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The neural code and knowledge representation: a bridge too far?

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The question of how activity patterns of neurons represent objects in the world has so far mainly been addressed by asking the question of how the identity of stimuli can be decoded from the neural signals. However, an even more interesting question is how the structure of the relationships between items and categories can be represented in a sparse and explicit neural code. The duality between “sets of objects” and “sets of features” have been extensively studied by the field of lattice theory called “Formal Concept Analysis” (FCA). FCA is proposed as a useful method for analysing a neural code because of this explicit structure. Examples from monkey inferotemporal cortex will be presented. Some possible practical computational applications for categorisation and semantic memory systems will be discussed.

This talk is part of the Craik Club series.

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