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

arXiv: https://arxiv.org/abs/1712.02449 · HTML: https://mlsanity.com/paper/1712.02449/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications](https://mlsanity.com/paper/2306.04539/) (2023)
- [Multimodal Fusion Interactions: A Study of Human and Automatic Quantification](https://mlsanity.com/paper/2306.04125/) (2023)
- [Accurate Estimation of Mutual Information in High Dimensional Data](https://mlsanity.com/paper/2506.00330/) (2025)

## Abstract (arXiv)

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(S;R;C), that quantifies how much of the sensory information carried by a neural response R is used for behavior during perceptual discrimination tasks. Building on the Partial Information Decomposition framework, we define III(S;R;C) as the part of the mutual information between the stimulus S and the response R that also informs the consequent behavioral choice C. We compute III(S;R;C) 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.