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Properties of artificial neurons that report lightness based on accumulated experience with luminance.

Morgenstern Y, Rukmini DV, Monson BB, Purves D - Front Comput Neurosci (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School Singapore, Singapore.

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.

No MeSH data available.


Network responses to stimuli before and after optimization using a genetic algorithm. (A) Example of a stimulus drawn at random from the 3500 stimuli presented to each evolving network during its lifetime. (B) Evolved network responses (blue) determined by accordance with the conditional CDF of the luminance intensities at the central stimulus grid square, given the luminance intensities of the context pattern in (A) (red). The gray triangles show network responses before evolution and blue squares the responses after 2000 generations; the abscissa is the scaled luminance intensity of the central stimulus grid square, and the ordinate is its cumulative probability. As expected from the nature of the paradigm, the evolved responses approximate the cumulative probability of the central grid square given the context grid squares in natural images. The red circle embedded in gray represents the scaled luminance of the central grid square in (A).
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Figure 3: Network responses to stimuli before and after optimization using a genetic algorithm. (A) Example of a stimulus drawn at random from the 3500 stimuli presented to each evolving network during its lifetime. (B) Evolved network responses (blue) determined by accordance with the conditional CDF of the luminance intensities at the central stimulus grid square, given the luminance intensities of the context pattern in (A) (red). The gray triangles show network responses before evolution and blue squares the responses after 2000 generations; the abscissa is the scaled luminance intensity of the central stimulus grid square, and the ordinate is its cumulative probability. As expected from the nature of the paradigm, the evolved responses approximate the cumulative probability of the central grid square given the context grid squares in natural images. The red circle embedded in gray represents the scaled luminance of the central grid square in (A).

Mentions: The best networks in eight simulations showed output response values that, as expected, approximated the conditional CDF of the central target in a given context (Figure 3). These responses, ranked as percentiles for a given target luminance value, indicate how often central luminance values in that context occurred more frequently than the value of interest and how often less frequently in the cumulative experience of the network's ancestors.


Properties of artificial neurons that report lightness based on accumulated experience with luminance.

Morgenstern Y, Rukmini DV, Monson BB, Purves D - Front Comput Neurosci (2014)

Network responses to stimuli before and after optimization using a genetic algorithm. (A) Example of a stimulus drawn at random from the 3500 stimuli presented to each evolving network during its lifetime. (B) Evolved network responses (blue) determined by accordance with the conditional CDF of the luminance intensities at the central stimulus grid square, given the luminance intensities of the context pattern in (A) (red). The gray triangles show network responses before evolution and blue squares the responses after 2000 generations; the abscissa is the scaled luminance intensity of the central stimulus grid square, and the ordinate is its cumulative probability. As expected from the nature of the paradigm, the evolved responses approximate the cumulative probability of the central grid square given the context grid squares in natural images. The red circle embedded in gray represents the scaled luminance of the central grid square in (A).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4217489&req=5

Figure 3: Network responses to stimuli before and after optimization using a genetic algorithm. (A) Example of a stimulus drawn at random from the 3500 stimuli presented to each evolving network during its lifetime. (B) Evolved network responses (blue) determined by accordance with the conditional CDF of the luminance intensities at the central stimulus grid square, given the luminance intensities of the context pattern in (A) (red). The gray triangles show network responses before evolution and blue squares the responses after 2000 generations; the abscissa is the scaled luminance intensity of the central stimulus grid square, and the ordinate is its cumulative probability. As expected from the nature of the paradigm, the evolved responses approximate the cumulative probability of the central grid square given the context grid squares in natural images. The red circle embedded in gray represents the scaled luminance of the central grid square in (A).
Mentions: The best networks in eight simulations showed output response values that, as expected, approximated the conditional CDF of the central target in a given context (Figure 3). These responses, ranked as percentiles for a given target luminance value, indicate how often central luminance values in that context occurred more frequently than the value of interest and how often less frequently in the cumulative experience of the network's ancestors.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School Singapore, Singapore.

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.

No MeSH data available.