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Performance of Basic Strategies for following Gradients in Two Dimensions

โœ Scribed by DAVID B. DUSENBERY


Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
209 KB
Volume
208
Category
Article
ISSN
0022-5193

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โœฆ Synopsis


Computer models for following stimulus gradients in two-dimensional space were evaluated to determine the relative advantages of di!erent strategies and to identify the issues that must be addressed in making such a comparison. The simulations were implemented with emphasis on making them as general and free of speci"c assumptions as possible. Performance was de"ned as progress along the gradient divided by the cost of the movements and time taken. Plausible values of costs were taken from data on animal energetics. The models also included various kinds of noise that limit performance. These included unintended variations and biases in motor outputs as well as sensory inputs. An initial guess at appropriate noise levels led to performance worse than that observed in experiments with leukocytes. Reduced noise levels gave good agreement. Under these, more appropriate, conditions, peak performance for the various models varied from 24 to 99% of the maximum possible. The threshold gradient required to provide performance equal to 1% of the maximum possible varied from 800 to 5000 searcher diameters per gradient decay length. Some models performed well only over a narrow range of gradients. There was no indication of a tradeo! between sensitivity to shallow gradients and high performance in steep gradients. The model of tropotaxis (simultaneous, spatial comparison) with movement in any direction was superior in having the lowest threshold, the highest maximum performance, requiring the fewest parameters to "t, and performed well over the widest range of gradients. This result suggests that amoeboid cells and echinoderms might be particularly well suited to following gradients. The modeling demonstrates the need to obtain quantitative estimates for a number of parameters (relating costs and noise levels) for a more rigorous understanding of the relative advantages of these di!erent strategies.


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