The story of a single cell: Peeking into the semantics of spikes

Traditionally, the modeling of sensory neurons has focused on the characterization and/or the learning of input-output relations. Motivated by the view that different neurons impose different partitions on the stimulus space, we propose instead to learn the structure of the stimulus space, as imposed by the cell, by learning a cell specific distance function or kernel. Metaphorically speaking, this direction attempts to bypass the syntactic question of “how the cell speaks”, by focusing instead on the semantic and fundamental question of “what the cell says”.