SUMMARYSegmental duplications contribute to human evolution, adaptation and genomic instability but are often poorly characterized. We investigate the evolution, genetic variation and coding potential of human-specific segmental duplications (HSDs). We identify 218 HSDs based on analysis of 322 deeply sequenced archaic and contemporary hominid genomes. We sequence 550 human and nonhuman primate genomic clones to reconstruct the evolution of the largest, most complex regions with protein-coding potential (n=80 genes/33 gene families). We show that HSDs are non-randomly organized, associate preferentially with ancestral ape duplications termed “core duplicons”, and evolved primarily in an interspersed inverted orientation. In addition to Homo sapiens-specific gene expansions (e.g., TCAF1/2), we highlight ten gene families (e.g., ARHGAP11B and SRGAP2C) where copy number never returns to the ancestral state, there is evidence of mRNA splicing, and no common gene-disruptive mutations are observed in the general population. Such duplicates are candidates for the evolution of human-specific adaptive traits.

Parvalbumin-positive (PV+) GABAergic interneurons in hippocampal microcircuits are thought to play a key role in several higher network functions, such as feedforward and feedback inhibition, network oscillations, and pattern separation. Fast lateral inhibition mediated by GABAergic interneurons may implement a winner-takes-all mechanism in the hippocampal input layer. However, it is not clear whether the functional connectivity rules of granule cells (GCs) and interneurons in the dentate gyrus are consistent with such a mechanism. Using simultaneous patch-clamp recordings from up to seven GCs and up to four PV+ interneurons in the dentate gyrus, we find that connectivity is structured in space, synapse-specific, and enriched in specific disynaptic motifs. In contrast to the neocortex, lateral inhibition in the dentate gyrus (in which a GC inhibits neighboring GCs via a PV+ interneuron) is ~ 10-times more abundant than recurrent inhibition (in which a GC inhibits itself). Thus, unique connectivity rules may enable the dentate gyrus to perform specific higher-order computations.

Pattern separation is a fundamental brain computation that converts small differences in synaptic input patterns into large differences in action potential (AP) output patterns. Pattern separation plays a key role in the dentate gyrus, enabling the efficient storage and recall of memories in downstream hippocampal CA3 networks. Several mechanisms for pattern separation have been proposed, including expansion of coding space, sparsification of neuronal activity, and simple thresholding mechanisms. Alternatively, a winner-takes-all mechanism, in which the most excited cells inhibit all less-excited cells by lateral inhibition, might be involved. Although such a mechanism is computationally powerful, it remains unclear whether it operates in biological networks. Here, we develop a full-scale network model of the dentate gyrus, comprised of granule cells (GCs) and parvalbumin + (PV + ) inhibitory interneurons, based on experimentally determined biophysical cellular properties and synaptic connectivity rules. Our results demonstrate that a biologically realistic principal neuron-interneuron (PN-IN) network model is a highly efficient pattern separator. Mechanistic dissection in the model revealed that a winner-takes-all mechanism by lateral inhibition plays a crucial role in pattern separation. Furthermore, both fast signaling properties of PV + interneurons and focal GC-interneuron connectivity are essential for efficient pattern separation. Thus, PV + interneurons are not only involved in basic microcircuit functions, but also contribute to higher-order computations in neuronal networks, such as pattern separation.