An application of formal concept analysis to semantic neural decoding
| Autor: | Dominik Endres, Peter Földiák, Uta Priss |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Theoretical computer science
Quantitative Biology::Neurons and Cognition Computer science business.industry Applied Mathematics Context (language use) Naive Bayes classifier Artificial Intelligence Robustness (computer science) Formal concept analysis Lattice Miner Artificial intelligence Neural coding Representation (mathematics) business Neural decoding |
| Zdroj: | Annals of Mathematics and Artificial Intelligence. 57:233-248 |
| ISSN: | 1573-7470 1012-2443 |
| DOI: | 10.1007/s10472-010-9196-8 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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