1Modelling of Cognitive Processes, Technical University of Berlin, Berlin, Germany 2Charité–Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany 3Bernstein Center for Computational Neuroscience, Berlin, Germany 4Science of Intelligence, Research Cluster of Excellence, Berlin, Germany
*Email:simone.ciceri@tu-berlin.de
Introduction Animal survival depends on the ability to maintain the stability of a set of internal variables, such as nutrient levels or water balance. This internal regulation, known as homeostasis, often requires the acquisition of resources via interactions with the external environment [1]. We reasoned that competition among multiple homeostatic needs combined with a rich environment may be sufficient to explain a wide range of complex behaviors. To test this hypothesis, we developed a control-theoretic problem setting for an agent that aims to preserve homeostasis of multiple internal variables while foraging in environments with distributed resources.
Methods We model a synthetic agent that actively forages to minimize deviations of its internal variables from their respective set points, which reflect its individual demands. These variables gradually decay over time but can be replenished by collecting resources from the environment. The resources are distributed around the environment to generate competition among different needs. We study the foraging behavior that results from minimizing a cost function that combines homeostatic errors and motion costs. In simple 1D environments, we obtain optimal behavioral policies using optimal control methods. In 2D settings, we parametrize the policies with artificial neural networks that are optimized using evolutionary algorithms.
Results We show that internal homeostasis can generate a rich repertoire of behaviors that depend on both the structure of the environment and internal demands. First, when resources are sparse the agent displays planning strategies, such as stocking up on one variable before foraging for others. Second, agent behaviors can be decomposed into a small set of simpler policies, each of which satisfies one internal need. The agent hierarchically selects from this set of behaviors based on its internal state. Finally, optimal strategies can be highly sensitive to the agent's demands. In the same environment, we can observe sudden transitions between different behaviors when changing the set point at which the internal variables need to be maintained.
Discussion Our model demonstrates the possible emergence of complex behavior from the simple goal of internal stability. Optimal foraging strategies are shaped by both environmental factors and internal demands, potentially accounting for the large variability often observed among individuals of the same species, even within the same environment. Our model also emphasizes how strongly the dynamics of the internal state—which are generally not accessible in behavioral experiments—are mirrored in the agent's behavior. The relevance of these findings is not confined to behavioral modeling and analysis: it is likely that the neural activity that drives animal behaviors will be similarly sensitive to the internal state of the animal.
Acknowledgements - References ● Woods, S. C., & Ramsay, D. S. (2007). Homeostasis: Beyond Curt Richter. Appetite, 49(2), 388-398.https://doi.org/10.1016/j.appet.2006.09.015
