An application of formal concept analysis to semantic neural decoding

  • Dominik Maria Endres
  • Peter Földiák
  • Uta Priss


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.


Formal concept analysis FCA Neural code Sparse coding High-level vision STS Bayesian classification Semantic Neural decoding 

Mathematics Subject Classifications (2010)

06 92 62 


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Dominik Maria Endres
    • 1
  • Peter Földiák
    • 2
  • Uta Priss
    • 3
  1. 1.Section for Theoretical Sensomotorics, Department of Cognitive Neurology, Hertie Institute for Clinical Brain Research and Center for Integrative NeuroscienceUniversity Clinic TübingenTübingenGermany
  2. 2.School of PsychologyUniversity of St AndrewsScotlandUK
  3. 3.School of ComputingEdinburgh Napier UniversityEdinburghUK

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