Kavli Affiliate: Michael Stryker
| Authors: Luciano Dyballa, Greg D Field, Michael P Stryker and Steven W Zucker
| Summary:
A challenge in sensory neuroscience is understanding how populations of neurons operate in concert to represent diverse stimuli. To meet this challenge, we have created “encoding manifolds” that reveal the overall responses of brain areas to diverse stimuli with the resolution of individual neurons and their response dynamics. Here we use encoding manifold to compare the population-level encoding of primary visual cortex (VISp) with five higher visual areas (VISam, VISal, VISpm, VISlm, and VISrl). We used data from the Allen Institute Visual Coding–Neuropixels dataset from the mouse. We show that the encoding manifold topology computed only from responses to grating stimuli is continuous, for V1 and for higher visual areas, with smooth coordinates spanning it that include orientation selectivity and firing-rate magnitude. Surprisingly, the manifolds for each visual area revealed novel relationships between how natural scenes are encoded relative to static gratings—a relationship that was conserved across visual areas. Namely, neurons preferring natural scenes preferred either low or high spatial frequency gratings, but not intermediate ones. Analyzing responses by cortical layer reveals a preference for gratings concentrated in layer 6, whereas preferences for natural scenes tended to be higher in layers 2/3 and 4. The results reveal how machine learning approaches can be used to organize and visualize the structure of sensory coding, thereby revealing novel relationships within and across brain areas and sensory stimuli. Significance Statement Manifolds have become a commonplace for analyzing and visualizing neural responses. However, prior work has focused on building manifolds that organize diverse stimuli in neural response coordinates. Here, we demonstrate the utility of an alternative approach: building manifolds to represent neurons in stimulus/response coordinates, which we term ‘encoding manifolds’. This approach has several advantages, such as being able to directly visualize and compare how different brain areas encode diverse stimulus ensembles. We use the approach to reveal novel relationships between layer-specific responses and the encoding of natural versus artificial stimuli.