Recent breakthroughs in brain-inspired computing promise to address a wide range of
problems from security to healthcare. However, the current strategy of implementing artificial …

Since their inception more than 50 years ago, Brain-Computer Interfaces (BCIs) have held
promise to compensate for functions lost by people with disabilities through allowing direct …

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The
majority of results in AI thus far have been achieved using deep neural networks trained with …

In the materials science domain, the accurate prediction of the yield strength of metallic
compositions has often resulted in extensive experimental endeavors, leading to …

One of the most fundamental laws of physics is the principle of least action. Motivated by its
predictive power, we introduce a neuronal least-action principle for cortical processing of …

It has been suggested that the brain employs probabilistic generative models to optimally
interpret sensory information. This hypothesis has been formalised in distinct frameworks …

Abstract Models of sensory processing and learning in the cortex need to efficiently assign
credit to synapses in all areas. In deep learning, a known solution is error backpropagation …

In this work, we tackle the problems of efficiency and scalability for predictive coding
networks in machine learning. To do so, we first propose a library called PCX, whose focus …

Backpropagation has rapidly become the workhorse credit assignment algorithm for modern
deep learning methods. Recently, modified forms of predictive coding (PC), an algorithm …

Backpropagation (BP) is the most successful and widely used algorithm in deep learning.
However, the computations required by BP are challenging to reconcile with known …