title: Properties of artificial neurons that report lightness based on accumulated experience with luminance.
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.
authors.affiliation: Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School Singapore, Singapore.
authors.forename: Yaniv
authors.initials: Y
authors.lastname: Morgenstern
authors.affiliation: Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School Singapore, Singapore.
authors.forename: Dhara V
authors.initials: DV
authors.lastname: Rukmini
authors.affiliation: Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School Singapore, Singapore.
authors.forename: Brian B
authors.initials: BB
authors.lastname: Monson
authors.affiliation: Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School Singapore, Singapore ; Department of Neurobiology, Duke University Medical Center Durham, NC, USA ; Duke Institute for Brain Sciences, Duke University Durham, NC, USA.
authors.forename: Dale
authors.initials: D
authors.lastname: Purves
keywords: efficient coding; empirical ranking; gain control; image statistics; inverse problem; lightness perception; receptive field; vision