document: On the emergence of rules in neural networks A simple associationist neural network learns to factor abstract rules -LRB- i.e. , grammars -RRB- from sequences of arbitrary input symbols by inventing abstract representations that accommodate unseen symbol sets as well as unseen but similar grammars . The neural network is shown to have the ability to transfer grammatical knowledge to both new symbol vocabularies and new grammars . Analysis of the state-space shows that the network learns generalized abstract structures of the input and is not simply memorizing the input strings . These representations are context sensitive , hierarchical , and based on the state variable of the finite-state machines that the neural network has learned . Generalization to new symbol sets or grammars arises from the spatial nature of the internal representations used by the network , allowing new symbol sets to be encoded close to symbol sets that have already been learned in the hidden unit space of the network . The results are counter to the arguments that learning algorithms based on weight adaptation after each exemplar presentation -LRB- such as the long term potentiation found in the mammalian nervous system -RRB- can not in principle extract symbolic knowledge from positive examples as prescribed by prevailing human linguistic theory and evolutionary psychology
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