Abstract
Neocortical computations underlying vision are performed by a distributed network of functionally specialized areas. Mouse visual cortex, a dense interareal network that exhibits hierarchical properties, comprises subnetworks interconnecting distinct processing streams. To determine the layout of the mouse visual hierarchy, we have evaluated the laminar patterns formed by interareal axonal projections originating in each of ten areas. Reciprocally connected pairs of areas exhibit feedforward/feedback relationships consistent with a hierarchical organization. Beta regression analyses, which estimate a continuous hierarchical distance measure, indicate that the network comprises multiple nonhierarchical circuits embedded in a hierarchical organization of overlapping levels. Single-unit recordings in anaesthetized mice show that receptive field sizes are generally consistent with the hierarchy, with the ventral stream exhibiting a stricter hierarchy than the dorsal stream. Together, the results provide an anatomical metric for hierarchical distance, and reveal both hierarchical and nonhierarchical motifs in mouse visual cortex.
Highlights
Neocortical computations underlying vision are performed by a distributed network of functionally specialized areas
We injected the anterograde tracer biotinylated dextran amine (BDA) into V1 and 9 of the 10 higher visual areas that have been previously identified through topographic mapping of projections from V123,24, and whose borders have been identified through anatomical and molecular landmarks:[8,21,25] LM, AL, RL, P, LI, PM, AM, A, POR, and PORa (Fig. 1a, b)
In order to minimize BDA uptake by broken fibers of passage, and to ensure labeling of neurons in all six layers, iontophoretic injections with fine pipettes were performed through the depth of cortex
Summary
Neocortical computations underlying vision are performed by a distributed network of functionally specialized areas. Each higher area is thought to assemble the feature-selective RFs of lower areas and integrate them to form increasingly complex representations of the visual scene whose diverse spatiotemporal features are distributed across multiple higher-order areas[3,6] While this bottomup view of visual processing has inspired theoretical models of object recognition and categorization[2,7], it fails to account for the densely reciprocally interconnected network of the neocortex as well as the inferential nature of visual perception[8,9,10,11,12]. It is unclear to what extent consistent hierarchical relationships govern the ‘ultra-dense’ mouse cortical graph in which almost all possible connections between visual areas have been shown to exist[8,14,21] Such a dense network could lead to reciprocally connected pairs that exhibit FF laminar patterns in both directions being more frequent than in macaque[19]
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