Dominik Endres
An application of formal concept analysis to semantic neural decoding.
Endres, Dominik; Foldiak, Peter; Priss, Uta
Authors
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 |
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