Critics attack this new productivity argument from various angles, focusing mainly on the empirical case studies adduced by Gallistel and King. Peter Dayan (2009), John Donahoe (2010), and Christopher Mole (2014) argue that biologically plausible neural network models can accommodate at least some of the case studies. Dayan and Donahoe argue that empirically adequate neural network models can dispense with anything resembling read/write memory. Mole argues that, in certain cases, empirically adequate neural network models can implement the read/write memory mechanisms posited by Gallistel and King. Debate on these fundamental issues seems poised to continue well into the future.