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Analysis of behavioral changes of zebrafish ( Danio rerio) in response to formaldehyde using Self-organizing map and a hidden Markov model

Liu, Yuedan ; Lee, Sang-Hee ; Chon, Tae-Soo

Ecological modelling, 2011-07, Vol.222 (14), p.2191-2201 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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  • Título:
    Analysis of behavioral changes of zebrafish ( Danio rerio) in response to formaldehyde using Self-organizing map and a hidden Markov model
  • Autor: Liu, Yuedan ; Lee, Sang-Hee ; Chon, Tae-Soo
  • Assuntos: Behavioral monitoring ; Central and boundary zones ; Emission ; Emission probability matrix ; Formaldehyde ; Freshwater ; Markov processes ; Mathematical analysis ; Mathematical models ; Movement ; Movement pattern ; Response behavior ; Transition probabilities ; Transition probability matrix ; Zebrafish
  • É parte de: Ecological modelling, 2011-07, Vol.222 (14), p.2191-2201
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: ► Self-organizing map (SOM) and Hidden Markov model (HMM) were suitable in presenting dynamic processes of behavioral states. ► SOM was capable of patterning behavioral states of Danio rerio. ► HMM was useful in illustrating transition probabilities of behavioral states. ► Estimated transition and emission probabilities were accordingly evaluated to present behavioral states before and after chemical treatment. ► Computational methods are suitable in automatic behavioral monitoring. Two computational methods were applied to classification of movement patterns of zebrafish ( Danio rerio) to elucidate Markov processes in behavioral changes before and after treatment of formaldehyde (0.1 mg/L) in semi-natural conditions. The complex data of the movement tracks were initially classified by the Self-organizing map (SOM) to present different behavioral states of test individuals. Transition probabilities between behavioral states were further evaluated to fit Markov processes by using the hidden Markov model (HMM). Emission transition probability was also obtained from the observed variables (i.e., speed) for training with the HMM. Experimental transition and emission probability matrices were successfully estimated with the HMM for recognizing sequences of behavioral states with accuracy rates in acceptable ranges at central and boundary zones before (77.3–81.2%) and after (70.1–76.5%) treatment. A heuristic algorithm and a Markov model were efficiently combined to analyze movement patterns and could be a means of in situ behavioral monitoring tool.
  • Editor: Amsterdam: Elsevier B.V
  • Idioma: Inglês;Russo
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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