  Most of the real world is governed by complex and chaotic dynamical systems.
All of these dynamical systems pose a challenge in modelling them using neural
networks. Currently, reservoir computing, which is a subset of recurrent neural
networks, is actively used to simulate complex dynamical systems. In this work,
a two dimensional activation function is proposed which includes an additional
temporal term to impart dynamic behaviour on its output. The inclusion of a
temporal term alters the fundamental nature of an activation function, it
provides capability to capture the complex dynamics of time series data without
relying on recurrent neural networks.
