  Training neural networks to perform different tasks is relevant across
various disciplines. In particular, Recurrent Neural Networks (RNNs) are of
great interest in Computational Neuroscience. Open-source frameworks dedicated
to Machine Learning, such as Tensorflow and Keras have produced significant
changes in the development of technologies that we currently use. This work
aims to make a significant contribution by comprehensively investigating and
describing the implementation of a temporal processing task, specifically a
3-bit Flip Flop memory. We delve into the entire modelling process,
encompassing equations, task parametrization, and software development. The
obtained networks are meticulously analyzed to elucidate dynamics, aided by an
array of visualization and analysis tools. Moreover, the provided code is
versatile enough to facilitate the modelling of diverse tasks and systems.
Furthermore, we present how memory states can be efficiently stored in the
vertices of a cube in the dimensionally reduced space, supplementing previous
results with a distinct approach.
