Evolving Artificial Neural Networks for Simulating Fish Social Interactions

Can we use computational modeling to infer whether fish can remember or anticipate each other’s movements? What minimum of temporal input and internal complexity is sufficient to model a specific fish, or to produce generally “fish-like” behavior? Agent-based modeling to emulate biological behavior has been used to great effect, both in real-world and simulated experiments. We present feedforward neural network architectures for simulating fish social interactions, evolved using evolution strategies in two different experiments. Evolution of the temporal input of the partner fish’s position when testing models on labeled data uncovers anticipation or memory capacities used by a focal fish. When testing via a general discriminator for fish-like trajectories, the right neural network architecture and temporal input are shown to be a necessary, but insufficient condition for highly lifelike simulations. Lifelike simulations for some datasets are possible as simple functions of the input, showing variability in the complexity of individual fish’s social behaviors.