A class of brain-inspired recurrent algorithms known as reservoir computing (RC) networks reduces the computational complexity and cost of training machine-learning models by using random, fixed synaptic strengths. This article offers insights about a spiking reservoir network, the liquid state machine (LSM), the inner workings of the algorithm, the design metrics, and neuromorphic designs. The discussion extends to variations of the LSM that incorporate local plasticity mechanisms and hierarchy to improve performance and memory capacity.

中文翻译：

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Spiking Reservoir Networks：使用随机、固定突触强度的脑启发循环算法

一类受大脑启发的循环算法称为储层计算 (RC) 网络，通过使用随机、固定的突触强度降低了训练机器学习模型的计算复杂性和成本。本文提供了有关尖峰储层网络、液态机 (LSM)、算法的内部工作原理、设计指标和神经形态设计的见解。讨论扩展到 LSM 的变体，这些变体结合了局部可塑性机制和层次结构，以提高性能和内存容量。