The Virtual Rodent: A Platform to Study the Artificial and Biological Control of Natural Behavior

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Project Summary Controlling complex bodies in uncertain environments is a challenge our brains have evolved to perfect, yet the algorithms and neural network implementations that enable flexible and robust control have been difficult to identify. This proposal is premised on the idea that progress will be served by embracing the complexities of the underlying control systems, including the bodies they control and the diversity of animal behavior. To test this idea and, more generally, provide a versatile platform for interrogating the neural circuit-level principles and mechanisms underlying embodied motor control, I propose the virtual rodent. This in-silico animal will have a body like a real rat, experience normal physics, and be trained to produce naturalistic rat behaviors. It will have an artificial brain that can be fully interrogated, manipulated, and reconfigured. After establishing this platform, I will develop an analysis approach to compare in-vivo neural activity from freely moving animals to the network representations of the model. This endeavor expands upon recent approaches linking neural representations with the representations of task-optimized artificial models in sensory systems, enabling the comparison of neural activity with analytical models in the motor domain and during complex behavior. I then propose to further develop the virtual rodent to probe questions related to hierarchical control and motor learning in animals and machines. In the F99 phase of this proposed research, I will continue to develop the virtual rodent as a platform to study the artificial and biological control of natural behavior. Specifically, in Aim 1, I will finalize a behavioral measurement, processing, and modeling pipeline to train artificial neural networks to imitate the behaviors of real rodents while in a physical simulator, validate its performance, and demonstrate its utility as a model for embodied motor control. In Aim 2, I will then record from motor centers of real rodents as they freely move and compare their neural activity to the network activity of models enacting the same diverse movements. In the K00 phase of this proposed research, I will expand upon the virtual rodent model to study hierarchical control, a conserved feature of flexible and adaptive mammalian control. I will train an artificial neural network to reuse lower-level control modules created as part of the F99 phase to autonomously solve motor tasks commonly used in motor neuroscience research. This Aim is of great value to the field of motor neuroscience as it will facilitate the comparison of neural activity of animals performing controlled tasks with the network activity of analytical models performing physically simulated analogues of the same tasks. Together, these Aims offer a new path in the study of the neural control of movement, one which embraces the complexity of behavior and biomechanics to advance our understanding of flexible and adaptive motor control in health and disease.