The Variability of Representations in Mice and Humans Changes with Learning, Engagement, and Attention

Published: 29 Nov 2023, Last Modified: 29 Nov 2023NeurReps 2023 PosterEveryoneRevisionsBibTeX
Submission Track: Extended Abstract
Keywords: neural representation, trial-to-trial variability, noise correlations, generalization, mice, humans
TL;DR: We measure the geometry of neural representation variability under increasing stimulus familiarity, task engagement and selective attention, and find that the brain can adapt its variability based on task demands
Abstract: In responding to a visual stimulus, cortical neurons exhibit a high degree of variability, and this variability can be correlated across neurons. In this study, we use recordings from both mice and humans to systematically characterize how the variability in the representation of visual stimuli changes with learning, engagement and attention. We observe that in mice, familiarization with a set of images over many weeks reduces the variability of responses, but does not change its shape. Further, switching from passive to active task engagement changes the overall shape by shrinking the neural variability only along the task-relevant direction, leading to a higher signal-to-noise ratio. In a selective attention task in humans wherein multiple distributions are compared, a higher signal-to-noise ratio is obtained via a different mechanism, by mainly increasing the signal of the attended category. These findings show that representation variability can be adjusted with task needs. A potential speculative role for variability, consistent with these findings, is that it helps generalization.
Submission Number: 51