Why brains don't need Gaussian math to predict the world

The brain performs perceptual inference by minimizing prediction error, but existing mathematical models assume neurons compute with bell-curve statistics—limiting and biologically implausible. Using a broader class of probability distributions, researchers show how brains could implement this inference while exhibiting realistic nonlinear responses and avoiding impossible negative firing rates. The framework connects theory to biological plausibility through local learning rules neurons could actually use.