Spatial context in images induces perceptual phenomena associated with salience and
modulates the responses of neurons in primary visual cortex (V1). However, the …

A method is presented for learning the reciprocal feedforward and feedback connections
required by the predictive coding model of cortical function. When this method is used …

Although models based on independent component analysis (ICA) have been successful in
explaining various properties of sensory coding in the cortex, it remains unclear how …

Algorithms that encode images using a sparse set of basis functions have previously been
shown to explain aspects of the physiology of a primary visual cortex (V1), and have been …

We study a sparse coding learning algorithm that allows for a simultaneous learning of the
data sparseness and the basis functions. The algorithm is derived based on a generative …

A highly effective kernel-based strategy used in machine learning is to transform the input
space into a new “feature” space where nonlinear problems become linear and more readily …

We study the application of a strongly non-linear generative model to image patches. As in
standard approaches such as Sparse Coding or Independent Component Analysis, the …

We investigate how the neural processing in auditory cortex is shaped by the statistics of
natural sounds. Hypothesising that auditory cortex (A1) represents the structural primitives …

Sparse coding algorithms with continuous latent variables have been the subject of a large
number of studies. However, discrete latent spaces for sparse coding have been largely …

Simple cells in primary visual cortex were famously found to respond to low-level image
components such as edges. Sparse coding and independent component analysis (ICA) …