Causal Evidence for the Role of Specific GABAergic Interneuron Types in Entorhinal Recruitment of Dentate Granule Cells

Abstract

The dentate gyrus (DG) is the primary gate of the hippocampus and controls information flow from the cortex to the hippocampus proper. To maintain normal function, granule cells (GCs), the principal neurons in the DG, receive fine-tuned inhibition from local-circuit GABAergic inhibitory interneurons (INs). Abnormalities of GABAergic circuits in the DG are associated with several brain disorders, including epilepsy, autism, schizophrenia, and Alzheimer disease. Therefore, understanding the network mechanisms of inhibitory control of GCs is of functional and pathophysiological importance. GABAergic inhibitory INs are heterogeneous, but it is unclear how individual subtypes contribute to GC activity. Using cell-type-specific optogenetic perturbation, we investigated whether and how two major IN populations defined by parvalbumin (PV) and somatostatin (SST) expression, regulate GC input transformations. We showed that PV-expressing (PV+) INs, and not SST-expressing (SST+) INs, primarily suppress GC responses to single cortical stimulation. In addition, these two IN classes differentially regulate GC responses to θ and γ frequency inputs from the cortex. Notably, PV+ INs specifically control the onset of the spike series, whereas SST+ INs preferentially regulate the later spikes in the series. Together, PV+ and SST+ GABAergic INs engage differentially in GC input-output transformations in response to various activity patterns.

Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy

“…Bursting does not seem to result from other changes in excitatory input-output coupling either ( Figure 7B5 ), which suggest that inhibition might be involved. GCs are indeed subjected to strong tonic, feedforward and feedback inhibition that controls the sparseness of their activity (Coulter and Carlson, 2007; Ewell and Jones, 2010; Pardi et al, 2015; Lee et al, 2016) and is altered in TLE (Alexander et al, 2016; Dengler and Coulter, 2016). We did not record evoked IPSCs directly but, in normal mice, partial block of inhibition elevates GC firing rates and causes bursts (Madar et al, 2019b) similar to what we observed here in KA mice.…”

Experience-dependent inhibitory plasticity is mediated by CCK+ basket cells in the developing dentate gyrus

“…In the DG, the connectivity of the GABAergic network is uniquely suited to support pattern separation (Bel et al, 2015;Espinoza et al, 2018; C.-T. T. Lee et al, 2016). In light of the structural remodeling induced by EE observed in our study, we questioned whether these changes could impact the transformation of the entorhinal inputs in the circuit.…”

“…Recruitment of GC during spatial exploration and learning tasks is very sparse (Chawla et al, 2005;Deng et al, 2013;Hainmueller & Bartos, 2018;Leutgeb, Leutgeb, Moser, & Moser, 2007). Anatomical and electrophysiological studies demonstrate that the unique properties of the inhibitory network in the DG is well suited to support sparsification and decorrelation (Acsády, Kamondi, Sík, Freund, & Buzsáki, 1998;Acsády, Katona, Martínez-Guijarro, Buzsáki, & Freund, 2000;Bel et al, 2015;Espinoza et al, 2018; C.-T. T. Lee et al, 2016;Madar et al, 2019b;Sambandan, Sauer, Vida, & Bartos, 2010). Nonetheless, the specific role of distinct classes of IN in DG computation is not well established.…”

“…Interestingly, each inhibitory cell-type projects their axons to distinct laminae where they target specific subdomains along the somatodendritic axis of principal excitatory GCs. This unique connectivity rule enables pattern separation, a process by which the rich and overlapping multimodal inputs from the entorhinal cortex are decorrelated into a sparse and specific output pattern (Barak et al, 2019;Bel, Pardi, Ogando, Schinder, & Marin-Burgin, 2015;Espinoza, Guzman, Zhang, & Jonas, 2018; C.-T. T. Lee et al, 2016;Madar, Ewell, & Jones, 2019b;Szabo et al, 2017). This computation is critical for information coding in the hippocampus and for memory indexation and retrieval (Deng, Mayford, & Gage, 2013;Kesner, Kirk, Yu, Polansky, & Musso, 2016).…”

Quantitative properties of a feedback circuit predict frequency-dependent pattern separation

“…The DG is thought to subserve this task by converting different types of inputs to sparse, nonoverlapping activity patterns of granule cells (GCs). However, in contrast to the insect olfactory system, the DG feedback circuit is extremely complex, comprising numerous interconnected interneuron types (Supplementary Table 1) [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] . For instance, interneurons subserving feedback inhibition are also incorporated into circuits mediating feedforward inhibition 32,35,40 and disinhibition 21,29 .…”