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Liquid State Machines (LSMs) are computing reservoirs composed of recurrently connected Spiking Neural Networks which have attracted research interest for their modeling capacity of biological structures and as promising pattern recognition tools suitable for their implementation in neuromorphic processors, benefited from the modest use of computing resources in their training process. However, it has been difficult to optimize LSMs for solving complex tasks such as event-based computer visiondoi:10.3389/fnins.2022.819063 pmid:35360182 pmcid:PMC8964061 fatcat:zx74emjq2fawzghjrqib4g4tg4