Integrating Visual Perception With Decision Making in Neuromorphic Fault-Tolerant Quadruplet-Spike Learning Framework
Shuangming Yang, Haowen Wang, Yanwei Pang, Yaochu Jin, B. Linares-Barranco
Abstract
The brain possesses the remarkable ability to seamlessly integrate perception with decision making within a dynamically changing environment in a fault-tolerant, end-to-end manner. This extraordinary capability offers a compelling solution for brain-inspired intelligence, replete with the advantages of end-to-end decision making: robustness, high accuracy, real-time responsiveness, autonomous intelligence, and a high degree of biological plausibility. Neuromorphic computing stands as a promising avenue for brain-inspired intelligence through the harmonious co-design of algorithms and hardware, aimed at unlocking full potential. This article introduces a comprehensive neuromorphic computing framework for end-to-end intelligence. It introduces the quadruplet spike-timing-dependent plasticity, which serves as a cornerstone for perceptual to decision-making tasks. A fault-tolerant neuromorphic routing strategy is presented to fortify the framework’s robustness. Empirical results underscore its impressive attributes with high accuracy, robustness, fault tolerance, and minimal computational latency when orchestrating end-to-end decision-making alongside visual perception. This study marks a pioneering effort in unified, fault-tolerant neuromorphic framework engineered for brain-inspired end-to-end intelligent tasks, merging visual perception with adaptive decision making. Such an endeavor is profoundly meaningful, as it propels the development of artificial general intelligence, holding vast implications for the field’s advancement.