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 Gao, 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.