Somatostatin-expressing-interneurons (SOMIs) in the dentate gyrus (DG) control formation of granule cell (GC) assemblies during memory acquisition. Hilar-perforant-path-associated interneurons (HIPP cells) have been considered to be synonymous for DG-SOMIs. Deviating from this assumption, we show two functionally contrasting DG-SOMI-types. The classical feedback-inhibitory HIPPs distribute axon fibers in the molecular layer. They are engaged by converging GC-inputs and provide dendritic inhibition to the DG circuitry. In contrast, SOMIs with axon in the hilus, termed hilar interneurons (HILs), provide perisomatic inhibition onto GABAergic cells in the DG and project to the medial septum. Repetitive activation of glutamatergic inputs onto HIPP cells induces long-lasting-depression (LTD) of synaptic transmission but long-term-potentiation (LTP) of synaptic signals in HIL cells. Thus, LTD in HIPPs may assist flow of spatial information from the entorhinal cortex to the DG, whereas LTP in HILs may facilitate the temporal coordination of GCs with activity patterns governed by the medial septum.DOI: http://dx.doi.org/10.7554/eLife.21105.001

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Even though it has been known for some time that in many mammalian brain areas interneurons are electrically coupled, a quantitative description of the network electrical connectivity and its impact on cellular passive properties is still lacking. Approaches used so far to solve this problem are limited because they do not readily distinguish junctions among direct neighbors from indirect junctions involving intermediary, multiply connected cells. In the cerebellar cortex, anatomical and functional evidence indicates electrical coupling between molecular layer interneurons (basket and stellate cells). An analysis of the capacitive currents obtained under voltage clamp in molecular layer interneurons of juvenile rats or mice reveals an exponential component with a time constant of ∼20 ms, which represents capacitive loading of neighboring cells through gap junctions. These results, taken together with dual cell recording of electrical synapses, have led us to estimate the number of direct neighbors to be ∼4 for rat basket cells and ∼1 for rat stellate cells. The weighted number of neighbors (number of neighbors, both direct and indirect, weighted with the percentage of voltage deflection at steady state) was 1.69 in basket cells and 0.23 in stellate cells. The last numbers indicate the spread of potential changes in the network and serve to estimate the contribution of gap junctions to cellular input conductance. In conclusion the present work offers effective tools to analyze the connectivity of electrically connected interneuron networks, and it indicates that in juvenile rodents, electrical communication is stronger among basket cells than among stellate cells. To model the functional role of gap junctions (GJs) in the IN network and in cellular computation, it is necessary to determine the number of cells that are connected to a given cell, as well as the geometry of the network. Methods that have been developed to extract this information include dye coupling analysis (e.g., ref. 6), paired recordings coupled with anatomical descriptions (7), and frequency-dependent impedance measurements (8). The first two methods do not readily distinguish direct connections from indirect connections involving an intermediate IN (7,9,10), and all three methods are labor intensive and difficult to implement in a fully quantitative manner. In addition, they do not provide information on the spatial arrangement of the GJs. Therefore, the data that have been exploited for modeling GJ connectivity in IN networks are missing critical elements.In the cerebellum, Golgi cells and molecular layer interneurons (MLIs) have been shown to form anatomical and functional networks involving GJs that are specific to a given cell type (6,(11)(12)(13)(14). In both cases GJs may be involved in the generation of concerted oscillations under some pharmacological conditions (12, 15), and spikelets (spikes of coupled cells filtered through GJs) have been shown to encode sensory information in MLIs (16). MLIs are particularly interesting because th...