Speaker
Description
Cortical sensory maps vary across species. In visual cortex, some animals organise neuronal preferences into smooth topological maps, whereas others show a salt-and-pepper-like arrangement. Both architectures can support sensory coding, but it remains unclear why evolution favours one organisation over the other. We propose that these architectures represent alternative solutions to a trade-off between local and global wiring costs.
Our model separates lateral cortical connectivity into a local pool, which supports organised domains and activity pooling, and a global pool, which supports longer-range interactions. Large local domains require more local connections, but pooling across these domains makes cortical activity more robust to connection loss and permits sparser global connectivity. By contrast, salt-and-pepper-like organisation reduces local wiring requirements but provides less pooling, requiring denser global connectivity to preserve function. The preferred architecture is the one that achieves matched coding performance and stability with the lowest total number of realised connections.
Using self-organising computational models, we measured combinations of local and global sparsity that preserved 95% of unperturbed activity stability. These simulations revealed a trade-off: increasing local pooling allowed greater sparsity in the global connection pool. We incorporated these relationships into a wiring-cost function and combined them with anatomical estimates of local and global connection pools across species.
The model predicts a crossover between two economical regimes. Salt-and-pepper-like organisation is cheaper when global connection demands are relatively small, whereas topological maps become cheaper when savings from sparse global connectivity outweigh the added cost of building large local domains. Applied to species-level data, the lower-cost regime was consistent with the observed cortical organisation across the animals.
These results suggest that topological and salt-and-pepper cortical maps can be understood within a common wiring-economy framework. More generally, cortical map architecture may reflect the minimum synaptic cost required to achieve robust and functionally adequate neural coding.
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