A Mathematical Model for Electrolocation in Weakly Electric Fishes
Weakly electric fishes usually live in the rivers of Africa and South America. They are able to orient themselves thanks to an electric sense, called electrolocation, that consist of emitting an electric field and sensing it at the surface of their skin. Whenever an object is nearby the fish, it dis...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2016-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | http://dx.doi.org/10.1051/bioconf/20160603004 |
| _version_ | 1818338962341625856 |
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| author | Boulier Thomas |
| author_facet | Boulier Thomas |
| author_sort | Boulier Thomas |
| collection | DOAJ |
| description | Weakly electric fishes usually live in the rivers of Africa and South America. They are able to orient themselves thanks to an electric sense, called electrolocation, that consist of emitting an electric field and sensing it at the surface of their skin. Whenever an object is nearby the fish, it distorts the emitted electric field; this perturbation is recorded by the electroreceptors, hence allowing the fish to recognize the object.
Discovered in 1958 by the work of Hans Lissmann and Ken Machin, this electric sense has then been intensively studied. We now know that weakly electric fishes are able to locate obstacles with an accuracy of about one centimetre, and to differentiate between different shapes and/or materials. However, a fundamental question has risen from a physical point of view: how is it possible to recover such details with such a low-amplitude (1 mV) and low-frequency (1 kHz) signal?
Mathematical modelling of the electric field emitted by these fishes allow us to quantify,
theoretically and numerically, the information contained into this signal. In this work, we propose to
derive the equations governing the emitted-and-perturbed electric field of a fish; we also describe
possible algorithms to recover the position and the shape of an object. In particular, we show that the
use of multiple frequencies can give us the location of an object, and the use of movement can lead us
to the differentiation between different shapes, into a machine-learning framework. |
| first_indexed | 2024-12-13T15:19:26Z |
| format | Article |
| id | doaj.art-b721c6e678f64ab2a48b1c618e6a4ba4 |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-12-13T15:19:26Z |
| publishDate | 2016-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-b721c6e678f64ab2a48b1c618e6a4ba42022-12-21T23:40:37ZengEDP SciencesBIO Web of Conferences2117-44582016-01-0160300410.1051/bioconf/20160603004bioconf_eabs2016_03004A Mathematical Model for Electrolocation in Weakly Electric FishesBoulier ThomasWeakly electric fishes usually live in the rivers of Africa and South America. They are able to orient themselves thanks to an electric sense, called electrolocation, that consist of emitting an electric field and sensing it at the surface of their skin. Whenever an object is nearby the fish, it distorts the emitted electric field; this perturbation is recorded by the electroreceptors, hence allowing the fish to recognize the object. Discovered in 1958 by the work of Hans Lissmann and Ken Machin, this electric sense has then been intensively studied. We now know that weakly electric fishes are able to locate obstacles with an accuracy of about one centimetre, and to differentiate between different shapes and/or materials. However, a fundamental question has risen from a physical point of view: how is it possible to recover such details with such a low-amplitude (1 mV) and low-frequency (1 kHz) signal? Mathematical modelling of the electric field emitted by these fishes allow us to quantify, theoretically and numerically, the information contained into this signal. In this work, we propose to derive the equations governing the emitted-and-perturbed electric field of a fish; we also describe possible algorithms to recover the position and the shape of an object. In particular, we show that the use of multiple frequencies can give us the location of an object, and the use of movement can lead us to the differentiation between different shapes, into a machine-learning framework.http://dx.doi.org/10.1051/bioconf/20160603004 |
| spellingShingle | Boulier Thomas A Mathematical Model for Electrolocation in Weakly Electric Fishes BIO Web of Conferences |
| title | A Mathematical Model for Electrolocation in Weakly Electric Fishes |
| title_full | A Mathematical Model for Electrolocation in Weakly Electric Fishes |
| title_fullStr | A Mathematical Model for Electrolocation in Weakly Electric Fishes |
| title_full_unstemmed | A Mathematical Model for Electrolocation in Weakly Electric Fishes |
| title_short | A Mathematical Model for Electrolocation in Weakly Electric Fishes |
| title_sort | mathematical model for electrolocation in weakly electric fishes |
| url | http://dx.doi.org/10.1051/bioconf/20160603004 |
| work_keys_str_mv | AT boulierthomas amathematicalmodelforelectrolocationinweaklyelectricfishes AT boulierthomas mathematicalmodelforelectrolocationinweaklyelectricfishes |