学术报告第201859期

报告题目：Implementing Neuromorphic Reservoir Computing with Self-Assembled Memristive Switching Networks

报 告 人：Thomas H. LaBean（North Carolina State University）

报告时间：2018年10月29日14：00

报告地点：南一楼中311

Abstract:

Training and use of large nonlinear neural networks on conventional computer architectures is impeded by poor scalability and high energy penalties for sequential updates of neuron weights. Here we develop a low power and highly scalable computing paradigm of self-assembled neural network-like architectures using DNA origami, functional peptides, and inorganic components for fabrication of circuits with potential for real-time computing. We experimentally and theoretically examine circuits created by the molecular assembly of functional components capable of displaying complex, emergent electronic behaviors such as memristor based reservoir computing. Deterministic assembly at low nanometer length-scales followed by stochastic assembly at high nanoscale and up to micron scale should provide circuits with exploitable electronic behaviors. Theoretical work focuses particularly on modeling and simulation of device function and network structure/function in order to predict emergent electronic properties. Network architectures will follow neuromorphic principles and will result in trainable or learnable circuits with potential capabilities including memory, logic, and complex signal processing.

Short Biography:

Thomas H. LaBean is Professor of Materials Science and Engineering at North Carolina State University. He earned BS and PhD degrees in Biochemistry from the Honors College at Michigan State University and the University of Pennsylvania, respectively. He studied folding and assembly of arbitrary sequence proteins in graduate school, then moved to Duke University as a Biochemistry postdoc and studied de novo protein design. As a Research Professor in Computer Science, he worked on DNA-based molecular computation and self-assembling biomolecular nanostructures. He has been at North Carolina State University since 2011, and his current research involves self-assembling polypeptides and DNA nanostructures for molecular materials, bioinspired nanoelectronics fabrication, and nanomedicine.