Examines how brain neural circuits and function can affect the design of machine learning hardware and software, and vice versa. Builds an understanding of how similar and different the computational approaches of the two are, and what can be deduced from one area about the other. Studies the relationship between brain neural circuits and machine learning design, exploring how insights from one can inform the other. Compares biological concepts like neurons, connectomes, and non-backpropagation learning with artificial neural network hardware and software designs, scaling laws, and state-of-the-art optimization techniques.