  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.
