Recurrent Self-Organizing Map Implemented
To Detection Of Temporal Line-movement
Patterns Of Lumbriculus Variegatus
(Oligochaeta: Lumbriculidae) In Response To
The Treatments Of Heavy Metal
Author(s)
K.-H. Son, C. W. Ji, Y.-M. Park, Y. Cui, H. Z. Wang,
T.-S. Chon & E. Y. Cha
Abstract
Measurement of behavioral responses have been recently considered as an
important method for monitoring risk assessment. Computational processing
could be applied to continuous data for automatic determination of changes in
behavioural states of indicator specimens. Behavioral monitoring could be used
as an alternative tool to fill the gaps between large (e.g., ecological survey) and
small (e.g., molecular analysis) scale methods for risk assessment. While the
points were conventionally used for indicating movement of test specimens, the
line shapes of blackworms, Lumbriculus variegatus, were trained by Artificial
Neural Networks in this study. We proposed an unsupervised temporal model,
Recurrent Self-Organizing Map (RSOM), to detect sequential changes in the
line-movement of blackworms after the treatments of a toxic substance, copper,
in this study. RSOM was feasible in addressing the stressful behaviors of
indicator specimens such as body contraction, high degree of folding, etc. We
demonstrated that the unsupervised temporal model is efficient in classifying
temporal behavior patterns and could be used as an alternative tool for the real-time
monitoring of toxic substances in aquatic ecosystems in the future.
Keywords: Recurrent Self-Organizing Map, response behavior, temporal
sequence processing, Lumbriculus variegates.
Keywords
Recurrent Self-Organizing Map, response behavior, temporal
sequence processing, Lumbriculus variegates.
Related Book
Other papers in this volume
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