An application of formal concept analysis to semantic neural decoding

Endres, Dominik Maria; Foldiak, Peter; Priss, Uta

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. M., Foldiak, P., & Priss, U. (2009). An application of formal concept analysis to semantic neural decoding. Annals of Mathematics and Artificial Intelligence, 57(3-4), 233-248. https://doi.org/10.1007/s10472-010-9196-8

• Journal Article Type: Article
• Online Publication Date: Jul 9, 2010
• Publication Date: 2009-12
• Deposit Date: Aug 31, 2010
• Journal: Annals of Mathematics and Artificial Intelligence
• Print ISSN: 1012-2443
• Electronic ISSN: 1573-7470
• Publisher: BMC
• Peer Reviewed: Peer Reviewed
• Volume: 57
• Issue: 3-4
• 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