Machine learning with artificial neural networks (ANNs), provides solutions for the growing complexity of modern communication systems. This complexity, however, increases power consumption, making the systems energy-intensive. Spiking neural networks (SNNs) represent a novel generation of neural networks inspired by the highly efficient human brain. By emulating its event-driven and energy-efficient mechanisms, SNNs enable low-power, real-time signal processing. They differ from ANNs in two key ways: they exhibit inherent temporal dynamics and process and transmit information as short binary signals called spikes. Despite their promise, major challenges remain, e.g., identifying optimal learning rules and effective neural encoding. This thesis investigates the design of SNN-based receivers for nonlinear time-invariant frequency-selective channels. Backpropagation through time with surrogate gradients is identified as a promising update rule and the novel quantization encoding (QE) as promising neural encoding. Given the model of the intensity modulation with direct detection link, we compare two different receiver architectures based on equalization performance and spike count. Using decision feedback and QE achieves both strong performance and low spike counts. Notably, SNN-based receivers significantly outperform ANN-based counterparts. We furthermore introduce policy gradient-based update (PGU), an reinforcement learning-based update algorithm that requires no backpropagation. Using PGU, encoding parameters are optimized, drastically reducing runtime, complexity, and spikes per inference while maintaining performance. This thesis contributes a successful design and optimization framework for SNN-based receivers. By addressing key challenges in SNN optimization, it facilitates future advances in the design and deployment of energy-efficient SNN receivers.