Research Output

An application of formal concept analysis to semantic neural decoding.

  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

  • Type:

    Article

  • Date:

    30 November 2009

  • Publication Status:

    Published

  • Publisher

    ACM

  • DOI:

    10.1007/s10472-010-9196-8

  • ISSN:

    1012-2443

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

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. doi:10.1007/s10472-010-9196-8. ISSN 1012-2443

Authors

Keywords

formal concept analysis; neural coding; decoding; semantic; sparse coding; Bayesian classification;

Available Documents