Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …

The ongoing exponential rise in recording capacity calls for new approaches for analysing
and interpreting neural data. Effective dimensionality has emerged as an important property …

Deep neural network models of sensory systems are often proposed to learn
representational transformations with invariances like those in the brain. To reveal these …

Deep neural networks currently provide the best quantitative models of the response
patterns of neurons throughout the primate ventral visual stream. However, such networks …

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history.
In more recent times, however, communication and collaboration between the two fields has …

Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to
solve cognitive tasks at which humans excel. In the absence of explanations for such …

Objects are the core meaningful elements in our visual environment. Classic theories of
object vision focus upon object recognition and are elegant and simple. Some of their …

Highlights•New generations of recording and computing technologies have enabled
neuroscience at the level of the neural population.•Landmark scientific findings suggest the …

To learn how cognition is implemented in the brain, we must build computational models
that can perform cognitive tasks, and test such models with brain and behavioral …

Convolutional neural networks (CNNs) are increasingly used to model human vision due to
their high object categorization capabilities and general correspondence with human brain …