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. 2020 Mar 20;14:218. doi: 10.3389/fnins.2020.00218

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Copyright © 2020 Liu, Papale, Hengenius, Patel, Ermentrout and Urban.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Analysis of reduced models and parameter sweeps. (A) The full model (CSM) included three components: (1) a concentration-dependent velocity term (V), (2) a concentration-dependent casting term (C); and, (3) a binaral-olfaction term (B). Removal of any of these components (–V), (–C), or (–B) may, in principle, reduce model performance. (B) Comparison of noise effects on CSM success rate shows that the CSM model is robust for a wide array of different noise conditions, but fails if random noise approaches 100% or Gaussian filtering approaches 0 mm. (C) We added a coefficient in front of the hyperbolic tangent term in Eq. 5 and set it equal to 3. With this additional term, (–B) had a larger effect on performance; however, the effect of removing (–V) or (–C) terms decreased.