Differential coupling of adult-born granule cells to parvalbumin and somatostatin interneurons

Abstract

The dentate gyrus of the hippocampus is dominated by a strong GABAergic tone that maintains sparse levels of activity. Adult neurogenesis disrupts this balance through the continuous addition of new granule cells (GCs) that display high excitability while develop and connect within the preexisting host circuit. The dynamics of the connectivity map for developing GCs in the local inhibitory networks remains unknown. We used optogenetics to study afferent and efferent synaptogenesis between new GCs and GABAergic interneurons expressing parvalbumin (PV-INs) and somatostatin (SST-INs). Inputs from PV-INs targeted the soma and remained immature until they grew abruptly in >4-week-old GCs. This transition was accelerated by exposure to enriched environment. Inputs from SST-INs were dendritic and developed slowly until reaching maturity by 8 weeks. Synaptic outputs from GCs onto PV-INs matured faster than those onto SST-INs, but also required several weeks. In the mature dentate network, PV-INs exerted an efficient control of GC spiking and were involved in both feedforward and feedback loops, a mechanism that would favor lateral inhibition and sparse coding. Our results reveal a long-lasting transition where adult-born neurons remain poorly coupled to inhibition, which might enable a parallel streaming channel from the entorhinal cortex to CA3 pyramidal cells.

Adult dentate gyrus neurogenesis: a functional model

“…We model a small patch of cells within dentate gyrus as a recurrent network of 100 DGCs and 25 GABAergic interneurons, omitting the Mossy cells for the sake of simplicity (Figure 1A). The modeled interneurons correspond to SST-INs from the HIPP category, as they are the providers of feedback inhibition to DGCs through dendritic projections (Stefanelli et al, 2016; Yuan et al, 2017; Groisman et al, 2020). The activity of a DGC with index i and an interneuron with index k is described by their continuous firing rates v i and , respectively.…”

“…For the stimulation of EC cells, we apply patterns representing thousands of handwritten digits in different writing styles from MNIST, a standard data set in artificial intelligence (LeCun et al, 1998). We implicitely model feedforward inhibition from PV-INs (Groisman et al, 2020) by normalizing the L2-norm of each input pattern to unity (Methods). Below, we present results for a representative combination of three digits (digits 3, 4 and 5), but other combinations of digits have also been tested (Suppl.…”

“…1d). We implicitely model feedforward inhibition from PV-INs (Groisman et al, 2020) by normalizing the L2-norm of each input pattern to unity (Methods). Below, we present results for a representative combination of three digits (digits 3, 4 and 5), but other combinations of digits have also been tested (Suppl .…”

“…PV-expressing interneurons (PV-INs) and SST-INs both target adult-born DGCs early (after 2-3 weeks) in their maturation (Groisman et al, 2020). PV-INs provide both feedforward inhibition and feedback inhibition (also called lateral inhibition) to the DGCs (Groisman et al, 2020). In general, SST-INs provide lateral, but not feedforward, inhibition onto DGCs (Stefanelli et al, 2016; Groisman et al, 2020).…”

Neurophotonics Approaches for the Study of Pattern Separation

“…After activation of MCs and abGCs, they can initiate an inhibitory network. While abGCs can inhibit directly GCs (Luna et al, 2019), MCs and abGCs would activate PV (Scharfman, 2018;Groisman et al, 2020), which in turn produces a lateral inhibition proposed to be important for pattern separation (Espinoza et al, 2018). Interestingly, the activity of SOM modulates the activity of PV (Savanthrapadian et al, 2014;Yuan et al, 2017) and can produce itself a lateral inhibition (Stefanelli et al, 2016).…”