Skip to main content

Research Repository

Advanced Search

An application of formal concept analysis to semantic neural decoding.

Endres, Dominik; Foldiak, Peter; Priss, Uta

Authors

Dominik Endres

Peter Foldiak

Uta Priss



Abstract

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

Citation

Endres, D., Foldiak, P., & Priss, U. (2009). An application of formal concept analysis to semantic neural decoding. Annals of Mathematics and Artificial Intelligence, 57, 233-248. https://doi.org/10.1007/s10472-010-9196-8

Journal Article Type Article
Publication Date 2009-12
Deposit Date Aug 31, 2010
Print ISSN 1012-2443
Publisher BMC
Peer Reviewed Peer Reviewed
Volume 57
Pages 233-248
DOI https://doi.org/10.1007/s10472-010-9196-8
Keywords formal concept analysis; neural coding; decoding; semantic; sparse coding; Bayesian classification;
Public URL http://researchrepository.napier.ac.uk/id/eprint/3823
Publisher URL http://dx.doi.org/10.1007/s10472-010-9196-8


You might also like



Downloadable Citations