What you need to know to train recurrent neural networks to make Flip Flops memories and more

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.