A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex

Abstract

To 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 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 from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 243 adult mice, in response to a systematic set of visual stimuli. We classify neurons based on joint reliabilities to multiple stimuli and validate this functional classification with models of visual responses. While most classes are characterized by responses to specific subsets of the stimuli, the largest class is not reliably responsive to any of the stimuli and becomes progressively larger in higher visual areas. These classes reveal a functional organization wherein putative dorsal areas show specialization for visual motion signals. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Chandelier cell anatomy and function reveal a variably distributed but common signal

“…To simultaneously acquire imagery from sparsely distributed cells ( Figure 6A), we used a multi-plane imaging system ) that allowed near-simultaneous monitoring across a range of cortical depth ( Figure 6C). The imaging procedure followed a standardized awake behaving paradigm (de Vries et al, 2020), during which head-fixed mice were presented with a screen with uniform luminance and allowed to engage in spontaneous behavior. In all imaging sessions, we observed striking seconds-long bouts where all ChCs were active at the same time.…”

“…The calcium trace for each ROI was calculated by the mean of pixel-wise product between the ROI and each frame of the movie, and its neuropil trace was calculated in the same way using its neuropil ROI. To remove the neuropil contamination, the neuropil contribution of each ROI's calcium trace was estimated by a linear model and optimized by gradient descendent regression with a smoothness regularization de Vries et al, 2020).…”

Adaptation supports short-term memory in a visual change detection task

“…Asymmetry in the detectability of natural images 38 We tested two types of models in the context of the change detection task, one based on persistent neural 39 activity and one based on short-term synaptic depression ( Figure 2). Input activity to the models was sparse, 40 matching the observed responses in a large-scale survey of mouse visual cortex [4]. Both models reached 41 similar performance as the mice in the experiments, as quantified by d-prime, hit rates, and false alarm 42 rates ( Figure 3A-C).…”

“…232 Stimuli were spherically warped to account for the variable distance from the eye to the periphery of the 233 display monitor (all figures show the unwarped stimuli for ease of visualization). A set of eight natural 234 images ("image set A") was presented, with all images being drawn from a larger set of images previously 235 used as part of the Allen Brain Observatory [4]. Natural images were presented in grayscale, contrast 236 normalized, and matched to have equal mean luminance.…”

“…Short-term memory, or the ability to temporarily store and maintain task-relevant information, is a 2 fundamental cognitive function. A large body of experimental and computational work suggests that this 3 information can be maintained in persistent neural activity arising from local recurrent connections [28] or 4 even cortical-subcortical loops [11] (for a recent review, we refer the reader to [31]). Both sustained and 5 sequential forms of persistent activity have been observed across tasks and brain regions [26], including the 6 prefrontal cortex (PFC) [7,18,24].…”