Biology subjects

Merel, J.

Publications and source records attributed to Merel, J..

3 recordsLinked to original sources

bioRxiv2026

Why do we seek information about the future? On the origins of subjective value without instrumental value

Why do we want to know the future? Humans and many animals pay for information to predict uncertain rewards, even when they cannot control them. The reason for this conserved yet seemingly paradoxical preference - "subjective value without instrumental value" - remains unknown. Here we develop a normative framework to explain the origins of such preferences, their persistence across species, and their contributions to survival. We formalize and evaluate theories that explain subjective value as originating from an adaptive estimate of advantage for solving core computational problems in naturalistic environments. We show that human and animal subjective values are remarkably well suited to solve these problems, accomplishing the goals of multiple theories simultaneously. We derive novel forms of information to enable existing theories to be dissociated, and show that pooling their subjective values improves performance across diverse environments. Thus, organisms may value information because it pays diverse dividends in nature.

neuroscience↗

bioRxiv2024

Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning

The body of an animal influences how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly Drosophila melanogaster in the MuJoCo physics engine. Our model is general-purpose, enabling the simulation of diverse fly behaviors, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion, both flight and walking. To support these behaviors, we have extended MuJoCo with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. We demonstrate the use of visual sensors and the re-use of a pre-trained general-purpose flight controller by training the model to perform visually guided flight tasks. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context.

animal behavior and cognition↗

bioRxiv2023

Interpretable and Generalizable Strategies for Stably Following Hydrodynamic Trails

Aquatic animals offer compelling evidence that flow sensing alone, without vision, is sufficient to guide a swimming organism to the source of an unsteady hydrodynamic trail. However, the sensory feedback strategies that allow these remarkable trail tracking abilities remain opaque. Here, by integrating mechanistic flow simulations with reinforcement learning techniques, we discovered two simple and equally effective strategies for hydrodynamic trail following. Though not a priori obvious, these strategies possess parsimonious interpretations, analogous to Braitenbergs simplest vehicles, where the agent senses local flow signals and turns away from or toward the direction of stronger signals. A rigorous stability analysis shows that the effectiveness of these strategies in robustly tracking flow currents is independent of the type of sensor but depends on sensor placement and the traveling nature of the flow signal. Importantly, these results inform a suite of versatile strategies for hydrodynamic trail following applicable to both vortical and turbulent flows. These insights support the future design and implementation of adaptive real-time sensory feedback strategies for autonomous robots in dynamic flow environments.

biophysics↗