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

Determining how much of the sensory information carried by a neural code contributes to behavioral performance is key to understand sensory function and neural information flow. However, there are as yet no analytical tools to compute this information that lies at the intersection between sensory coding and behavioral readout. Here we develop a novel measure, termed the information-theoretic intersection information $\III(R)$, that quantifies how much sensory information carried by a neural response $R$ is also used for behavior during perceptual discrimination tasks. Building on the Partial Information Decomposition framework, we define $\III(R)$ as the mutual information between the presented stimulus $S$ and the consequent behavioral choice $C$ that can be extracted from $R$. We compute $\III(R)$ in the analysis of two experimental cortical datasets, to show how this measure can be used to compare quantitatively the contributions of spike timing and spike rates to task performance, and to identify brain areas or neural populations that specifically transform sensory information into choice.