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Title: Properties of artificial neurons that report lightness based on accumulated experience with luminance
Authors: Morgenstern, Y 
Rukmini, D.V 
Monson, B.B 
Purves, D 
Keywords: Aldehydes
Gain control
Image coding
Inverse problems
Neural networks
Efficient coding
Empirical ranking
Image statistics
Lightness perception
Receptive fields
artificial neural network
contrast gain control
controlled study
light intensity
luminance gain control
nerve cell network
positive feedback
receptive field
retina image
visual nervous system
visual stimulation
visual system parameters
Issue Date: 2014
Publisher: Frontiers Media S.A.
Citation: Morgenstern, Y, Rukmini, D.V, Monson, B.B, Purves, D (2014). Properties of artificial neurons that report lightness based on accumulated experience with luminance. Frontiers in Computational Neuroscience 8 (November) : 11-Jan. ScholarBank@NUS Repository.
Abstract: The responses of visual neurons in experimental animals have been extensively characterized. To ask whether these responses are consistent with a wholly empirical concept of visual perception, we optimized simple neural networks that responded according to the cumulative frequency of occurrence of local luminance patterns in retinal images. Based on this estimation of accumulated experience, the neuron responses showed classical center-surround receptive fields, luminance gain control and contrast gain control, the key properties of early level visual neurons determined in animal experiments. These results imply that a major purpose of pre-cortical neuronal circuitry is to contend with the inherently uncertain significance of luminance values in natural stimuli. © 2014 Morgenstern, Rukmini, Monson and Purves.
Source Title: Frontiers in Computational Neuroscience
ISSN: 16625188
DOI: 10.3389/fncom.2014.00134
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