33 The mammalian visual system, from retina to neocortex, has been extensively studied at both 34 anatomical and functional levels. Anatomy indicates the cortico-thalamic system is hierarchical, 35 but characterization of cellular-level functional interactions across multiple levels of this 36 hierarchy is lacking, partially due to the challenge of simultaneously recording activity across 37 numerous regions. Here, we describe a large, open dataset (part of the Allen Brain Observatory) 38 that surveys spiking from units in six cortical and two thalamic regions responding to a battery of 39 visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional 40 connectivity mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. 41Classical functional measures of hierarchy, including visual response latency, receptive field 42 size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated 43 with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral 44 relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide 45 a foundation for understanding coding and dynamics in the mouse cortico-thalamic visual 46 system. 47

SummaryTo understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.