Modelling motor cortex using neural network control laws
Lillicrap, Timothy Paul
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The ease with which our brains learn to control our bodies belies intricate neural processing which remains poorly understood. We know that a network of brain regions work together in a carefully coordinated fashion to allow us to move from one place to another. In mammals, we know that the motor cortex plays a central role in this process, but precisely how its activity contributes to control is a matter of long and continued debate. In this thesis we demonstrate the need for developing mechanistic neural network models to address this question. Using such models, we show that contentious response properties of non-human primate primary motor cortex (M1) neurons can be understood as reflecting control processes which take into account the physics of the body. And we develop new computational techniques for teaching neural network models how to execute control. In the first study (Chapter 2), we critically examined a recently developed correlation-based descriptive model for characterizing the activity of M1 neuron activity. In the second study (Chapter 3), we developed neural network control laws which performed reaching and postural tasks using a physics model of the upper limb. We show that the population of artificial neurons in these networks exhibit preferences for certain directions of movement and certain forces applied during posture. These patterns parallel empirical observations in M1, and the model shows that the patterns reflect particular features of the biomechanics of the arm. The final study (Chapter 4) develops new techniques for building network models. To understand how the brain solves difficult control tasks we need to be able to construct mechanistic models which can do the same. And, we need to be able to construct controllers that compute via simple neuron-like units. In this study, we combine tools for automatic computation of derivatives with recently developed ideas about second-order approaches to optimization to build better neural network control laws. Taken together, this thesis helps develop arguments for, and the tools to build mechanistic neural network models to understand how motor cortex contributes to control of the body.