NCS-FO: Biomimetic membrane networks as adaptable neuromorphic computation circuits

NCS-FO: Biomimetic membrane networks as adaptable neuromorphic circuits

Garrett S. Rose (PI) and C. Patrick Collier (co-PI)

University of Tennessee, Knoxville

Proposal Objectives:

Using biologically-inspired approach that better mimics the architecture and signaling currency of biological neural networks and to offer untapped potential for new forms of multifunctional neuromorphic behaviors.

Nontechnical Abstract:

Neuromorphic computing as a field comprises computational hardware engineered to mimic the behavior of the mammalian brain. Such systems are well suited to perform applications that are often simple for a biological brain but prove to be difficult for classical artificial computers for example, recognizing human faces at most any angle. While many neuromorphic architectures have been studied over the years, implementations based purely on traditional silicon-based circuitry fail to mimic the basic properties of biological neural networks, and, possibly as a result, they require far more power to achieve similar computational capability. To address this limitation and to uncover new insights into the role of properties such as tunable ion transport and synapse-neuron organization on complex computation in the brain, this project considers a new class of adaptable brain-inspired circuits comprised of biomimetic membranes with reconfigurable transport properties that mimic the variable weighting found in real synapses and solid-state artificial neurons that communicate via these synapses.

Technical Abstract:

This project delineates significantly from prior electronic neuromorphic hardware and digital software systems. Specifically, in this work the PI investigates: 1) how molecular transport through biomimetic membranes can be gated using physical stimuli to reproducibly vary synaptic weights in artificial synaptic mimics; 2) learn how to integrate membrane-based synaptic mimics with solid-state (i.e. transistor based) neural circuitry to develop hybrid devices that exhibit controllable and reproducible plasticity; and 3) utilize network modeling techniques to predict how variable synapse weighting within multi-neuron architectures affect collective sensing and learning functionality. This high-risk, high-reward project features a comprehensive integration plan across research and educational activities such that the results from previous work inform many, including neuroscientists studying complexity in the brain, engineers developing neuromorphic computation devices and brain-hardware interfaces, and social scientists in understanding how continuously-variable signaling pathways aid in learning, individuality, and group behavior. The broader impacts of the proposed work are transformative in nature and of interest to multiple fields of science, engineering, and medicine. By tapping into environmentally friendly, easily configurable soft materials and emulating nature's design, the PI seeks to advance computational strategies past Exascale, thus setting the stage for the next generation of low-power autonomic computation, distributed sensing, and information storage. Our approach also contributes to advancing brain-inspired computing systems, energy-efficient circuitry, and synthetic devices capable of communicating with live tissues, including multifunctional medical implants, drug-delivery devices, systems for disease monitoring and treatment, and technologies that can assist the brain in learning and cognition. The modeling and simulation aspects will also generate new open-source software that will allow students and researchers to explore the characteristics of network arrangement and synapse plasticity on network functionality. The educational impact leverages the fact that this project interfaces topics in engineering, biology, physics, and chemistry; PhD students involved in this work will receive exclusive scientific training to prepare them for making contributions in multiple fields. These activities also support broader impacts by generating interest in STEM, increasing participation of underrepresented groups, and expanding engineering curricula.