TY - GEN
T1 - A neural model of landmark navigation in the fiddler crab Uca lactea
AU - Cho, Hyunggi
AU - Kim, Daeeun
PY - 2009
Y1 - 2009
N2 - The fiddler crabs, Uca lactea, which live on intertidal mudflats, exhibit a remarkable ability to return to its burrow. It has been reported that the species usually use path integration, an ideothetic mechanism for short-range homing. During the mating season, however, the accumulation error of the process increases due to vigorous courtship movement. To compensate for this, most courting males construct the vertical mud structures, called semidomes, at the entrance of their burrows and use them as landmarks. Here, we suggest a possible neural model that demonstrates how visual landmark navigation could be implemented in the fiddler crab's central nervous system. The model consisting of two levels of population of neurons, is based on the snapshot hypothesis and a simplified version of Franz's algorithm is used for the computation of home vector.
AB - The fiddler crabs, Uca lactea, which live on intertidal mudflats, exhibit a remarkable ability to return to its burrow. It has been reported that the species usually use path integration, an ideothetic mechanism for short-range homing. During the mating season, however, the accumulation error of the process increases due to vigorous courtship movement. To compensate for this, most courting males construct the vertical mud structures, called semidomes, at the entrance of their burrows and use them as landmarks. Here, we suggest a possible neural model that demonstrates how visual landmark navigation could be implemented in the fiddler crab's central nervous system. The model consisting of two levels of population of neurons, is based on the snapshot hypothesis and a simplified version of Franz's algorithm is used for the computation of home vector.
UR - http://www.scopus.com/inward/record.url?scp=84866719591&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866719591&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84866719591
SN - 2930307099
SN - 9782930307091
T3 - ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
SP - 355
EP - 360
BT - ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
T2 - 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009
Y2 - 22 April 2009 through 24 April 2009
ER -