Adaptive Neurally-Inspired Computing: Models, Algorithms, and Silicon-Based Architectures

University Of Washington, Seattle WA

Investigators

Abstract

EIA-0130705 -University of Washington-Guang R. Gao-Adaptive Neurally-Inspired Computing: Models, Algorithms, and Silicon-Based Architectures Rapid advances in silicon technology over the past few decades have allowed digital devices to achieve ultra-high speeds in numerical computation. However, a majority of these devices are prone to catastrophic failure when confronted with circumstances unforeseen at programming time. Endowing such devices with the ability to adapt and learn from experience is rapidly becoming a problem of fundamental importance in information technology. We propose a new approach to solving this problem: building information technology systems based on neurobiological computation and learning. We intend to achieve this goal by developing computational models of plasticity and information processing in neurons and networks of neurons in selected sensory and motor areas of the brain; testing these models and their corresponding algorithms in software-based simulations, and designing real-time implementations of these algorithms in silicon using synapse transistors and field-programmable learning arrays (FPLAs). We expect our research to provide a better understanding of computation within neuronal networks, and to lead to a new generation of adaptive neuromorphic devices that could be used for a variety of information technology applications, ranging from signal processing and pattern recognition to ubiquitous computing and robotic control.