hmmm... For many decades researchers and experts have stated that we really don't understand what the heck is going on inside these artificial neuron networds... ....Sounds like the decades long quest to understand how a network of ArtificialNeurons processes input data into useful output (for example: categorizing, deciding yes or no, or controlling the actuators of robots, etc.) ...has been cracked. That is, through conceptually understanding linguistics and concepts like 'tokens' and 'parsing', the researchers have been able to trace through the steps of what the FFNs are logically doing. [Regarding other commenters' comments: Two points to keep in mind.... 1) any trained ANN (in machine learning, as well as FFNs) can be reduced to a single mathematical formula to replicate the processing being done by these ArtificialNeuralNets. In other words, after training, the result can be reduced to a single bit of math that alone can be reused to replicate the trained net WITHOUT using the actual ANN anymore - using only the ANN for training in order to derive a definite mathematical result for future use. {See most graduate school level textbooks on machine learning for the details....} 2) ..."sparse" does not equal "unnecessary"; it sounds like what others have suggested, it's like a decision tree rather than complexes of connections between artificial neurons doing heaven only knows what logically....]

~jgroch