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.

中文翻译：

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映射复杂动力系统的时空激活函数

大多数现实世界都由复杂而混乱的动力系统控制。所有这些动态系统都给使用神经网络建模带来了挑战。目前，作为循环神经网络子集的储层计算被积极用于模拟复杂的动力系统。在这项工作中，提出了一个二维激活函数，其中包括一个额外的时间项，以在其输出上赋予动态行为。包含时间项改变了激活函数的基本性质，它提供了在不依赖循环神经网络的情况下捕获时间序列数据的复杂动态的能力。