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 …

Sensory signals are highly structured in both space and time. These structural regularities in
visual information allow expectations to form about future stimulation, thereby facilitating …

Why deep neural networks (DNNs) capable of overfitting often generalize well in practice is
a mystery [24]. To find a potential mechanism, we focus on the study of implicit biases …

Recent studies claim that visual perception of stimulus features, such as orientation,
numerosity, and faces, is systematically biased toward visual input from the immediate past …

Visual input often arrives in a noisy and discontinuous stream, owing to head and eye
movements, occlusion, lighting changes, and many other factors. Yet the physical world is …

Sensory responses and behavior are strongly shaped by stimulus history. For example,
perceptual reports are sometimes biased toward previously viewed stimuli (serial …

We propose a blind (no reference or NR) video quality evaluation model that is nondistortion
specific. The approach relies on a spatio-temporal model of video scenes in the discrete …

Predictive coding is a unifying framework for understanding redundancy reduction and
efficient coding in the nervous system. By transmitting only the unpredicted portions of an …

▪ Abstract It has long been assumed that sensory neurons are adapted, through both
evolutionary and developmental processes, to the statistical properties of the signals to …

Aims and Scope This book is both an introductory textbook and a research monograph on
modeling the statistical structure of natural images. In very simple terms,“natural images” are …