35 citations · 140 across the 13 of their papers we have counts for
6 papers · 1 filter
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
Tommaso Salvatori, Yuhang Song, Yordan Yordanov +6
Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstabl…
A Theoretical Framework for Inference and Learning in Predictive Coding Networks
Beren Millidge, Yuhang Song, Tommaso Salvatori +2
Predictive coding (PC) is an influential theory in computational neuroscience, which argues that the cortex forms unsupervised world models by implementing a hierarchical process o…
Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning
Beren Millidge, Yuhang Song, Tommaso Salvatori +2
How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients tha…
Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?
Beren Millidge, Tommaso Salvatori, Yuhang Song +2
The backpropagation of error algorithm used to train deep neural networks has been fundamental to the successes of deep learning. However, it requires sequential backward updates a…
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
Beren Millidge, Tommaso Salvatori, Yuhang Song +2
A large number of neural network models of associative memory have been proposed in the literature. These include the classical Hopfield networks (HNs), sparse distributed memories…
Learning on Arbitrary Graph Topologies via Predictive Coding
Tommaso Salvatori, Luca Pinchetti, Beren Millidge +4
Training with backpropagation (BP) in standard deep learning consists of two main steps: a forward pass that maps a data point to its prediction, and a backward pass that propagate…