The ability to study populations of neurons with a combination of network anatomy and in vivo
physiology creates new opportunities for examining how neuronal circuits process information. Here, we explored how both the geometry and the function of cortical neurons influence the patterns of connections between them. In the case of excitatory input to local inhibitory interneurons, geometry appeared to dominate over function7
. This finding may provide an anatomical substrate for a prediction from recent physiological studies of mouse visual cortex, in which inhibitory neurons were found to be less selective than excitatory neurons24–27
, cf. 28,29
(). Inhibitory interneurons that pool excitatory input could be used to set the gain of orientation-selective pyramidal cells26,34,35
; they might also be involved in modulation of brain state34
or in attention-dependent normalization of cortical activity36
A few studies have found some subtypes of inhibitory neurons to be selective in the mouse28,29
. We did not classify different subtypes of inhibitory neurons, and thus cannot rule out the possibility that some might receive more selective input. Furthermore, although our sample size was large enough to exclude a strong bias in the preferred orientation tunings of convergent cell pairs, a larger sampling is necessary to exclude weaker biases (see Methods).
Until it is possible to fully reconstruct large EM volumes22,37,38
, analysis of network connectivity will be limited to a partial sampling of the underlying anatomy. Here we concentrated on reconstructing the axons of functionally characterized pyramidal cells and their postsynaptic targets. Within our sample, we found that 51% of synapses were onto inhibitory targets, despite the preponderance of excitatory neurons in the cortex39–41
, and reports that 10–20% of the synapses made by pyramidal cells are onto inhibitory targets in cat17
. Whether the higher percentage we observed is due to a species difference43
, cf. 44
, or to the fact that we sampled synapses from proximal portions of the pyramidal cell axonal arbours, it resulted in our ability to sample a large number of convergences onto inhibitory targets ().
Although the volume we imaged using electron microscopy was comparatively large, it proved to be near the minimum required to perform an analysis relating cortical function to network anatomy. We collected the series of wide-field (350 × 450 μm) high-resolution EM images to encompass the axonal arbours of the functionally characterized neurons and the dendrites of their targets. Nonetheless, we were limited by the shortest dimension in our volume (52 μm), determined by the number of thin sections, so we could trace only 245 out of the thousands of synapses made by the functionally characterized neurons.
We anticipate that the size of serial EM volumes will increase substantially in the near future, due to increases in imaging throughput and series length made possible by automated techniques45,46
. The time required to trace connectivity between neurons will likely remain a limiting factor, although semi-automated techniques have already achieved 10-fold increases in throughput over purely manual approaches22
. For large-scale reconstruction, data quality is paramount. To trace unlabelled, fine-calibre axons, minimum section thickness, minimum section loss, and optimal tissue quality are essential47,48
In the current study, we found a large number of convergent inputs onto inhibitory neurons principally because they were densely innervated by the excitatory axons we reconstructed. Probing the network anatomy of more sparsely interconnected (and possibly weakly biased49
) excitatory neurons50
, however, will require larger samples. Here, we sought to limit tissue damage from the infrared laser, so two-photon calcium imaging was confined to a single plane, or less than 1% of the cells in the volume (Supplementary Fig. 8a
). Recent advances in calcium imaging47,48
, however, should now allow physiology to be collected from many more cells in a volume while maintaining tissue quality.
It is fortunate that increases in the dimensions of an EM-imaged volume, and the number of physiologically characterized cells within it, produce combinatorial increases in the number of network motifs8
that can be analyzed in a single experiment (Supplementary Fig. 8b–d
). In particular, if a population of neurons is sparsely sampled, the number of interconnections found between them increases as the square of the sampling density. With moderate gains in the number of functionally imaged cells, or in the volumes encompassed by EM reconstructions, insight into the functional logic of cortical networks should therefore increase at an accelerating pace.