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Training neural networks to perform different tasks is relevant acrossvarious disciplines. In particular, Recurrent Neural Networks (RNNs) are ofgreat interest in Computational Neuroscience. Open-source frameworks dedicatedto Machine Learning, such as Tensorflow and Keras have produced significantchanges in the development of technologies that we currently use. This workaims to make a significant contribution by comprehensively investigating anddescribing the implementation of a temporal processing task, specifically a3-bit Flip Flop memory. We delve into the entire modelling process,encompassing equations, task parametrization, and software development. Theobtained networks are meticulously analyzed to elucidate dynamics, aided by anarray of visualization and analysis tools. Moreover, the provided code isversatile enough to facilitate the modelling of diverse tasks and systems.Furthermore, we present how memory states can be efficiently stored in thevertices of a cube in the dimensionally reduced space, supplementing previousresults with a distinct approach.

Exploring Flip Flop memories and beyond: training recurrent neural networks with key insights