A central goal of neuroscience is to understand the representations formed by brain activity
patterns and their connection to behaviour. The classic approach is to investigate how …

Our knowledge of sensory processing has advanced dramatically in the last few decades,
but this understanding remains far from complete, especially for stimuli with the large …

We investigate how the population nonlinearities resulting from lateral inhibition and
thresholding in sparse coding networks influence neural response selectivity and …

We present a signal representation framework called the sparse manifold transform that
combines key ideas from sparse coding, manifold learning, and slow feature analysis. It …

In vision science, cascades of Linear+ Nonlinear transforms are very successful in modeling
a number of perceptual experiences. However, the conventional literature is usually too …

Highlights•Subunit pooling and normalization are building blocks of hierarchical cortical
models.•Image statistics models predict when normalization is recruited in primary …

Deep convolutional neural networks (CNNs) are becoming increasingly popular models to
predict neural responses in visual cortex. However, contextual effects, which are prevalent in …

In this article, we explore two forms of selectivity in sensory neurons. The first we call classic
selectivity, referring to the stimulus that optimally stimulates a neuron. If a neuron is linear …

Measuring the color tuning of visual neurons is important for understanding the neural basis
of vision, but it is challenging because of the inherently three-dimensional nature of color …

Abstract The Locally Competitive Algorithm (LCA) is a recurrent neural network for
performing sparse coding and dictionary learning of natural signals. The network itself lacks …