Predictive coding is a leading theory of how the brain performs probabilistic inference. However, there are a number of distinct algorithms which are described by the term "predictive coding". This article provides a concise review of these different predictive coding algorithms, highlighting their similarities and differences. Five algorithms are covered: linear predictive coding which has a long and influential history in the signal processing literature; the first neuroscience-related application of predictive coding to explaining the function of the retina; and three versions of predictive coding that have been proposed to model cortical function. While all these algorithms aim to fit a generative model to sensory data, they differ in the type of generative model they employ, in the process used to optimise the fit between the model and sensory data, and in the way that they are related to neurobiology.

Two developmental disorders, autism and Williams syndrome, are both commonly described as having difficulties in integrating perceptual features, i.e. binding spatially separate elements into a whole. It is already known that healthy adults and infants display electroencephalographic (EEG) gamma-band bursts (around 40 Hz) when the brain is required to achieve such binding. Here we explore gamma-band EEG in autism and Williams Syndrome and demonstrate differential abnormalities in the two phenotypes. We show that despite putative processing similarities at the cognitive level, binding in Williams syndrome and autism can be dissociated at the neurophysiological level by different abnormalities in underlying brain oscillatory activity. Our study is the first to identify that binding-related gamma EEG can be disordered in humans.

To be published in Behavioral and Brain Sciences (in press) Cambridge University Press 2007 Below is the unedited précis of a book that is being accorded BBS multiple book review. This preprint has been prepared for potential commentators who wish to nominate themselves for formal commentary invitation. Please do not write a commentary unless you receive a formal invitation. Invited commentators will receive full instructions. Commentary must be based on the book.*

Attention acts, through cortical feedback pathways, to enhance the response of cells encoding expected or predicted information. Such observations are inconsistent with the predictive coding theory of cortical function which proposes that feedback acts to suppress information predicted by higher-level cortical regions. Despite this discrepancy, this article demonstrates that the predictive coding model can be used to simulate a number of the effects of attention. This is achieved via a simple mathematical rearrangement of the predictive coding model, which allows it to be interpreted as a form of biased competition model. Nonlinear extensions to the model are proposed that enable it to explain a wider range of data.