Karashchuk, L. | Search bioRxiv

bioRxiv2026

Mice navigate complex landscapes by learning a sparse vocabulary of paths

Effective navigation is critical for survival, but how animals contend with the scale, complexity, and self-similarity of natural landscapes is unknown. Here, we investigate spontaneous exploratory mouse behavior, both outdoors and in dark self-similar landscapes made from tens to hundreds of tiles of varying heights. Within 2 hours of exploration, mice detected small covert changes to the landscape and jumped across ledges to invisible landing spots, revealing detailed spatial knowledge. In this process, mice converged on a sparse set of memorized paths. These routes depended on prior experience and increased the compressibility of sensory information over time. Our results demonstrate that mice rapidly learn sparse internal representations of their surroundings and suggest that animals use structured behavioral sequences to reduce the complexity of natural navigation.

animal behavior and cognition↗

bioRxiv2024

Sensorimotor delays constrain robust locomotion in a 3D kinematic model of fly walking

Walking animals must maintain stability in the presence of external perturbations, despite significant temporal delays in neural signaling and muscle actuation. Here, we develop a 3D kinematic model with a layered control architecture to investigate how sensorimotor delays constrain robustness of walking behavior in the fruit fly, Drosophila. Motivated by the anatomical architecture of insect locomotor control circuits, our model consists of three component layers: a neural network that generates realistic 3D joint kinematics for each leg, an optimal controller that executes the joint kinematics while accounting for delays, and an inter-leg coordinator. The model generates realistic simulated walking that resembles real fly walking kinematics and sustains walking even when subjected to unexpected perturbations, generalizing beyond its training data. However, we found that the models robustness to perturbations deteriorates when sensorimotor delay parameters exceed the physiological range. These results suggest that fly sensorimotor control circuits operate close to the temporal limit at which they can detect and respond to external perturbations. More broadly, we show how a modular, layered model architecture can be used to investigate physiological constraints on animal behavior.

neuroscience↗