity in V1. This suggests that one role of contrast normalization in V1 is to guide the neonatal development of receptive fields, so that neurons respond to different features of visual input.
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Gain regulation has been suggested to enable networks of neurons to produce distributed representations of stimulus features.
“Gain regulation has been suggested to enable networks of neurons to produce distributed representations of stimulus features223, and to allow downstream decoders to be optimized for diverse stimuli by separating those features224.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [223]
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Mechanisms underlying gain modulation in the cortex
Ferguson, K. A., & Cardin, J. A. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0253-y · Cited by 327 · see citing works
- Authors
- Willmore, B. D. B., Bulstrode, H., & Tolhurst, D. J.
- Affiliations
- University of Oxford (GB)
- University of Cambridge (GB)
- Corresponding author
- Willmore, Ben D.B.
- Venue
- Vision Research
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 54
- Pages
- 49-60
- ISSN
- 0042-6989 (ISSN-L)
- PMC
- PMC3334822
- OpenAlex
- W2161725119
- Cited by
- 10
- References
- 61
- Citations by year
- 2022: 1 · 2021: 1 · 2020: 2 · 2017: 1 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 1 · 2012: 1
- FWCI
- 0.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Vision Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (WT076508AIA)
- 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
- contrast normalization, receptive field development, primary visual cortex, BCM rule, Hebbian learning, stimulus selectivity, inhibitory interneurons, neural network model, visual development
- MeSH terms
- Humans, Models, Neurological, Retina, Visual Cortex, Visual Fields, Visual Perception, Contrast Sensitivity
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