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). 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). 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.

基于事件的计算由于其在效率和速度方面的内在优势，最近在视觉识别应用中引起了越来越多的研究兴趣。然而，现有的基于事件的视觉识别模型面临着网络复杂性大、训练成本昂贵等问题。在本文中，我们提出了一种改进的多液体状态机（M-LSM）方法，用于高性能视觉识别。具体来说，我们引入了两种方法，即多状态融合和多液体搜索来优化液体状态机（LSM）。通过在多个时间步对液态进行采样的多态融合可以保留更丰富的时空信息。我们采用网络架构搜索（NAS）来寻找多液体状态机的潜在最优架构。我们还通过无监督学习规则尖峰时序相关可塑性（STDP）来训练 M-LSM。我们的 M-LSM 在两个基于事件的数据集上进行评估，展示了最先进的识别性能，在网络复杂性和训练成本方面具有卓越优势。