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 …

Neuroscientists today can measure activity from more neurons than ever before, and are
facing the challenge of connecting these brain-wide neural recordings to computation and …

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust
understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in …

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized
hierarchically. However, direct observation of cellular-level functional interactions across this …

Map** behavioural actions to neural activity is a fundamental goal of neuroscience. As our
ability to record large neural and behavioural data increases, there is growing interest in …

A neuronal population encodes information most efficiently when its stimulus responses are
high-dimensional and uncorrelated, and most robustly when they are lower-dimensional …

We present a unique, extensive, and open synaptic physiology analysis platform and
dataset. Through its application, we reveal principles that relate cell type to synaptic …

In vivo two-photon calcium imaging (2PCI) is a technique used for recording neuronal
activity in the intact brain. It is based on the principle that, when neurons fire action …

Recent studies have shown that neuronal representations gradually change over time
despite no changes in the stimulus, environment, or behavior. However, such …

To understand the brain we must relate neurons' functional responses to the circuit
architecture that shapes them. Here, we present a large functional connectomics dataset …