FET: Medium: Neural network computation and learning in well-mixed and spatially-organized molecular systems

The principles of neural computation, developed mostly over the past 50-years, have led to profoundly improved understanding of how brains process information and have served as the foundation for modern machine learning techniques. Only recently has it begun to be appreciated the extent to which these principles of neural computation apply more broadly and can be found operating within non-neural systems.

In particular, the implications and potential of neural computing principles for designing advanced synthetic molecular systems are only beginning to be explored. Key principles include the power of high-dimensional pattern recognition using linear threshold units and winner-take-all competition, learning from experience to sculpt network connection strengths, the generation of complex patterns from the resonance of network dynamics, and the interplay between spatial structure and computing capabilities.

Molecular programming using DNA nanotechnology now has sufficiently developed methods to design systems that explore and exploit these neural computing principles. This research will pioneer three new types of DNA neural networks: those capable of unsupervised learning of complex patterns in their environment, those capable of complex spatial pattern formation as reaction-diffusion systems, and those capable of exploiting the molecular-scale spatial organization of DNA nanostructures to perform more efficient computation in certain regimes of operation.

In the long term, the incorporation of neural computation principles into future molecular systems will open doors to new applications ranging from biomedicine to materials science. In the medium term, an important impact of this project will be the future careers of the postdocs and graduate students who will benefit from the research experience and mentoring, whether they move on to academia, industry, or entrepreneurship.

In the short term, the researchers will incorporate their scientific understanding into online software tools, continue to integrate research with education, provide undergraduates with mentored summer research, enhance the interdisciplinary research environment, and involve more women in science. They will also increase public engagement with science by presenting at public events, interviewing for popular science magazines, and creating artworks to illustrate their research.

Specific research goals for the development of the three new types of DNA neural networks are as follows. First, learning is arguably the most desirable property of synthetic molecular circuits. This project builds on the researchers’ current work demonstrating supervised learning and expands it to the broader category of unsupervised learning.

A limitation of supervised learning is that a “teacher” must provide training examples that indicate what should be learned. Unsupervised learning addresses this limitation by exposing the molecular circuits to only what they encounter but not how they should respond; this new capability would be necessary for molecular robotic systems that operate autonomously, as cells do, within a molecular milieu.

Second, reaction-diffusion pattern formation has been studied since Alan Turing’s seminal work on morphogenesis, both for its relevance to biological patterning and as an intrinsic physical mechanism of self-organization. However, the complexity of reaction-diffusion patterns has been limited. The researchers will leverage recent breakthroughs in deep learning techniques to design complex reaction-diffusion networks by example.

They will use the differentiable programming approach, combined with the recent advances in the synthesis of large DNA neural networks and reliable DNA hydrogels as a spatial substrate for reaction-diffusion experiments. Finally, the researchers will perform a theoretical study that applies their expertise in DNA origami tiles and surface-localized chemical reaction networks to introduce a novel computing architecture for DNA neural networks.

This architecture provides a new trade-off between design complexity and molecular operation that may scale better than prior approaches as the network size increases.

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.