Training neural networks to perform different tasks is relevant across various disciplines beyond Machine Learning. In particular, Recurrent Neural Networks (RNNs) are of great interest to different scientific communities. Open-source frameworks dedicated to Machine Learning, such as Tensorflow [1] and Keras [2] have produced significant changes in the development of technologies that we currently use. One relevant problem that can be approached with them is how to build the models to study dynamical systems and the brain. Specifically, how to extract the relevant information to answer the scientific questions of interest. The purpose of the present work is to contribute to this aim by analyzing a temporal processing task, in this case, a 3-bit Flip Flop memory. The modelling procedure in every step is shown: from equations to the software development. The networks obtained were analyzed to describe the dynamics and to show different visualization and analysis tools. The code developed in this premier is also provided to be used for modelling other tasks or systems.

翻译：训练神经网络执行不同任务的相关性已超越机器学习领域，涉及多个学科。其中，循环神经网络（RNN）尤其受到不同科学群体的广泛关注。以 Tensorflow [1] 和 Keras [2] 为代表的机器学习开源框架，已显著改变了当前技术的开发模式。这些框架可处理的重要问题之一，是如何构建用于研究动力系统与大脑的模型，具体而言，即如何提取相关科学问题所需的关键信息。本文旨在通过分析时序处理任务（具体为3位触发器记忆任务）为此目标做出贡献。我们展示了从方程推导到软件实现的完整建模流程，并对所得网络进行动力学描述与分析，同时提供多种可视化及分析工具。本文所开发的入门级代码亦可直接用于建模其他任务或系统。