NSF supports Lehigh interdisciplinary research effort to engineer a living neural network

Will the computers of tomorrow be manufactured, or will they be cultivated?

This question lies at the heart of new research from Lehigh University that aims to engineer a neural network—a computer system modeled on the human brain and nervous system—from actual living cells, and program it to compute a basic learning task.

The National Science Foundation (NSF) has recently announced its support for the project, to the tune of more than $500,000, as part of a wider NSF effort, announced September 11, in support of Understanding the Brain and the BRAIN Initiative, a coordinated research effort that seeks to accelerate the development of new neurotechnologies.

“Recent developments in optogenetics, patterned optical stimulation, and high-speed optical detection enable simultaneous stimulation and recording of thousands of living neurons,” says Xiaochen Guo, an assistant professor of electrical and computer engineering at Lehigh University and principal investigator on the project. “And scientists already know that connected biological living neurons naturally exhibit the ability to perform computations and to learn. With support from NSF, we will be building an experimental testbed that will enable optical stimulation and detection of the activity in a living network of neurons, and we’ll develop algorithms to train it.”

The team, which includes Lehigh associate professors and co-principal investigators Yevgeny Berdichevsky of bioengineering and Zhiyuan Yan of electrical and computer engineering, brings together complementary expertise in computer architecture, bioengineering, and signal processing. The team believes their effort could have “transformative impact” in the fields of neuron science and computer engineering.

In the research, images of handwritten digits will be encoded into what are called "spike train stimuli," similar to a two-dimensional bar code. The encoding of the spike train will then be optically applied to a group of networked in vitro neurons with optogenetic labels.

In their winning proposal to the NSF, the team explains that the intended impact of this work is to help computer engineers develop new ways to think about the design of solid state machines, and may influence other brain-related research.

“We hope that neuron scientists will be able to use this technology as a testbed for studying the human brain,” says Berdichevsky, who’s previous research has delved into the causes and solutions of epilepsy and other diseases.

“This research will study how to stabilize the living neural network such that a Spike Time Dependent Plasticity (STDP)-based programming protocol can imprint the desired synaptic strengths onto a living neural network,” says Yan. “Our team will also investigate how to strategically design and apply an STDP-based protocol to maximize programming throughput and optimize the convergence rate of the network states. And on the algorithm side, the proposed research will study data representation and training algorithms that take into account various constraints of the wetware system we are designing."

The team’s project is one of 18 cross-disciplinary projects to conduct innovative research on neural and cognitive systems, supported by NSF to advance the frontiers of foundational research in four focus areas: Neuroengineering and brain-inspired concepts and designs, individuality and variation, cognitive and neural processes in realistic, complex environments, and data-intensive neuroscience and cognitive science.

The projects will leverage advanced research within and across these focus areas to investigate how neural and cognitive systems interact with education, engineering and computer science, as part of the NSF Integrative Strategies for Understanding Neural and Cognitive Systems (NCS) program. The NCS program supports innovative, boundary-crossing efforts to push the frontiers of brain science.

About the team

Xiaochen Guo, a 2018 NSF CAREER Award recipient, conducts research that leverages emerging technologies to build energy-efficient processors, memory systems, and accelerators. She is a recipient of the IBM Ph.D. Fellowship, P. C. Rossin Assistant Professorship, and the Lawrence Berkeley National Laboratory Computing Sciences Research Pathways Fellowship. She has published her work in Proceedings of the International Symposium on Computer Architecture, Proceedings of the International Symposium on Microarchitecture, Institute of Electrical and Electronics Engineers (IEEE) Transactions on Very Large Scale Integration Systems, and IEEE Transactions on Computers. Guo earned her doctorate and master’s degrees in electrical and computer engineering from the University of Rochester in 2011 and 2015, respectively, and her bachelor’s in computer science and engineering from Beihang University.

Yevgeny Berdichevsky focuses on applying bioMEMS/microfluidic technology to neuroscience. Current projects focus on 3 areas: development of culture and recording technology for high throughput drug screens; study of signaling pathways in post-traumatic epilepsy, using high-throughput recording chips and molecular/proteomic approaches; and, use of microstructures/microfluidics to study the relationship between neural circuit architecture and function. Before joining Lehigh in 2012, he served as a postdoctoral fellow at the Harvard Medical School and Massachusetts General Hospital. He has published articles in journals such as Journal of Neuroscience, Advanced Materials, and Lab on a Chip. He has presented his work at conferences across the country and holds a U.S. patent. He is a member of the Institute of Electrical and Electronics Engineers, IEEE Engineering in Medicine and Biology Society, Society for Neuroscience, and the American Epilepsy Society.

Zhiyuan Yan focuses on research in error control coding, communication theory, signal processing,  VLSI design and implementation of communication and signal processing systems, and has published over 100 technical papers in refereed journals and conference proceedings. He is currently an associate editor of the Journal of Signal Processing Systems, and was a senior area editor of the IEEE Transactions on Signal Processing from 2011 to 2015 and an associate editor of the IEEE Communications Letters from 2008 to 2012 and IEEE Access from 2013 to 2018. He was a Guest Co-Editor for a special issue of the Journal of Electrical and Computer Engineering on Implementations of Signal Processing Algorithms for OFDM Systems and several special issues of Journal of Signal Processing Systems. He is a member of the Circuits and Systems for Communications (CASCOM) and VLSI Systems and Application (VSA) Technical Committees of the IEEE Circuits and Systems Society. He was the chair of the Design and Implementation of Signal Processing Systems (DISPS) Technical Committee of the IEEE Signal Processing Society. He is a member of Tau Beta Pi, Sigma Xi, and Phi Kappa Phi honor societies and a Senior Member of IEEE. He was a recipient of the US National Science Foundation CAREER Award in 2011.

Running image recognition on living neuron testbed

An overview of running image recognition on living neuron testbed (Image courtesy of Xiaochen Guo/Lehigh University)

Xiaochen Guo

Xiaochen Guo, Assistant Professor, Electrical and Computer Engineering at Lehigh University (Photo by Ryan Hulvat/Lehigh University)

Zhiyuan Yan

Zhiyuan Yan, Associate Professor, Electrical and Computer Engineering at Lehigh University (Photo by Douglas Benedict/Lehigh University)

Yevgeny Berdichevsky

Yevgeny Berdichevsky, Associate Professor, Bioengineering at Lehigh University (Photo by Ryan Hulvat/Lehigh University)