The vertebrate retina first evolved some 500 million years ago in ancestral marine
chordates. Since then, the eyes of different species have been tuned to best support their …

Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large
computer vision benchmarks. A successful approach to SSL is to learn embeddings which …

It has been known for over a century that the basic organization of the retina is conserved
across vertebrates. It has been equally clear that retinal cells can be classified into …

Neural computations are currently investigated using two separate approaches: sorting
neurons into functional subpopulations or examining the low-dimensional dynamics of …

Understanding the neural basis of the remarkable human cognitive capacity to learn novel
concepts from just one or a few sensory experiences constitutes a fundamental problem. We …

The discovery of entorhinal grid cells has generated considerable interest in how and why
hexagonal firing fields might emerge in a generic manner from neural circuits, and what their …

Pressures for survival make sensory circuits adapted to a species' natural habitat and its
behavioral challenges. Thus, to advance our understanding of the visual system, it is …

Visual neurons respond selectively to features that become increasingly complex from the
eyes to the cortex. Retinal neurons prefer flashing spots of light, primary visual cortical (V1) …

Neurons in the brain are often finely tuned for specific task variables. Moreover, such
disentangled representations are highly sought after in machine learning. Here we …

In vertebrate vision, early retinal circuits divide incoming visual information into functionally
opposite elementary signals: On and Off, transient and sustained, chromatic and achromatic …