Using Optimality to Predict Photoreceptor Distribution in the Retina

The concept of evolution implies that fitness traits of an organism tend toward some constrained optimality. Here, the fitness trait we consider is the distribution of photoreceptors on an organism’s retina. We postulate that an organism’s photoreceptor distribution optimizes some balance between two quantities, a benefit and a cost. The benefit is defined as the area of the field of vision. The cost is defined as the amount of time spent saccading to some target in the visual field; during this time we assume nothing is seen. Three constraints are identified. First, we assume proportional noise exists in the motor command. Second, we assume saccades are a noisy process. Third, we constrain the number of total photoreceptors. This simplified model fails to predict the human retinal photoreceptor distribution in full detail. Encouragingly, the photoreceptor distribution it predicts gets us closer to that goal. We discuss possible reasons for its current failure, and we suggest future research directions.

Authors and Affiliations

1. Department of Computer Science, University of Otago, Dunedin, New Zealand

   Travis Monk

2. Centre for Theoretical and Computational Neuroscience, University of Plymouth, Plymouth, Devon, PL4 8AA, UK

   Chris Harris

Editors and Affiliations

1. Kyushu Institute of Technology, Network Design and Research Center, 680-4 Fukuoka, 820-8502, Kawazu, Iizuka, Japan

   Mario Köppen

2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

   Nikola Kasabov

3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street, 1142, Auckland, New Zealand

   George Coghill

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