Prediction of Individual Fish Trajectory from Its Neighbors’ Movement by a Recurrent Neural Network

Abstract

Individuals in large groups respond to the movements and positions of their neighbors by following a set of interaction rules. These rules are central to understanding the mechanisms of collective motion. However, whether individuals actually use these rules to guide their movements remains untested. Here we show that the real-time movements of individual fish can be directly predicted from their neighbors’ motion. We train a recurrent neural network to predict the trajectories of individual fish from input signals. The inputs are projected to the recurrent network as time series representing the movements and positions of neighboring fish. By comparing the data output from the model with the target fish’s trajectory, we provide direct evidence that individuals guide their movements via interaction rules. Because the error between the model output and actual trajectory changes when the fish perceive a noxious contaminant, the model is potentially applicable to water quality monitoring.