An application of formal concept analysis to semantic neural decoding.

Endres, Dominik, Foldiak, Peter and Priss, Uta (2009) An application of formal concept analysis to semantic neural decoding. Annals of Mathematics and Artificial Intelligence, 57 (3/4). pp. 233-248. ISSN 1012-2443

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This paper proposes a novel application of Formal Concept Analysis (FCA) to neural decoding: the semantic relationships between the neural representations of large sets of stimuli are explored using concept lattices. In particular, the effects of neural code sparsity are modelled using the lattices. An exact Bayesian approach is employed to construct the formal context needed by FCA. This method is explained using an example of neurophysiological data from the high-level visual cortical area STSa. Prominent features of the resulting concept lattices are discussed, including indications for hierarchical face representation and a product-of-experts code in real neurons. The robustness of these features is illustrated by studying the effects of scaling the attributes

Item Type: Article
Print ISSN: 1012-2443
Uncontrolled Keywords: formal concept analysis; neural coding; decoding; semantic; sparse coding; Bayesian classification;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods > 006.3 Artificial intelligence
Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Item ID: 3823
Depositing User: Computing Research
Date Deposited: 31 Aug 2010 15:16
Last Modified: 18 Dec 2012 12:11

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