This work presents a spectrum of approximations of the softmax and squash functions used in Capsule Neural Networks, building upon previous related research as well as introducing new ideas and solutions. The main focus of the work is to explore approximate computing techniques for softmax and squash at the algorithmic level, in order to demonstrate a trade-off between complexity cost of the hardware implementation of the functions and inference accuracy of the Capsule Network. In particular, area usage and power consumption are considered as hardware complexity metrics and the inference pass is performed with different Capsule Network models on different benchmark image datasets.