RI: Small: Modules for neural computation

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Artificial intelligence need not replicate the human brain, but may constructively take inspiration from it. The brain is organized hierarchically, from large brain structures to smaller regions, cortical columns, all the way down to microcircuits made of a few interacting neurons. These modules may make our brains more efficient by dissecting problems into locally solvable subproblems. This in turn may make us learn faster by figuring out where in the brain a mistake was made and allow us to do better on new problems. Modules may also make the brain more efficient, allowing it to use fewer neurons and synapses. In those areas where brains seem to benefit from modularity, modern deep learning systems appear to be weaker. Building modules into deep neural networks promises to greatly improve generalization, interpretability, credit assignment in learning, computational cost and make them more resilient to adversarial stimuli. The result of this project will be improvements to the performance and understanding of modern artificial intelligence systems. The project will contribute in a broad way to the dissemination of computational results to neuroscience and of neuroscience results to the computational community through a combination of summer schools, teaching, and publishing. To meet these goals, this project enables a broadly interdisciplinary approach both to produce systems with modularity and to dissect their modular aspects. The research aims to build networks that, while learning, dissect training tasks by incrementally developing structural modules. This is done by minimizing cost functions that evaluate the community structure of neuron connectivity. It will design learning algorithms that encourage modularity by performing credit assignment at the level of modules. This is done by gating learning at both the neuron and the module level. Finally, the project team will develop tools for interpreting modular networks. This is done by performing psychophysical experiments with human subjects and by computational analysis. All of these components interrelate, provide a unified picture of how modularity can improve current machine learning approaches, and build a new bridge between neuroscience and deep learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.