1 citations · 1 across the 5 of their papers we have counts for
6 papers
PC: Scaling Predictive Coding to 100+ Layer Networks
Francesco Innocenti, El Mehdi Achour, Christopher L. Buckley
The biological implausibility of backpropagation (BP) has motivated many alternative, brain-inspired algorithms that attempt to rely only on local information, such as predictive c…
JPC: Flexible Inference for Predictive Coding Networks in JAX
Francesco Innocenti, Paul Kinghorn, Will Yun-Farmbrough +3
We introduce JPC, a JAX library for training neural networks with Predictive Coding. JPC provides a simple, fast and flexible interface to train a variety of PC networks (PCNs) inc…
Learning in Hybrid Active Inference Models
Poppy Collis, Ryan Singh, Paul F Kinghorn +1
An open problem in artificial intelligence is how systems can flexibly learn discrete abstractions that are useful for solving inherently continuous problems. Previous work in comp…
Hybrid Recurrent Models Support Emergent Descriptions for Hierarchical Planning and Control
Poppy Collis, Ryan Singh, Paul F Kinghorn +1
An open problem in artificial intelligence is how systems can flexibly learn discrete abstractions that are useful for solving inherently continuous problems. Previous work has dem…
Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?
Francesco Innocenti, El Mehdi Achour, Ryan Singh +1
Predictive coding (PC) is an energy-based learning algorithm that performs iterative inference over network activities before updating weights. Recent work suggests that PC can con…
Understanding Tool Discovery and Tool Innovation Using Active Inference
Poppy Collis, Paul F Kinghorn, Christopher L Buckley
The ability to invent new tools has been identified as an important facet of our ability as a species to problem solve in dynamic and novel environments. While the use of tools by…