Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits

Hill, SL. Wang, Y. Riachi, I. Schurmann, F. Markram, H. (2012) Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits. PNAS 109 (42): E2885-E2894.

At SfN this year there was a big poster session on Markram's Blue Brain project. The goal of this project is to basically take everything we know about cortex and then put it together in a bottom-up fashion. I don't think that putting together cortex in this manner will create a working cortex, but it will definitely guide ideas about a theory of cortex. This is one of several papers from Blue Brain project that I'm going to review.

The conclusion of this paper is that for most neurons/neuron types the probability that two neurons are connected is directly related to the amound of over-lap between the pre-synaptic axons and the post-synaptic dendrites. The model is basically that if neurons can wire together (i.e. because they actually make contact) then they will. The alternative is that chemical cues lead to specific types of connectivity, but for this to work tons of different cue combinations would be needed. However, on a larger-scale this may be the case, especially across neuron types - i.e. one neural type may be avoid another neuronal type, even though their axons/dendrites overlap significantly.

Rat somatosensory cortex P12-16. 10 types of neurons. For functional connectivity they measured responses in paired whole-cell patch-clamp recordings. They also stained neurons and made light-based reconstructions to identify the putative synapses. Putative synapses were described with two measurements: dendritic and axonal branch order, and path distance. Putative synapses showed domain-specific patterning - where different classes target specific areas of the dendritic tree. i.e. Pyr->Pyr synapses are typically on basal tree, martinotti neurons innervate distal dendrites of Pyr, and small baskets innervate somata and proximal dendrites.

To get statistical connectivity they took arbors from different animals and placed them randomly in their cortex model. Then measured sites of potential appositions. Statistical connectivity also reflected the domain-specific innervations of functional connectivity.

Tweaks to the model, such as slightly repositioning the neurons, changing the range of putative synapses does not have major effects on the statistical structural connectome. Altering the morphology and changing the number of morphological types of pyramids leads to similar results, suggesting that the structural connectome is robust to perturbations and that synapse distributions are invariant across animals.

The statistical connectivity can equally be calculated by combining morphologies of pyramids into a distribution and calculating the overlaps of the probabilistic distributions. This yields essentially the same result as calculating the statistical connections based on real neuronal morphologies and finding overlaps. Here is what the statistical distributions of pyramidal cells look like from their model:

In conclusion, chemical signals are present that set-up the layer-specific targeting of dendrites and axons. They may be involved in some other minor changes like repulsion of Py-Py synapses away from the soma. But most of connectivity can be approximated from statistical overlap. It is then likely that experience-based plasticity then strengthens and removes synapses.