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 …

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 …

Neurons in primary visual cortex (V1) are commonly classified as simple or complex based
upon their sensitivity to the sign of stimulus contrast. The responses of both cell types can be …

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

In this study we investigate temporal slowness as a learning principle for receptive fields
using slow feature analysis, a new algorithm to determine functions that extract slowly …

Neuroscientists classify neurons into different types that perform similar computations at
different locations in the visual field. Traditional methods for neural system identification do …

The computation represented by a sensory neuron's response to stimuli is constructed from
an array of physiological processes both belonging to that neuron and inherited from its …

Through the influence of Goldman-Rakic, much research has been focused on the role of
the dorsolateral prefrontal cortex in spatial working memory, decision making, and saccade …

The response properties of neurons in the early stages of the visual system can be
described using the rectified responses of a set of self-similar, spatially shifted linear filters …