Neural Circuit Architectural Priors for Rat Locomotion

Animals generate behavior through continuous interactions between their neural circuits, bodies, and environment. Embodied simulations provide a means to study these interactions by testing neural circuit models in biologically realistic settings. Recent efforts have developed embodied simulations of diverse animals, and emerging work on biologically grounded neural architectures has begun to integrate circuit connectivity and single-unit dynamics from experimental data into artificial neural networks, introducing stronger inductive biases and closer mechanistic alignment with biology. How can we develop embodied simulations that enable the study of increasingly complex bodies and neural circuits? In this work, we address the challenge through coordinated advances in the simulated body, neural architecture, and training algorithm. We introduce Rat v2, a biomechanical model that provides an efficient and flexible tool for simulating rat behavior. To control this body, we develop Rat NCAP, a biologically grounded neural architecture for locomotion based on mammalian spinal circuits that incorporates cell-type-specific connectivity and a novel flexor-extensor neuromechanical interface. To optimize circuit parameters, we refine an evolutionary algorithm to support reliable and efficient training. The integrated system learns to locomote at different speeds without imitation learning, producing locomotion statistics and gaits that match experimental data. Systematic analyses reveal that neuromechanical interface choice substantially affects trainability and gait quality, and that specific neural populations play distinct roles in gait generation. Together, these findings support the view that embodied simulations at an intermediate level of abstraction can simultaneously reproduce complex behavior and yield mechanistic insights into the neural circuits that generate it.