Localist models are compatible with information measures, sparseness indices, and complementary-learning systems in the brain

ABSTRACT In this paper, I express continued support for localist modelling in psychology and critically evaluate previous studies that have sought to weaken the localist case in favour of models with thoroughgoing distributed representation. I question claims that information measures and sparseness indices derived from single-cell recording data are supportive of distributed representation and show that the patterns observed in those data can be reproduced from simulations of a model that is known to be localist. I also set out some logical objections to the complementary-learning hypothesis, particularly in as much as it is used to justify thoroughgoing distributed models of the cortex.

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