  While a lot of analysis has been carried to demonstrate linguistic knowledge
captured by the representations learned within deep NLP models, very little
attention has been paid towards individual neurons.We carry outa neuron-level
analysis using core linguistic tasks of predicting morphology, syntax and
semantics, on pre-trained language models, with questions like: i) do
individual neurons in pre-trained models capture linguistic information? ii)
which parts of the network learn more about certain linguistic phenomena? iii)
how distributed or focused is the information? and iv) how do various
architectures differ in learning these properties? We found small subsets of
neurons to predict linguistic tasks, with lower level tasks (such as
morphology) localized in fewer neurons, compared to higher level task of
predicting syntax. Our study also reveals interesting cross architectural
comparisons. For example, we found neurons in XLNet to be more localized and
disjoint when predicting properties compared to BERT and others, where they are
more distributed and coupled.
