Reservoir computing is a promising approach for harnessing the computational power of recurrent neural networks while dramatically simplifying training. This paper investigates the application of integrate-and-fire neurons within reservoir computing frameworks for two distinct tasks: capturing chaotic dynamics of the H\'enon map and forecasting the Mackey-Glass time series. Integrate-and-fire neurons can be implemented in low-power neuromorphic architectures such as Intel Loihi. We explore the impact of network topologies created through random interactions on the reservoir's performance. Our study reveals task-specific variations in network effectiveness, highlighting the importance of tailored architectures for distinct computational tasks. To identify optimal network configurations, we employ a meta-learning approach combined with simulated annealing. This method efficiently explores the space of possible network structures, identifying architectures that excel in different scenarios. The resulting networks demonstrate a range of behaviors, showcasing how inherent architectural features influence task-specific capabilities. We study the reservoir computing performance using a custom integrate-and-fire code, Intel's Lava neuromorphic computing software framework, and via an on-chip implementation in Loihi. We conclude with an analysis of the energy performance of the Loihi architecture.

翻译：储备池计算是一种极具前景的方法，它能够利用循环神经网络的计算能力，同时极大地简化训练过程。本文研究了在储备池计算框架中应用积分发放神经元来完成两项不同的任务：捕捉Hénon映射的混沌动力学特性以及预测Mackey-Glass时间序列。积分发放神经元可在低功耗神经形态架构（如Intel Loihi）中实现。我们探究了通过随机交互生成的网络拓扑结构对储备池性能的影响。我们的研究揭示了网络有效性在不同任务间的差异，突显了针对不同计算任务定制架构的重要性。为确定最优网络配置，我们采用了元学习方法与模拟退火算法相结合的策略。该方法能高效探索可能的网络结构空间，识别在不同场景下表现优异的架构。由此产生的网络展示了一系列行为，揭示了其固有的架构特征如何影响任务特定的能力。我们使用自定义的积分发放神经元代码、Intel的Lava神经形态计算软件框架，以及通过Loihi芯片上的实现，研究了储备池计算的性能。最后，我们对Loihi架构的能耗性能进行了分析。