A detailed behavioral, videographic, and neural dataset on object recognition in mice

Abstract

Mice adeptly use their whiskers to touch, recognize, and learn about objects in their environment. This ability is enabled by computations performed by populations of neurons in the somatosensory cortex. To understand these computations, we trained mice to use their whiskers to recognize different shapes while we recorded activity in the barrel cortex, which processes whisker input. Here, we present a large dataset of high-speed video of the whiskers, along with rigorous tracking of the entire extent of multiple whiskers and every contact they made on the shape. We used spike sorting to identify individual neurons, which responded with precise timing to whisker contacts and motion. These data will be useful for understanding the behavioral strategies mice use to identify objects by touch, as well as the neuronal responses that mediate those strategies. More generally, our carefully curated labeled data could be used to develop new computer vision algorithms for tracking body posture, or for extracting responses of individual neurons from large-scale neural recordings.