@article{Wang2023, 
author = {Lei Wang and Sha-Sha Guo and Lian-Hua Qu and Shuo Tian and Wei-Xia Xu},
title = {M-LSM: An Improved Multi-Liquid State Machine for Event-Based Vision Recognition},
year = {2023},
journal = {Journal of Computer Science and Technology},
volume = {38},
number = {6},
pages = {1288-1299},
keywords = {liquid state machine, bio-inspired learning, classification, event-based vision},
url = {https://www.sciopen.com/article/10.1007/s11390-021-1326-8},
doi = {10.1007/s11390-021-1326-8},
abstract = {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. In this paper, we propose an improved multi-liquid state machine (M-LSM) method for high-performance vision recognition. Specifically, we introduce two methods, namely multi-state fusion and multi-liquid search, to optimize the liquid state machine (LSM). Multi-state 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). Our M-LSM is evaluated on two event-based datasets and demonstrates state-of-the-art recognition performance with superior advantages on network complexity and training cost.}
}