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Determining how much of the sensory information carried by a neural codecontributes to behavioral performance is key to understand sensory function andneural information flow. However, there are as yet no analytical tools tocompute this information that lies at the intersection between sensory codingand behavioral readout. Here we develop a novel measure, termed theinformation-theoretic intersection information $I_{II}(S;R;C)$, that quantifieshow much of the sensory information carried by a neural response R is used forbehavior during perceptual discrimination tasks. Building on the PartialInformation Decomposition framework, we define $I_{II}(S;R;C)$ as the part ofthe mutual information between the stimulus S and the response R that alsoinforms the consequent behavioral choice C. We compute $I_{II}(S;R;C)$ in theanalysis of two experimental cortical datasets, to show how this measure can beused to compare quantitatively the contributions of spike timing and spikerates to task performance, and to identify brain areas or neural populationsthat specifically transform sensory information into choice.

Quantifying how much sensory information in a neural code is relevant for behavior