scholarly journals Sector search strategies for odor trail tracking

2022 ◽  
Vol 119 (1) ◽  
pp. e2107431118
Author(s):  
Gautam Reddy ◽  
Boris I. Shraiman ◽  
Massimo Vergassola
Keyword(s):  
Polymer Physics ◽  
Sequential Optimization ◽  
Odor Trail ◽  
Learning Agents ◽  
Search Patterns ◽  
Behavioral Paradigm ◽  
Scent Trails ◽  
Loss Of Contact ◽  
Tracking Behavior ◽  
Geometric Elements

Ants, mice, and dogs often use surface-bound scent trails to establish navigation routes or to find food and mates, yet their tracking strategies remain poorly understood. Chemotaxis-based strategies cannot explain casting, a characteristic sequence of wide oscillations with increasing amplitude performed upon sustained loss of contact with the trail. We propose that tracking animals have an intrinsic, geometric notion of continuity, allowing them to exploit past contacts with the trail to form an estimate of where it is headed. This estimate and its uncertainty form an angular sector, and the emergent search patterns resemble a “sector search.” Reinforcement learning agents trained to execute a sector search recapitulate the various phases of experimentally observed tracking behavior. We use ideas from polymer physics to formulate a statistical description of trails and show that search geometry imposes basic limits on how quickly animals can track trails. By formulating trail tracking as a Bellman-type sequential optimization problem, we quantify the geometric elements of optimal sector search strategy, effectively explaining why and when casting is necessary. We propose a set of experiments to infer how tracking animals acquire, integrate, and respond to past information on the tracked trail. More generally, we define navigational strategies relevant for animals and biomimetic robots and formulate trail tracking as a behavioral paradigm for learning, memory, and planning.

scholarly journals Sector search strategies for odor trail tracking

2021 ◽  
Author(s):  
Gautam Reddy ◽  
Boris I. Shraiman ◽  
Massimo Vergassola
Keyword(s):  
Close Contact ◽  
Sequential Optimization ◽  
Odor Trail ◽  
Optimization Framework ◽  
Behavioral Paradigm ◽  
Scent Trails ◽  
Loss Of Contact ◽  
Tracking Behavior ◽  
Geometric Elements ◽  
Unified Description

Terrestrial animals such as ants, mice and dogs often use surface-bound scent trails to establish navigation routes or to find food and mates, yet their tracking strategies are poorly understood. Tracking behavior features zig-zagging paths with animals often staying in close contact with the trail. Upon sustained loss of contact, animals execute a characteristic sequence of sweeping “casts” – wide oscillations with increasing amplitude. Here, we provide a unified description of trail-tracking behavior by introducing an optimization framework where animals search in the angular sector defined by their estimate of the trail’s heading and its uncertainty.In silicoexperiments using reinforcement learning based on this hypothesis recapitulate experimentally observed tracking patterns. We show that search geometry imposes limits on the tracking speed, and quantify its dependence on trail statistics and memory of past contacts. By formulating trail-tracking as a Bellman-type sequential optimization problem, we quantify the basic geometric elements of optimal sector search strategy, effectively explaining why and when casting is necessary. We propose a set of experiments to infer how tracking animals acquire, integrate and respond to past information on the tracked trail. More generally, we define navigational strategies relevant for animals and bio-mimetic robots, and formulate trail-tracking as a novel behavioral paradigm for learning, memory and planning.

scholarly journals Mice follow odor trails using stereo olfactory cues and rapid sniff to sniff comparisons

2018 ◽  
Author(s):  
Peter W Jones ◽  
Nathan N Urban
Keyword(s):  
Neural Substrates ◽  
Olfactory Cues ◽  
Odor Trail ◽  
Odor Concentration ◽  
Trail Use ◽  
Brain Circuits ◽  
Behavioral Paradigm ◽  
The Brain ◽  
Localization Behavior ◽  
Behavioral Monitoring

AbstractAnimals use the distribution of chemicals in their environment to guide behaviors essential for life, including finding food and mates, and avoiding predators. The presence of this general class of behavior is extremely widespread, even though the olfactory sensory apparatus and strategies used may vary between animals. The strategies and cues used by mammals to localize and track odor sources have recently become of interest to neuroscientists, but are still poorly understand. In order to study how mice localize odors, we trained mice to perform a trail following task using a novel behavioral paradigm and behavioral monitoring setup. We find that mice, in order to follow an odor trail, use both sniff by sniff odor concentration comparisons and internares comparisons to guide their behavior. Furthermore, they employ olfactory information to guide adaptive behaviors with remarkably short latencies of approximately 80ms. This study and its findings establish a rich, quantifiable olfactory localization behavior in mice that is amenable to physiological investigations and motivates investigation into the neural substrates of the identified olfactory cues.SignificanceMany animals, like rodents, rely heavily on their sense smell to guide them as they navigate their environment, to find food and mates and to avoid predators. Yet, in mammals, this function of the olfactory system is much less well studied than odor identification. We created a behavioral task where mice had to follow odor trails in order to efficiently find food and then tracked their movements around those trails. We found that they respond to sniff-by-sniff changes in odor intensity, using those changes to guide movements in less than one tenth of a second and also confirm that they use stereo cues to guide behavior. These results lay the groundwork for determining the brain circuits underlying olfactory navigation.

Use of the Magnetostatic Analogue In Electromagnetic Lens Engineering

1970 ◽  
Vol 28 ◽  
pp. 360-361
Author(s):  
John W. Coleman
Keyword(s):  
Boundary Conditions ◽  
High Performance ◽  
Force Fields ◽  
Optical Design ◽  
Direct Conversion ◽  
Design Engineering ◽  
Design Data ◽  
Scalar Potentials ◽  
Geometric Elements ◽  
At Will

In the design engineering of high performance electromagnetic lenses, the direct conversion of electron optical design data into drawings for reliable hardware is oftentimes difficult, especially in terms of how to mount parts to each other, how to tolerance dimensions, and how to specify finishes. An answer to this is in the use of magnetostatic analytics, corresponding to boundary conditions for the optical design. With such models, the magnetostatic force on a test pole along the axis may be examined, and in this way one may obtain priority listings for holding dimensions, relieving stresses, etc..The development of magnetostatic models most easily proceeds from the derivation of scalar potentials of separate geometric elements. These potentials can then be conbined at will because of the superposition characteristic of conservative force fields.

Machine-Scored Syntax: Comparison of the CLAN Automatic Scoring Program to Manual Scoring

2020 ◽  
Vol 51 (2) ◽  
pp. 479-493
Author(s):  
Jenny A. Roberts ◽  
Evelyn P. Altenberg ◽  
Madison Hunter
Keyword(s):  
Data Exchange ◽  
Child Language ◽  
Exchange System ◽  
Absolute Point ◽  
Language Analysis ◽  
Search Patterns ◽  
Language Data ◽  
Report Accuracy ◽  
Point To Point ◽  
Automatic Scoring

Purpose The results of automatic machine scoring of the Index of Productive Syntax from the Computerized Language ANalysis (CLAN) tools of the Child Language Data Exchange System of TalkBank (MacWhinney, 2000) were compared to manual scoring to determine the accuracy of the machine-scored method. Method Twenty transcripts of 10 children from archival data of the Weismer Corpus from the Child Language Data Exchange System at 30 and 42 months were examined. Measures of absolute point difference and point-to-point accuracy were compared, as well as points erroneously given and missed. Two new measures for evaluating automatic scoring of the Index of Productive Syntax were introduced: Machine Item Accuracy (MIA) and Cascade Failure Rate— these measures further analyze points erroneously given and missed. Differences in total scores, subscale scores, and individual structures were also reported. Results Mean absolute point difference between machine and hand scoring was 3.65, point-to-point agreement was 72.6%, and MIA was 74.9%. There were large differences in subscales, with Noun Phrase and Verb Phrase subscales generally providing greater accuracy and agreement than Question/Negation and Sentence Structures subscales. There were significantly more erroneous than missed items in machine scoring, attributed to problems of mistagging of elements, imprecise search patterns, and other errors. Cascade failure resulted in an average of 4.65 points lost per transcript. Conclusions The CLAN program showed relatively inaccurate outcomes in comparison to manual scoring on both traditional and new measures of accuracy. Recommendations for improvement of the program include accounting for second exemplar violations and applying cascaded credit, among other suggestions. It was proposed that research on machine-scored syntax routinely report accuracy measures detailing erroneous and missed scores, including MIA, so that researchers and clinicians are aware of the limitations of a machine-scoring program. Supplemental Material https://doi.org/10.23641/asha.11984364

Polymer Physics

2009 ◽  
Author(s):  
Fumihiko Tanaka
Keyword(s):  
Polymer Physics
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