Brief

Researchers have published a study comparing how different learning rules in artificial neural networks align with visual processing in both humans and macaques. The study found that early visual cortex alignment was conserved across species, with artificial neural networks showing higher correlation with macaque electrophysiology data than with human fMRI data. However, at higher visual areas like the IT cortex, the alignment rankings of learning rules diverged significantly between species, suggesting that model capacity and training data play a larger role than the specific learning rule in these areas. AI

Researchers have developed a new method for predictive coding networks that addresses their historical limitations in speed and performance with increasing depth. By treating these networks as deep hierarchical Gaussian filters and incorporating precision-weighted message passing, the new approach allows for dynamic uncertainty estimates and Hebbian-compatible updates. This closed-form variational inference method enables networks to learn activations, weights, and precisions simultaneously without iterative relaxation or global error signals, achieving performance comparable to backpropagation on benchmark tasks. AI