Visual information is broadcast among cortical areas in discrete channels

Abstract

Neural circuitry represents sensory input with patterns of spiking activity. Across brain regions, initial representations are transformed to ultimately drive adaptive behavior. In mammalian neocortex, visual information is processed by primary visual cortex (V1) and multiple higher visual areas (HVAs). The interconnections of these brain regions, over which transformations can occur, span millimeters or more. Shared variability in spiking responses between neurons, called noise correlations (NCs), can be due to shared input and/or direct or indirect connectivity. Thus, NCs provide insight into the functional connectivity of neuronal circuits. In this study, we used subcellular resolution, mesoscale field-of-view two-photon calcium imaging to systematically characterize the NCs for pairs of layer 2/3 neurons across V1 and four HVAs (areas LM, LI, AL and PM) of mice. The average NCs for pairs of neurons within or across cortical areas were orders of magnitude larger than trial-shuffled control values. Within-area NCs declined with distance between neurons, but for inter-area NCs the distance-dependence was mixed. NCs were higher for neuron pairs with similar tuning (i.e., signal correlations). To explore tuning-specific NCs in finer detail, we used an unbiased clustering approach to classify neurons based on their responses to orientated gratings. This analysis revealed biases in the coverage of spatiotemporal frequency space across HVAs, and relationships between orientation tuning biases and spatiotemporal frequency preferences. Using the resulting functional groupings, we found group-specific within-area and inter-area patterns of NCs, indicating functionally specific subnetworks. Overall, these results reveal new principles for the functional organization and correlation structure across multiple cortical areas, which can inform and constrain computational theories of cortical networks.