Elements of information theory
Elements of information theory
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Learning linear, sparse, factorial codes
Learning linear, sparse, factorial codes
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Formal Concept Analysis: Foundations and Applications (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Formal concept analysis in information science
Annual Review of Information Science and Technology
Understanding the semantic structure of human fMRI brain recordings with formal concept analysis
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
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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.