UH Biocomputation Group - turbulencehttp://biocomputation.herts.ac.uk/2020-12-09T07:07:13+00:00A comparison between mouse, in silico, and robot odor plume navigation reveals advantages of mouse odor-tracking2020-12-09T07:07:13+00:002020-12-09T07:07:13+00:00Michael Schmuckertag:biocomputation.herts.ac.uk,2020-12-09:/2020/12/09/a-comparison-between-mouse-in-silico-and-robot-odor-plume-navigation-reveals-advantages-of-mouse-odor-tracking.html<p class="first last">Michael Schmucker's Journal Club session where he will talk about a paper "A comparison between mouse, in silico, and robot odor plume navigation reveals advantages of mouse odor-tracking".</p>
<p>This week on Journal Club session Michael Schmucker will talk about a paper "A comparison between mouse, in silico, and robot odor plume navigation reveals advantages of mouse odor-tracking".</p>
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<p>Localization of odors is essential to animal survival, and thus animals are
adept at odor navigation. In natural conditions animals encounter odor sources
in which odor is carried by air flow varying in complexity. We sought to
identify potential minimalist strategies that can effectively be used for
odor-based navigation and asses their performance in an increasingly chaotic
environment. To do so, we compared mouse, in silico model, and Arduino-based
robot odor-localization behavior in a standardized odor landscape. Mouse
performance remains robust in the presence of increased complexity, showing a
shift in strategy towards faster movement with increased environmental
complexity. Implementing simple binaral and temporal models of tropotaxis and
klinotaxis, an in silico model and Arduino robot, in the same environment as
the mice, are equally successful in locating the odor source within a plume of
low complexity. However, performance of these algorithms significantly drops
when the chaotic nature of the plume is increased. Additionally, both
algorithm-driven systems show more successful performance when using a strictly
binaral model at a larger sensor separation distance and more successful
performance when using a temporal and binaral model when using a smaller sensor
separation distance. This suggests that with an increasingly chaotic odor
environment, mice rely on complex strategies that allow for robust odor
localization that cannot be resolved by minimal algorithms that display robust
performance at low levels of complexity. Thus, highlighting that an animal's
ability to modulate behavior with environmental complexity is beneficial for
odor localization</p>
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<p>Papers:</p>
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<li>Gumaste A, Coronas-Samano G, Hengenius J, Axman R, Connor E, Baker K, Ermentrout B, Crimaldi J and Verhagen J V <a class="reference external" href="https://doi.org/10.1523/eneuro.0212-19.2019">"A comparison between mouse, in silico, and robot odor plume navigation reveals advantages of mouse odor-tracking."</a> , eNeuro 10 January 2020, 7 (1) ENEURO.0212-19.2019.</li>
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<p><strong>Date:</strong> 11/12/2020 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: online</p>