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 …

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 …

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 …

One major criticism of deep learning centers around the biological implausibility of the credit
assignment schema used for learning--backpropagation of errors. This implausibility …

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

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 …

Neuroscience-inspired models, such as predictive coding, have the potential to play an
important role in the future of machine intelligence. However, they are not yet used in …

Predictive coding (PC) is an influential theory in computational neuroscience, which argues
that the cortex forms unsupervised world models by implementing a hierarchical process of …