Generative network explains category formation in Alzheimer patients

This paper presents a generative data reconstruction neural network model equipped with plastic lateral connections. The model is capable of capturing basic phenomena related to category formation. It explains category formation as an effect of cumulative memory traces at the level of lateral connectivity. The formed memory traces change network activity that is the basis of categorization according to the model. This change depends on the structure of the lateral connectivity and on the stimuli used in demonstrations. We argue that the model resolves the seemingly contradictory demonstrational results carried out with Alzheimer disease patients on category formation. We consider different stimulus sets and degraded lateral connectivity and show that the categorization probability can change from monotone to non-monotone functions depending on the sets.