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AIMS Neuroscience, 2018, 5(3): 162-178. doi: 10.3934/Neuroscience.2018.3.162. Review

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New encoding concepts for shape recognition are needed

Laboratory for Neurometric Research, Department of Psychology, University of Southern California, Los Angeles, California, USA

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Models designed to explain how shapes are perceived and stored by the nervous system commonly emphasize encoding of contour features, especially orientation, curvature, and linear extent. A number of experiments from my laboratory provide evidence that contours deliver a multitude of location markers, and shapes can be identified when relatively few of the markers are displayed. The emphasis on filtering for orientation and other contour features has directed attention away from full and effective examination of how the location information is registered and used for summarizing shapes. Neural network (connectionist) models try to deal with location information by modifying linkage among neuronal populations through training trials. Connections that are initially diffuse and not useful in achieving recognition get eliminated or changed in strength, resulting in selective response to a given shape. But results from my laboratory, reviewed here, demonstrate that unknown shapes that are displayed only once can be identified using a matching task. These findings show that our visual system can immediately encode shape information with no requirement for training trials. This encoding might be accomplished by neuronal circuits in the retina.

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Keywords: shape encoding; orientation filters; connectionist models

Citation: Ernest Greene. New encoding concepts for shape recognition are needed. AIMS Neuroscience, 2018, 5(3): 162-178. doi: 10.3934/Neuroscience.2018.3.162

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This article has been cited by:

1. Ernest Greene, Jack Morrison, Computational Scaling of Shape Similarity that Has Potential for Neuromorphic Implementation, IEEE Access, 2018, 1, 10.1109/ACCESS.2018.2853656
2. Ernest Greene, Comparing methods for scaling shape similarity, AIMS Neuroscience, 2019, 6, 2, 54, 10.3934/Neuroscience.2019.2.54

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