Policy optimization emerges from noisy representation learning

Nervous systems learn representations of the world and policies to act within it. We present a framework that uses reward-dependent noise to facilitate policy opti- mization in representation learning networks. These networks balance extracting normative features and task-relevant information to solve tasks. Moreover, their representation changes reproduce several experimentally observed shifts in the neural code during task learning. Our framework presents a biologically plausible mechanism for emergent policy optimization amid evidence that representation learning plays a vital role in governing neural dynamics. Code is available at: NeuralThermalOptimization.