A model of ant route navigation driven by scene familiarity.
In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002336&type=printable |
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| _version_ | 1849726902809395200 |
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| author | Bart Baddeley Paul Graham Philip Husbands Andrew Philippides |
| author_facet | Bart Baddeley Paul Graham Philip Husbands Andrew Philippides |
| author_sort | Bart Baddeley |
| collection | DOAJ |
| description | In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints. |
| format | Article |
| id | doaj-art-647a1ee0ed0c4909ba5cea4dab0f2f47 |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-647a1ee0ed0c4909ba5cea4dab0f2f472025-08-20T03:10:02ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582012-01-0181e100233610.1371/journal.pcbi.1002336A model of ant route navigation driven by scene familiarity.Bart BaddeleyPaul GrahamPhilip HusbandsAndrew PhilippidesIn this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002336&type=printable |
| spellingShingle | Bart Baddeley Paul Graham Philip Husbands Andrew Philippides A model of ant route navigation driven by scene familiarity. PLoS Computational Biology |
| title | A model of ant route navigation driven by scene familiarity. |
| title_full | A model of ant route navigation driven by scene familiarity. |
| title_fullStr | A model of ant route navigation driven by scene familiarity. |
| title_full_unstemmed | A model of ant route navigation driven by scene familiarity. |
| title_short | A model of ant route navigation driven by scene familiarity. |
| title_sort | model of ant route navigation driven by scene familiarity |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002336&type=printable |
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