Our work about spin-type neuromorphic computing entitled “Physical reservoir computing using magnetic skyrmion memristor and spin torque nano-oscillator” is published in Applied Physics Letters [Wencong Jiang*, Lina Chen*, Kaiyuan Zhou, Liyuan Li, Qingwei Fu, Youwei Du, and R. H. Liu*, Appl. Phys. Lett. 115, 192403 (2019)]. Congratuations to Mr. Wencong Jiang and Dr. Lina Chen. In this work, we  numerically modeled two physical RC systems based on one single magnetic skyrmion memristor (MSM) and 24 spin-torque nano-oscillators (STNOs) to process image classification task and nonlinear dynamic system prediction, respectively. Based on the nonlinear responses of the MSM and STNO with current pulse stimulation, our results demonstrate that the MSM-based RC system exhibits excellent performance on image classification, while the STNO-based RC system does well in solving the complex unknown nonlinear dynamic problems, e.g., a second-order nonlinear dynamic system and NARMA10. Our result and analysis of the current-dependent nonlinear dynamic properties of the MSM and STNO provide the strategy to optimize the experimental parameters in building the better spintronic-based brainlike devices for machine learning based computing.