Spatial organization of multisensory convergence in mouse isocortex

The diverse functions of different cortical areas are thought to arise from their distinct groups of inputs. However, additional organizing principles may exist in the spatial structure of converging inputs. We investigated spatial convergence patterns of projections from primary sensory areas to other areas throughout the mouse isocortex. We used a large tract tracing dataset to estimate the dimension of the space into which topographical connections from multiple modalities converged within each other cortical area. We call this measure the topography dimension (TD). TD is higher for areas that receive inputs of similar strength from multiple sensory modalities, and lower when multiple inputs terminate in register with one another. Across the isocortex, TD varied by a factor of 4. TD was positively correlated with hierarchy score, an independent measure that is based on laminar connection patterns. Furthermore, TD (an anatomical measure) was significantly related to several measures of neural activity. In particular, higher TD was associated with higher neural activity dimension, lower population sparseness, and lower lifetime sparseness of spontaneous activity, independent of an area’s hierarchical position. Finally, we analyzed factors that limited TD and found that linear correlations among projections from different areas typically had little impact, while diversity of connection strengths, both between different projections onto the same area, and within projections across different parts of an area, limited TD substantially. This analysis revealed additional intricacy of cortical networks, beyond areas’ sets of connections and hierarchical organization. We propose a means of approximating this organization in deep-network models.