We computed the potential connectivity map of all known neuron types in the rodent hippocampal formation by supplementing scantly available synaptic data with spatial distributions of axons and dendrites from the open-access knowledge base Hippocampome.org. The network that results from this endeavor, the broadest and most complete for a mammalian cortical region at the neuron-type level to date, contains more than 3,200 connections among 122 neuron types across six sub-regions. Analyses of these data employing graph theory metrics unveil the fundamental architectural principles of the hippocampal circuit. Globally, we identify a highly specialized topology minimizing communication cost; a modular structure underscoring the prominence of the tri-synaptic loop; a core set of neuron types serving as information processing hubs as well as a distinct group of particular anti-hub neurons; a nested, two-tier rich club managing much of the network traffic; and an innate resilience to random perturbations. At the local level, we uncover the basic building blocks, or connectivity patterns, that combine to produce complex global functionality, and we benchmark their utilization in the circuit relative to random networks. Taken together, these results provide a comprehensive connectivity profile of the hippocampus, yielding novel insights on its functional operations at the computationally crucial level of neuron types.
Significance Statement: Brain connectomes are being constructed at two disjointed levels. Microscopically, the wiring of individual neurons is being accumulated into cumbersome synaptomes; macroscopically, region-to-region projectomes obscure important circuit details. Neuron types provide a fertile middle ground. Using the 122 hippocampal formation types from Hippocampome.org, we augmented sparse connectivity knowledge with morphological evidence to obtain a full “potential connectome.” Though this network contains >3,200 connections that are not easily amenable to intuitive hypothesis generation and testing, such complexity can be tackled using graph theory analysis, whereby we investigate the relationship between the circuit’s connectivity properties and functions. As type-level data grows, the array of analyses detailed here can be extended to rapidly supplement our understanding of the computational operation of the hippocampus.
Authors report no conflict of interest.
This project was supported by grants NINDS R01NS39600 (NIH), NSF IIS-1302256, MURI N00014-10-1-0198 (ONR), and CENTEC (AFOSR). Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund.