NCS-FO: Integrated neuroengineering of brain-inspired algorithms for parsing realistic environments

For some tasks, modern computers vastly outperform the human brain – for example, large-scale numerical calculations, or the precisely accurate recall of organized information. But for other important tasks, the brains of humans and other animals are far superior to any computing system that has been built, both in terms of what they can do and in terms of the startlingly low energy required.

For example, people can recognize familiar individuals at a distance, not only by their facial features, but by gait or other subtleties of movement that they might not even be able to articulate. People and other animals rapidly acquire information from their environments, but also are able to intelligently apply that information under novel, unforeseen circumstances.

The study of brain-inspired computing is devoted to learning fundamental new ways to think about how computers work with information, so that they can perform better on such weakly-defined, open-world problems. In parallel, advances in physics have produced new optical materials and methods that can perform computations very rapidly and with extraordinarily low energy costs – if the problems of interest can be structured in ways compatible with these brain-inspired computing techniques.

This project seeks to develop a brain-inspired computing network that learns rapidly and solves a set of real-world identification tasks, and to deploy this network onto portable devices as well as custom testing platforms built with these advanced physical substrates. A key goal is to show how these brain-inspired computing methods can achieve superior performance on open-world problems, most radically so when deployed on next-generation optical computer platforms.

In contrast to contemporary deep networks, the brain-inspired networks described in this proposal are based on heterogeneous elements and feedback-mediated dynamical systems, and operate based on fully localized computations that obviate the need for shared memory resources. Consequently, they learn rapidly, and when deployed on neuromorphic platforms such as Intel Loihi they exhibit increased speed and tremendously reduced energy budgets.

Importantly, state of the art photonic computing substrates are directly compatible with neuromorphic computational architectures, suggesting that they will be compelling platforms for these decentralized, brain-inspired computing algorithms. The intellectual merit of this project is to develop, deploy, and benchmark an established set of decentralized, brain-inspired algorithms designed for successful sensory identification under unpredictable, open-world conditions on a range of platforms, including leading-edge photonic computational substrates.

Specifically, the algorithms will be extended to incorporate higher-order brain-inspired circuit properties, deployed onto portable device platforms for use in the field, and also deployed and tested on photonic substrates to demonstrate the transformational potential of these computational platforms. Broader impacts include a continuing commitment by both PIs to supervising undergraduate research experiences for students from groups underrepresented in STEM on projects directly connected with the research proposed here, as well as the potential for development of a new generation of smart devices using neuromorphic methods.

PI Cleland also intends to incorporate the concepts discussed in this application into a unit of his advanced undergraduate Neural Representations seminar course.

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.