Perceptual distinction in an unsupervised neural network: implications for theories of category-specific deficits

Gale, T.M., Peters, L., Frank, R. and Davey, N. (2000) Perceptual distinction in an unsupervised neural network: implications for theories of category-specific deficits. In: Procs of the 2nd Int ICSC Symposium on Neural Computation 2000 :. UNSPECIFIED.
Copy

There are many reports of patients who, after sustaining brain damage, exhibit a selective recognition deficit for certain categories of object. There has been much controversy as to whether this is informative about the neural organisation of knowledge in the human brain. In this paper we describe an unsupervised neural network model that is trained to process images from a variety of different object categories. Analysis of the unsupervised representations reveals some interesting distinctions between different classes of object. We contend that this model indicates a natural perceptual distinction between certain object categories, which may become exaggerated by the effects of human brain damage.


picture_as_pdf
900908.pdf
subject
Submitted Version

View Download

Atom BibTeX OpenURL ContextObject in Span OpenURL ContextObject Dublin Core MPEG-21 DIDL Data Cite XML EndNote HTML Citation METS MODS RIOXX2 XML Reference Manager Refer ASCII Citation
Export

Downloads