Incorporating rapid neocortical learning of new schema-consistent information into complementary learning systems theory.

The complementary learning systems theory of the roles of hippocampus and neocortex (McClelland, McNaughton, it is when inconsistent new knowledge is acquired quickly that catastrophic interference ensues. Several important features of the findings of Tse et al. (2007, 2011) are captured in these simulations, indicating that the neural network model used in McClelland et al. has characteristics in common with neocortical learning mechanisms. An additional simulation generalizes beyond the network model previously used, showing how the rate of change of cortical connections can depend on prior knowledge in an arguably more biologically plausible network architecture. In sum, the findings of Tse et al. are fully consistent with the idea that hippocampus and neocortex are complementary learning systems. Taken together, these findings and the simulations reported here advance our knowledge by bringing out the role of consistency of new experience with existing knowledge and demonstrating that the rate of change of connections in real and artificial neural networks can be strongly prior-knowledge dependent.