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:除机器学习外,用于执行不同任务的培训神经网络在不同学科中都具有相关性,特别是,经常神经网络(RNN)对不同的科学界非常感兴趣。专门用于机器学习的开放源码框架,如Tensorflow[1]和Keras[2],已经对我们目前使用的技术开发产生了重大变化。可以与它们探讨的一个相关问题是如何建立研究动态系统和大脑的模型。具体地说,如何提取相关信息以解答感兴趣的科学问题。目前工作的目的是通过分析一个时间处理任务来推动这一目标,在此情况下,分析一个3比特的Flip Flop内存。每个步骤的建模程序都显示:从方程式到软件开发。所获得的网络经过分析,以描述动态并展示不同的视觉和分析工具。在这一首期开发的代码也用于模拟其他任务或系统。