Year: 2026
Type: preprint
Abstract: Understanding how the brain supports flexible, goal-directed behavior is a central aim of neuroscience. Yet such behavior is highly variable and unfolds over long timescales, making it challenging to understand its organization. Advances in behavioral tracking quantify movements but do not reveal the objectives that guide them. Motivated by this, we develop inverse reinforcement learning (IRL) into a general framework for inferring these objectives from naturalistic behavior. Our approach represents an animal's objectives as internal rewards, and infers these rewards as well as the policies they induce from observed behavior. It applies across diverse behavioral settings, from freely moving behaviors to structured tasks. It can infer objectives that switch over time or gradually change with experience. In mice capturing crickets, it identified a small set of objectives that the animals switched between over the course of a trial. In mice learning a cued labyrinth, it inferred how the animals' internal rewards changed as learning progressed. Across both datasets, the models predicted held-out actions and generated extended behavior that reproduced key features of animal performance. By transforming long, variable behavioral sequences into time-resolved estimates of the animal's objectives, our approach provides a quantitative basis for studying how the brain organizes natural behavior.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Unified, Scalable, and Reproducible Neurostatistical Software, The Dynamics of Neural Representations for Distinct Spatial Contexts and Memory Episodes, CAREER: State Space Models for Neural and Behavioral Data, Development and validation of a porcine model of spinal cord injury-induced neuropathic pain, Precise neuromodulation for encoding reward in the hippocampus, Behavioral biomarkers for prediction of mortality and seizures in epilepsy, Interaction of external inputs with internal dynamics: influence of brain states on neural computation and behavior, Serial transformations in the olfactory system, CRCNS: Deconstructing dynamics of motor cortex in freely moving behavior, Understanding Sensorimotor Control Through Realistic Neuro-Biomechanical Simulation
OpenAlex ID: W7215111947