Topic: covariance structure

Cortical neurons exhibit a high degree of trial-to-trial variability in response to repeated presentations of the same stimulus. We propose that this variability serves a useful computational purpose: allowing the brain to generalize from a small number of examples. To formalize this theory, we consider a Gaussian model of few-shot classification, wherein we observe a few samples of each class, and each observation has trial-to-trial variability. Our model leads to three predictions about the optimal neural response variability that minimizes generalization error: (i) the optimal variability m