This chapter explores applications of slow electronics for temporal pattern recognition through numerical simulations of reservoir computing models. We demonstrate how the inherent time constants of slow electronic systems can be advantageously matched to tasks with similar temporal dynamics. Two primary applications are presented: handwriting authentication and blood glucose prediction. For handwriting authentication, we implement time series anomaly detection using both leaky integrator and leaky integrate-and-fire (LIF) neuron models, showing that reservoirs with longer time constants more effectively capture the unique temporal dynamics of individual handwriting patterns. For blood glucose prediction, we simulate a system using protonic devices as short-term memory elements, successfully forecasting glucose levels 10–30 min in advance–a critical timeframe for preventive healthcare interventions. The prediction accuracy remains comparable to conventional software implementations while offering the potential for ultra-low power operation. These simulations establish an important foundation for implementing slow electronics-based computing systems that effectively process human-scale temporal data while maintaining high performance within ultra-low power constraints.