Pattern recognition of the movement tracks of medaka ( Oryzias latipes) in response to sub-lethal treatments of an insecticide by using artificial neural networks

Abstract

Specimens of medaka ( Oryzias latipes) were observed continuously through an automatic image recognition system before and after treatments of an anti-cholinesterase insecticide, diazinon (0.1 mg/l), for 4 days in semi-natural conditions (2 days before treatment and 2 days after treatment). The “smooth” pattern was typically shown as a normal movement behavior, while the “shaking” pattern was frequently observed after treatments of diazinon. These smooth and shaking patterns were selected for training with an artificial neural network. Parameters characterizing the movement tracks, such as speed, degree of backward movements, stop duration, turning rate, meander, and maximum distance movements in the y-axis of 1-min duration, were given as input (six nodes) to a multi-layer perceptron with the backpropagation algorithm. Binary information for the smooth and shaking patterns was separately given as the matching output (one node), while eight nodes were assigned to a single hidden layer. As new input data were given to the trained network, it was possible to recognize the smooth and shaking patterns of the new input data. Average recognition rates of the smooth pattern decreased significantly while those for the shaking pattern increased to a higher degree after treatments of diazinon. The trained network was able to reveal the difference in the shaking pattern in different light phases before treatments of diazinon. This study demonstrated that artificial neural networks could be useful for detecting the presence of toxic chemicals in the environment by serving as in-situ behavioral monitoring tools.