Abstract
Data detailed on Liquid State Machines have been presented. According to news reporting originating in Changsha, People's Republic of China, by NewsRx journalists, research stated, "Event-based computation has recently gained increasing research interest for applications of vision recognition due to its intrinsic advantages on efficiency and speed. However, the existing event-based models for vision recognition are faced with several issues, such as large network complexity and expensive training cost." Funders for this research include National Natural Science Foundation of China (NSFC), Key Laboratory of Advanced Microprocessor Chips and System. The news reporters obtained a quote from the research from the National University of Defense Technology, "In this paper, we propose an improved multi-liquid state machine (M-LSM) method for highperformance vision recognition. Specifically, we introduce two methods, namely multi-state fusion and multi-liquid search, to optimize the liquid state machine (LSM). Multistate fusion by sampling the liquid state at multiple timesteps could reserve richer spatiotemporal information. We adapt network architecture search (NAS) to find the potential optimal architecture of the multi-liquid state machine. We also train the M-LSM through an unsupervised learning rule spike-timing dependent plasticity (STDP)."
NETL
NSTL
Springer Nature