Cellular and circuit features distinguish dentate gyrus semilunar granule cells and granule cells activated during contextual memory formation

Reviewer #1 (Public review):

Dovek and colleagues aimed at investigating the cellular and circuitry mechanisms underlying the recruitment of two morpho-physiologically-distinct subpopulations of dentate gyrus excitatory cells (granular cells or GCs, and semilunar cells or SGCs) into memory representations, also known as engrams.

To this end, the authors used TRAP2 mice to investigate the dentate gyrus "engram" neurons that were recruited or not (i.e., labeled or not) in a specific context (mostly enriched environment or EE, but also Barnes Maze or BM). GCs and SGCs were distinguished using a morphologically based classification. In line with previous observations (Erwin et al., 2022), SGCs exhibited a disproportionate context-dependent recruitment. Although they represent less than 5% of the excitatory neurons in the dentate gyrus, they comprise around 30% of behaviorally activated "engram" neurons.

Then, the authors compared the intrinsic physiological properties of GCs and SGCs that are recruited or not during EE. Consistent with previous observations (Williams et al., 2007, Afrasiabi et al., 2022), SGCs and GCs exhibited numerous differences (e.g., Rin, firing frequency) regardless of whether they were behaviorally activated or not. Only the adaptation in firing rate enabled the discrimination of "engram" SGCs (which displayed lower values) from non-recruited SGCs.

To examine how GCs and SGCs activated during EE are integrated into the local dentate gyrus microcircuits, the authors next performed a dual patch-clamp recording combined with wide-field optogenetics. Despite the presence of spontaneous EPSCs, no direct functional glutamatergic interconnection was observed between pairs of "engram" GCs and SGCs. In addition, the stimulation of behaviorally recruited GCs or SGCs rarely elicits IPSCs in non-engram excitatory neurons, which suggests limited lateral inhibition.

Last, the authors investigated whether neurons recruited in the same context were characterized by a higher propensity to receive temporally correlated inputs. To this end, they performed a dual patch-clamp and analyzed the temporal correlation of spontaneous EPSCs received by pairs of neurons (either two dentate gyrus "engram" neurons, or one "engram" neuron and one "non-engram" neuron in an EE context). They observed that the temporal correlation of excitatory events received by pairs of engram neurons was greater than that of pairs of neurons that do not belong to the same ensemble, and that expected by chance.

Altogether, the data suggest that distinctive intrinsic properties and shared excitatory afferent, rather than local microcircuit connectivity, are correlated with the context-dependent recruitment of dentate gyrus excitatory neurons.

Strengths:

This article raises interesting questions about the recruitment mechanisms of the neuronal ensembles that form memory engrams in the dentate gyrus. I find it particularly interesting that the authors considered not only granular cells, the main population of excitatory neurons in the dentate gyrus, but also a sparse subpopulation of semilunar cells, an understudied type of neuron described by Cajal, then almost forgotten for a century, and finally brought out of oblivion in the mid-2000s (Williams et al., 2007).

Weaknesses:

I think the article is a little too immature in its current form. I'd recommend that the authors work on their writing. For example, the objectives of the article are not completely clear to me after reading the manuscript, composed of parts where the authors seem to focus on SGCs, and others where they study "engram" neurons without differentiating the neuronal type (Figure 5). The next version of the manuscript should clearly establish the objectives and sub-aims.

In addition, some results are not entirely novel (e.g., the disproportionate recruitment as well as the distinctive physiological properties of SGCs), and/or based on correlations that do not fully support the conclusions of the article. In addition to re-writing, I believe that the article would benefit from being enriched with further analyses or even additional experiments before being resubmitted in a more definitive form.

Reviewer #2 (Public review):

Summary:

The authors use the TRAP2 mouse line to label dentate gyrus cells active during an enriched environment paradigm and cut brain slices from these animals one week later to determine whether granule cells (GC) and semilunar granule cells (SGC) labelled during the exposure share common features. They particularly focus on the role of SGCs and potential circuit mechanisms by which they could be selectively embedded in the labelled assembly. The authors claim that SGCs are disproportionately recruited into IEG-expressing assemblies due to intrinsic firing characteristics but cannot identify any contributing circuit connectivity motives in the slice preparation, although they claim that an increased correlation between spontaneous synaptic currents in the slice could signify common synaptic inputs as the source of assembly formation.

Strengths:

The authors chose a timely and relevant question, namely how memory-bearing neuronal assemblies, or 'engrams', are established and maintained in the dentate gyrus. After the initial discovery of such memory-specific ensembles of immediate-early gene expressing engrams in 2012 (Ramirez et al.) this issue has been explored by several high-profile studies that have considerably expanded our understanding of the underlying molecular and cellular mechanisms, but still leave a lot of unanswered questions.

Weaknesses:

Unfortunately, there are several major methodological issues that put into question most if not all central claims made by the authors:

(1) The authors conclude that SGCs are disproportionately recruited into cfos assemblies during the enriched environment and Barnes maze task given that their classifier identifies about 30% of labelled cells as SGCs in both cases and that another study using a different method (Save et al., 2019) identified less than 5% of an unbiased sample of granule cells as SGCs. To make matters worse, the classifier deployed here was itself established on a biased sample of GCs patched in the molecular layer and granule cell layer, respectively, at even numbers (Gupta et al., 2020). The first thing the authors would need to show to make the claim that SGCs are disproportionately recruited into memory ensembles is that the fraction of GCs identified as SGCs with their own classifier is significantly lower than 30% using their own method on a random sample of GCs (e.g. through sparse viral labelling). As the authors correctly state in their discussion, morphological samples from patch-clamp studies are problematic for this purpose because of inherent technical issues (i.e. easier access to scattered GCs in the molecular layer).

(2) The authors claim that recurrent excitation from SGCs onto GCs or other SGCs is irrelevant because they did not find any connections in 32 simultaneous recordings (plus 63 in the next experiment). Without a demonstration that other connections from SGCs (e.g. onto mossy cells or interneurons) are preserved in their preparation and if so at what rates, it is unclear whether this experiment is indicative of the underlying biology or the quality of the preparation. The argument that spontaneous EPSCs are observed is not very convincing as these could equally well arise from severed axons (in fact we would expect that the vast majority of inputs are not from local excitatory cells). The argument on line 418 that SGCs have compact axons isn't particularly convincing either given that the morphologies from which they were derived were also obtained in slice preparations and would be subject to the same likelihood of severing the axon. Finally, even in paired slice recordings from CA3 pyramidal cells the experimentally detected connectivity rates are only around 1% (Guzman et al., 2016). The authors would need to record from a lot more than 32 pairs (and show convincing positive controls regarding other connections) to make the claim that connectivity is too low to be relevant.

(3) Another troubling sign is the fact that optogenetic GC stimulation rarely ever evokes feedback inhibition onto other cells which contrasts with both other in vitro (e.g. Braganza et al., 2020) and in vivo studies (Stefanelli et al., 2016) studies. Without a convincing demonstration that monosynaptic connections between SGCs/GCs and interneurons in both directions is preserved at least at the rates previously described in other slice studies (e.g. Geiger et al., 1997, Neuron, Hainmueller et al., 2014, PNAS, Savanthrapadian et al., 2014, J. Neurosci), the notion that this setting could be closer to naturalistic memory processing than the in vivo experiments in Stefanelli et al. (e.g. lines 443-444) strikes me as odd. In any case, the discussion should clearly state that compromised connectivity in the slice preparation is likely a significant confound when comparing these results.

(4) Probably the most convincing finding in this study is the higher zero-time lag correlation of spontaneous EPSCs in labelled vs. unlabeled pairs. Unfortunately, the fact that the authors use spontaneous EPSCs to begin with, which likely represent a mixture of spontaneous release from severed axons, minis, and coordinated discharge from intact axon segments or entire neurons, makes it very hard to determine the meaning and relevance of this finding. At the bare minimum, the authors need to show if and how strongly differences in baseline spontaneous EPSC rates between different cells and slices are contributing to this phenomenon. I would encourage the authors to use low-intensity extracellular stimulation at multiple foci to determine whether labelled pairs really share higher numbers of input from common presynaptic axons or cells compared to unlabeled pairs as they claim. I would also suggest the authors use conventional Cross correlograms (CCG; see e.g. English et al., 2017, Neuron; Senzai and Buzsaki, 2017, Neuron) instead of their somewhat convoluted interval-selective correlation analysis to illustrate co-dependencies between the event time series. The references above also illustrate a more robust approach to determining whether peaks in the CCGs exceed chance levels.

(5) Finally, one of the biggest caveats of the study is that the ensemble is labelled a full week before the slice experiment and thereby represents a latent state of a memory rather than encoding consolidation, or recall processes. The authors acknowledge that in the discussion but they should also be mindful of this when discussing other (especially in vivo) studies and comparing their results to these. For instance, Pignatelli et al 2018 show drastic changes in GC engram activity and features driven by behavioral memory recall, so the results of the current study may be very different if slices were cut immediately after memory acquisition (if that was possible with a different labelling strategy), or if animals were re-exposed to the enriched environment right before sacrificing the animal.

Reviewer #3 (Public review):

Summary:

The study explores the cellular and circuit features that distinguish dentate gyrus semilunar granule cells and granule cells activated during contextual memory formation. The authors tag memory and enriched environment-activated dentate granule cells and semilunar granule cells and show their reactivation in an appropriate context a week later. They perform patch clamp recordings from activated and surrounding neurons to understand cellular driving the selective activation of semilunar granule cells and granule cells. Authors perform dual patch clamp recordings from various pairs of labeled semilunar granule cells, labeled granule cells, unlabeled granule cells, and unlabeled semilunar granule cells. The sustained firing of semilunar granule cells explained their preferential activation. In addition, activated neurons received correlated inputs.

Strengths:

The authors confirmed engram cell properties of activated semilunar granule cells and granule cells in two different paradigms, validated using an enriched environment paradigm.

The authors carefully separate semilunar granule cells from granule cells, using electrophysiology and morphology. Cell filling to confirm morphology further strengthens confidence.

The dual patch recordings, which are technically challenging, are carefully performed, and the presence of synaptic activity is confirmed.

Finally, the correlation analysis of EPSCs on labeled neurons is rigorous.

Weaknesses:

(1) Engram cells are (i) activated by a learning experience, (ii) physically or chemically modified by the learning experience, and (iii) reactivated by subsequent presentation of the stimuli present at the learning experience (or some portion thereof), resulting in memory retrieval. The authors show that exposure to Barnes Maze and the enriched environment-activated semilunar granule cells and granule cells preferentially in the superior blade of the dentate gyrus, and a significant fraction were reactivated on re-exposure. However, physical or chemical modification by experience was not tested. Experience modifies engram cells, and a common modification is the Hebbian, i.e., potentiation of excitatory synapses. The authors recorded EPSCs from labeled and unlabeled GCs and SGCs. Was there a difference in the amplitude or frequency of EPSCs recorded from labeled and unlabeled cells?

(2) The authors studied five sequential sections, each 250 μm apart across the septotemporal axis, which were immunostained for c-Fos and analyzed for quantification. Is this an adequate sample? Also, it would help to report the dorso-ventral gradient since more engram cells are in the dorsal hippocampus. Slices shown in the figures appear to be from the dorsal hippocampus.

(3) The authors investigated the role of surround inhibition in establishing memory engram SGCS and GCs. Surprisingly, they found no evidence of lateral inhibition in the slice preparation. Interneurons, e.g., PV interneurons, have large axonal arbors that may be cut during slicing. Similarly, the authors point out that some excitatory connections may be lost in slices. This is a limitation of slice electrophysiology.

Author response:

Reviewer 1:

(1) I think the article is a little too immature in its current form. I'd recommend that the authors work on their writing. For example, the objectives of the article are not completely clear to me after reading the manuscript, composed of parts where the authors seem to focus on SGCs, and others where they study "engram" neurons without differentiating the neuronal type (Figure 5). The next version of the manuscript should clearly establish the objectives and sub-aims.

Our overarching focus was to identify whether intrinsic physiology and circuit connectivity of SGCs contribute to their unique overrepresentation in neurons labeled as part of a behaviorally relevant dentate engram. Since our systematic analysis of “engram SGCs” did not support the proposal that engram SGCs drive robust feedforward excitation of engram GCs or feedback inhibition of non-engram GCs, we examined an alternative hypothesis that inputs drive recruitment of neurons, regardless of subtype (in figure 5). These are sparsely labeled neurons, with mixed populations of GCs and SGCs undergoing paired recordings. Since the focus of the experiment was input correlation between two simultaneously recorded neurons, we did not report the individual cell types. We regret that this caused confusion and will clarify this issue in the revised manuscript.

(2) In addition, some results are not entirely novel (e.g., the disproportionate recruitment as well as the distinctive physiological properties of SGCs), and/or based on correlations that do not fully support the conclusions of the article. In addition to re-writing, I believe that the article would benefit from being enriched with further analyses or even additional experiments before being resubmitted in a more definitive form.

We would like to note that while we and others have previously reported the distinctive SGC physiology, this study is the first to compare physiological properties of SGCs labeled as part of an engram to unlabeled SGCs. That was the thrust of the data presented which may have been missed and will be emphasized in the revision. Similarly, while others have shown higher SGC recruitment in dentate engrams, we had to validate this in the dentate dependent behaviors that we adopted in this study. We also note that the proportional SGC recruitment in our study, based on morphometric classification, differs from what was reported previously. These aspects of study, which were considered confirmatory, represent the necessary validation needed to proceed with the novel cell-type specific paired recordings and optogenetic analyses of engram neurons presented in subsequent sections of the manuscript. We will emphasize these considerations in the revised manuscript.

Reviewer 2:

(1) The authors conclude that SGCs are disproportionately recruited into cfos assemblies during the enriched environment and Barnes maze task given that their classifier identifies about 30% of labelled cells as SGCs in both cases and that another study using a different method (Save et al., 2019) identified less than 5% of an unbiased sample of granule cells as SGCs. To make matters worse, the classifier deployed here was itself established on a biased sample of GCs patched in the molecular layer and granule cell layer, respectively, at even numbers (Gupta et al., 2020). The first thing the authors would need to show to make the claim that SGCs are disproportionately recruited into memory ensembles is that the fraction of GCs identified as SGCs with their own classifier is significantly lower than 30% using their own method on a random sample of GCs (e.g. through sparse viral labelling). As the authors correctly state in their discussion, morphological samples from patch-clamp studies are problematic for this purpose because of inherent technical issues (i.e. easier access to scattered GCs in the molecular layer).

We regret that there seems to be some confusion about use of a classifier. We did NOT use any automated classifier in this study. All cell type classifications in the study were conducted by experienced investigators examining cell morphology and classifying cells based on established morphometric criteria. In our prior study (Gupta et al., 2020) we had conducted an automated cluster analysis that was able to classify GCs and SGCs as different cell types. The principal components underlying the automated clustering in Gupta et al 2020 were consistent with the major criteria identified in prior morphology-based analyses by us and others (including Williams et al 2010 and Save et al., 2019). To date, in the absence of a validated molecular marker, morphometry from recorded and filled cells or sparsely labeled neurons is the only established method to classify SGCs. This was the approach we adopted, and this will be further clarified in the revisions.

(2) The authors claim that recurrent excitation from SGCs onto GCs or other SGCs is irrelevant because they did not find any connections in 32 simultaneous recordings (plus 63 in the next experiment). Without a demonstration that other connections from SGCs (e.g. onto mossy cells or interneurons) are preserved in their preparation and if so at what rates, it is unclear whether this experiment is indicative of the underlying biology or the quality of the preparation. The argument that spontaneous EPSCs are observed is not very convincing as these could equally well arise from severed axons (in fact we would expect that the vast majority of inputs are not from local excitatory cells). The argument on line 418 that SGCs have compact axons isn't particularly convincing either given that the morphologies from which they were derived were also obtained in slice preparations and would be subject to the same likelihood of severing the axon. Finally, even in paired slice recordings from CA3 pyramidal cells the experimentally detected connectivity rates are only around 1% (Guzman et al., 2016). The authors would need to record from a lot more than 32 pairs (and show convincing positive controls regarding other connections) to make the claim that connectivity is too low to be relevant.

As noted in our discussion, we are fully cognizant that potential SGC to GC connections may have been missed by the nature of slice physiology experiments and made every effort to limit this possibility. As noted in the manuscript, we only analyzed GC/SGC pairs where hilar axon collaterals of the neurons were recovered. We do not claim that SGC to GC/SGC connections are irrelevant, rather, we indicate that these connections, if present, are sparse and unlikely to drive engram refinement. Interestingly, wide field optical stimulation, designed to activate multiple labeled engram neurons and axon terminals including those of SGCs whose somata were outside the slice, did not lead to EPSCs in other unlabeled GCs or SGCs suggesting the lack of robust SGC to GC/SGC synaptic connectivity. While we have previously published paired recordings from interneurons to GCs (Proddutur et al 2023) , we agree that recordings demonstrating the presence of SGC/GC to hilar neuron synapses would serve as an added control in the revised manuscript.

(3) Another troubling sign is the fact that optogenetic GC stimulation rarely ever evokes feedback inhibition onto other cells which contrasts with both other in vitro (e.g. Braganza et al., 2020) and in vivo studies (Stefanelli et al., 2016) studies. Without a convincing demonstration that monosynaptic connections between SGCs/GCs and interneurons in both directions is preserved at least at the rates previously described in other slice studies (e.g. Geiger et al., 1997, Neuron, Hainmueller et al., 2014, PNAS, Savanthrapadian et al., 2014, J. Neurosci), the notion that this setting could be closer to naturalistic memory processing than the in vivo experiments in Stefanelli et al. (e.g. lines 443-444) strikes me as odd. In any case, the discussion should clearly state that compromised connectivity in the slice preparation is likely a significant confound when comparing these results.

We would like to note that our data are consistent with Braganza 2020 study, as we explain below. Moreover, we would like to point out that the demonstration of “feedback inhibition” in the Stefanelli study was NOT in engram or behaviorally labeled neurons nor was it in vivo. As we explain below, the physiological assay in Stefanelli was in slices and in a cohort of GCs with virally driven ChR2 expression. Thus, we are fully confident that our experimental paradigm better reflects a behavioral engram. As noted in response (2, we have previously published paired monosynaptic connections from interneurons to GCs (Proddutur et al 2023) and find the connectivity consistent with published data. However, we agree that recordings demonstrating the presence of SGC/GC to hilar neuron synapses or recruitment of feedback inhibition by focal activation of GCs would serve to allay concerns regarding slice preparation. We also submit that we already discuss the potential concerns regarding compromised connectivity in slice preparations.

Regarding the lack of optically evoked feedback inhibition, we would like to point out that the Braganza 2020 study examined focal optogenetic activation of GCs, where a high density of GCs was labeled using a Prox-cre line. They reported that about 2-4% of these densely labeled cells need to be recruited to evoke feedback IPSCs. Our experimental condition, where ChR2 was expressed in behaviorally labeled neurons, leads to sparse labeling much less than the focal 4% needed to evoke IPSCs in the Braganza study. We do not claim that feedback inhibition cannot be activated by focal activation of a cohort of GCs and even show an example of paired recording with feedback GC inhibition of an SGC. Our conclusion is that the few sparsely labeled neurons during a behavioral episode do not support robust feedback inhibition proposed to mediate engram refinement. We submit that our findings are fully consistent with the sparse GC driven feedback inhibition, and the need to activate a cohort of focal GCs to recruit feedback inhibition, reported in Braganza 2020

Regarding the Stefanelli study, we maintain that our behaviorally relevant in vivo labeling approach is more naturalistic than the DREADD and Channelrhodopsin driven artificial “engrams” generated in the Stefanelli study. Of note, we used cFOS driven TRAP mice to label, in vivo, neurons active during a behavior and then undertook slice physiology studies in these mice a week later. In contrast, the slice physiology data demonstrating putative feedback inhibition in the Stefanelli study (Fig 5) used wildtype mice injected with AAV CAMKII-cre and AAV-DIO-ChR2. Thus, unlike our study, the physiological data demonstrating feedback inhibition in the Stefanelli study was not performed in a behaviorally labeled engram. Apart from the one set of histological experiments using AAV-SARE-GFP to demonstrate increased GFP labeling of SST neurons in behavior, all other data presented in the Stefanelli study are generated based on artificially generated engrams where optogenetic activation or silencing on granule cells was used to manipulate the numbers of neurons active during a task followed by histological analysis of cFOS staining or behaviors. Thus, the physiological experiments in the Stefanelli et al (2016) generated by wide field activation of a large cohort of GCs labeled by focal virally driven ChR2 expression, were similar to wide field optical stimulation studies in the Braganza 2020 study, and were NOT conducted in a behavioral engram. The strength of our study is in the use of a behaviorally tagged engram neurons for analysis and our findings in sparsely labeled neurons are consistent with the reports in Braganza 2020. We will further clarify in our discussion that the data presented in the Stefanelli study do NOT represent a natural behavior generated engram.

(4) Probably the most convincing finding in this study is the higher zero-time lag correlation of spontaneous EPSCs in labelled vs. unlabeled pairs. Unfortunately, the fact that the authors use spontaneous EPSCs to begin with, which likely represent a mixture of spontaneous release from severed axons, minis, and coordinated discharge from intact axon segments or entire neurons, makes it very hard to determine the meaning and relevance of this finding. At the bare minimum, the authors need to show if and how strongly differences in baseline spontaneous EPSC rates between different cells and slices are contributing to this phenomenon. I would encourage the authors to use low-intensity extracellular stimulation at multiple foci to determine whether labelled pairs really share higher numbers of input from common presynaptic axons or cells compared to unlabeled pairs as they claim. I would also suggest the authors use conventional Cross correlograms (CCG; see e.g. English et al., 2017, Neuron; Senzai and Buzsaki, 2017, Neuron) instead of their somewhat convoluted interval-selective correlation analysis to illustrate co-dependencies between the event time series. The references above also illustrate a more robust approach to determining whether peaks in the CCGs exceed chance levels.

We appreciate the comment can provide additional data on the EPSC frequency in individual labeled and unlabeled cells in the revised manuscript. As indicated in the manuscript, we constrained our analysis to cell pairs with comparable EPSC frequency in order to avoid additional confounds in analysis. We have additional experiments to show that over 50% of the sEPSCs represent action potential driven events which we will include in the revised manuscript. We thank the reviewer for the suggestion to explores alternative methods of analyses including CCGs to further strengthen our findings.

(5) Finally, one of the biggest caveats of the study is that the ensemble is labelled a full week before the slice experiment and thereby represents a latent state of a memory rather than encoding consolidation, or recall processes. The authors acknowledge that in the discussion but they should also be mindful of this when discussing other (especially in vivo) studies and comparing their results to these. For instance, Pignatelli et al 2018 show drastic changes in GC engram activity and features driven by behavioral memory recall, so the results of the current study may be very different if slices were cut immediately after memory acquisition (if that was possible with a different labelling strategy), or if animals were re-exposed to the enriched environment right before sacrificing the animal.

As noted by the reviewer, we fully acknowledge and are cognizant of the concern that slices prepared a week after labeling may not reflect ongoing encoding. Although our data show that labeled cells are reactivated in higher proportion during recall, we have discussed this caveat and will include alternative experimental strategies in the discussion.

Reviewer 3:

(1) Engram cells are (i) activated by a learning experience, (ii) physically or chemically modified by the learning experience, and (iii) reactivated by subsequent presentation of the stimuli present at the learning experience (or some portion thereof), resulting in memory retrieval. The authors show that exposure to Barnes Maze and the enriched environment-activated semilunar granule cells and granule cells preferentially in the superior blade of the dentate gyrus, and a significant fraction were reactivated on re-exposure. However, physical or chemical modification by experience was not tested. Experience modifies engram cells, and a common modification is the Hebbian, i.e., potentiation of excitatory synapses. The authors recorded EPSCs from labeled and unlabeled GCs and SGCs. Was there a difference in the amplitude or frequency of EPSCs recorded from labeled and unlabeled cells?

We agree that we did not examine the physical or chemical modifications by experience. Although we constrained our sEPSC analysis to cell pairs with comparable sEPSC frequency, we will include data on sEPSC parameters in labeled and unlabeled cells in the revised manuscript.

(2) The authors studied five sequential sections, each 250 μm apart across the septotemporal axis, which were immunostained for c-Fos and analyzed for quantification. Is this an adequate sample? Also, it would help to report the dorso-ventral gradient since more engram cells are in the dorsal hippocampus. Slices shown in the figures appear to be from the dorsal hippocampus.

We thank the reviewer for the comment. We analyzed sections along the dorso-ventral gradient. As explained in the methods, there is considerable animal to animal variability in the number of labeled cells which was why we had to use matched littermate pairs in our experiments This variability could render it difficult to tease apart dorsoventral differences.

(3) The authors investigated the role of surround inhibition in establishing memory engram SGCs and GCs. Surprisingly, they found no evidence of lateral inhibition in the slice preparation. Interneurons, e.g., PV interneurons, have large axonal arbors that may be cut during slicing. Similarly, the authors point out that some excitatory connections may be lost in slices. This is a limitation of slice electrophysiology.

We agree that slice physiology has limitations and discuss this caveat. As noted in response (2, we have previously published paired monosynaptic connections from interneurons to GCs (Proddutur et al 2023) and find the connectivity consistent with published data. However, we agree that recordings demonstrating the presence of SGC/GC to hilar neuron synapses or recruitment of feedback inhibition by focal activation of GCs would serve to allay concerns regarding slice preparation.