Artificial neural networks (ANNs) are essential tools in machine learning that have drawn
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …

Despite enormous progress in machine learning, artificial neural networks still lag behind
brains in their ability to generalize to new situations. Given identical training data …

Despite great efforts over several decades, our best models of primary visual cortex (V1) still
predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited …

We can claim that we know what the visual system does once we can predict neural
responses to arbitrary stimuli, including those seen in nature. In the early visual system …

Response properties of sensory neurons are commonly described using receptive fields.
This description may be formalized in a model that operates with a small set of linear filters …

Abstract System identification is a growing approach to sensory neurophysiology that
facilitates the development of quantitative functional models of sensory processing. This …

Deep neural networks (DNNs) optimized for visual tasks learn representations that align
layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this …

An ultimate goal of systems neuroscience is to understand how sensory stimuli encountered
in the natural environment are processed by neural circuits. Achieving this goal requires …

Classification images have recently become a widely used tool in visual psychophysics.
Here, I review the development of classification image methods over the past fifteen years. I …

The visual system evolved to process natural images, and the goal of visual neuroscience is
to understand the computations it uses to do this. Indeed the goal of any theory of visual …