We introduce a novel methodology for describing animal behavior as a tradeoff between value and complexity, using the Morris Water Maze navigation task as a concrete example. We develop a dynamical system model of the Water Maze navigation task, solve its optimal control under varying complexity constraints, and analyze the learning process in terms of the value and complexity of swimming trajectories. The value of a trajectory is related to its energetic cost and is correlated with swimming time. Complexity is a novel learning metric which measures how unlikely is a trajectory to be generated by a naive animal. Our model is analytically tractable, provides good fit to observed behavior and reveals that the learning process is characterized by early value optimization followed by complexity reduction. Furthermore, complexity sensitively characterizes behavioral differences between mouse strains.
Bibliographical noteFunding Information:
This study was supported by Advanced European Research Council (ERC) grant 340063 (project RATLAND; https://erc.europa.eu/) and F.I. R.S.T. grant 1075/13 to IN, and by a personal grant from the Israel Science Foundation (https://www. isf.org.il/) grant no. 575/17 to SS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
© 2020 Amir et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.