Training neural networks to perform different tasks is relevant across various disciplines that go beyond Machine Learning. In particular, Recurrent Neural Networks (RNN) are of great interest to different scientific communities, for example, Computational Neuroscience research and Dynamical Systems among others. Open-source frameworks dedicated to Machine Learning such as Tensorflow and Keras has produced significant changes in the development of technologies that we currently use. One relevant problem that can be approached is how to build the models for the study of dynamical systems, and how to extract the relevant information to be able to answer the scientific questions of interest. The purpose of the present work is to contribute to this aim by using a temporal processing task, in this case, a 3-bit Flip Flop memory, to show the modeling procedure in every step: from equations to the software code using Tensorflow and Keras. The obtained networks are analyzed to describe the dynamics and to show different visualization and analysis tools. The code developed in this work is provided to be used as a base for model other systems.

翻译：用于执行不同任务的培训神经网络在机器学习以外的不同学科中具有相关性。特别是,经常性神经网络(RNN)对不同的科学界具有极大的兴趣,例如,计算神经科学研究和动态系统等。专门用于机器学习的开放源代码框架,如Tensorflow和Keras,在我们目前使用的技术开发方面产生了重大变化。可以处理的一个相关问题是如何建立动态系统研究模型,以及如何提取相关信息以回答感兴趣的科学问题。目前工作的目的是通过使用时间处理任务,即3位Flip Floop记忆,展示每个步骤的模型程序:从方程式到使用Tensorplow和Keras的软件代码。对所获得的网络进行了分析,以描述动态并展示不同的可视化和分析工具。这项工作所开发的代码将用作其他系统模型的基础。