Generative network explains category formation in Alzheimer patients

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

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 however 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 (AD) 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.

Original languageEnglish
Pages64-68
Number of pages5
Publication statusPublished - Dec 1 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period7/10/997/16/99

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Aszalos, P., Keri, S., Kovacs, G., Benedek, G., Janka, Z., & Lorincz, A. (1999). Generative network explains category formation in Alzheimer patients. 64-68. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .