NSF-BSF: AF: Small: An Algorithmic Theory of Brain Networks

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Understanding how the brain works, as a computational device, is a central challenge of modern neuroscience and AI. Different research communities approach this challenge in different ways, including examining neural network structure as a clue to computational function, using functional imaging to study neural activation patterns, developing theory based on simplified models of neural computation, and engineering of neural-inspired machine learning architectures. This project will approach the problem using techniques from distributed computing theory and other branches of theoretical computer science. This project has the potential to improve our understanding of computation in the brain, by identifying key problems that are solved in the brain and key mechanisms that may be used to solve them. This work can also have impact on theoretical computer science, by contributing a new and fruitful direction for theoretical study. This collaboration between MIT and the Weizmann Institute in Israel will increase the participation of women and minority participants in this field and will seek to bridge the gap between computer scientists and biology researchers.Specifically, the project develops an algorithmic theory for brain networks, based on novel stochastic Spiking Neural Network models with general interconnection patterns. It defines a collection of abstract problems to be solved by these networks, inspired by problems that are solved in actual brains, such as problems of focus, recognition, learning, and memory. The project designs algorithms (networks) that solve the problems, and analyze them in terms of static costs such as network size, and dynamic costs such as time to converge to a correct solution. The investigators consider tradeoffs between the various costs, and will prove corresponding lower bound results. The models, problems, and solutions should be simple enough to enable theoretical analysis, yet realistic enough to provide insight into the behavior of real neural networks.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.