Reviewer #3:

This study by Pipitone et al. combines SBF-SEM microscopy with quantitative proteomics and lipidomics to explore chloroplast differentiation. Authors describe that chloroplast biogenesis occurs in a first phase of structure establishment with thylakoid biogenesis, followed by a second phase of chloroplast division. The images and 3D reconstructions are beautiful, the quantitative data are novel, and their integration offers a new perspective into the seedling de-etiolation process, a model system for physiological and molecular studies. However, in my opinion some aspects need to be better explained and significantly improved.

• In lines 276-282, the authors write: "After 8h of illumination (T8), we observed decreased abundance of only one protein (the photoreceptor cryptochrome 2, consistent with its photolabile property) and increased levels of only three proteins, which belonged to the chlorophyll a/b binding proteins category involved in photoprotection (AT1G44575 = PsbS; AT4G10340= Lhcb5; AT1G15820= Lhcb6". This is striking, as many well studied proteins change in abundance during the first hours of de-etiolation. Actually, looking into the data set with the quantification data for the ~5,000 proteins, it appears that many proteins do show significant changes between T0 and T8. For example PORA and ELIP, changes that are also reflected in figure 6A.

• Related to the above, well known proteins for example phyA and HY5, that undergo drastic changes in abundance when etiolated seedlings are first exposed to light, do not show changes in T4,T8 and T12 relative to T0 in the proteomics data set. This raises questions about the proteomic approach (sensitivity of the method?) or the experimental setup. Could authors please comment on this? I feel that validation of the proteomics approach is critical, especially taking into account the central conclusion that "the first 12h of illumination saw very few significant changes in protein abundance".

• Lines 570-572: A reference is needed. Also, it is mentioned that PSII appears later than PSI, which does not seem to match the observation that PSII proteins appear earlier than PSI, or that the surface area occupied at early time points by PSII is greater than the one occupied by PSI. Please check.

• Are the calculations of thylakoid surface expansion over time consistent with previous available data using tomography? Please include.

• In the introduction, authors could include mention of the massive transcriptional reprogramming that takes place during de-etiolation. In addition, I think that comparison of the proteomics data with the transcriptomic changes during de-etiolation (well described in the literature) would allow further understanding of the distinct phases proposed. For the chloroplast proteins already present in the dark, how does this correlate with expression of the corresponding genes?

Reviewer #3:

The present manuscript focuses on a subpopulation of layer 5 neurons in medial and lateral entorhinal cortex and its functional connections to target neurons in layers 2, 3 and 5. The authors show a difference in LVb-to-LVa connectivity between MEC and LEC. The results suggest that the entorhinal output circuit via LVb-to-LVa is present primarily in LEC.

The work relies on and is made possible by a newly described transgenic mouse (TG) where LVb neurons can be labeled and stimulated with light. The authors showed that these neurons are largely co-labeled with PCP4, a marker for LVb. They compared the apical dendritic extent from TG labeled cells (LVb) and Nac retrogradely labeled cells (LVa) in medial and lateral EC. The intrinsic electrophysiological properties of LVa and LVb neurons were measured and used for PCA showing segregation according to sublayer and region. The axonal distribution and translaminar local connections of LVb neurons form the TG mice were then examined. Cells were recorded in vitro and filled with biocytin, both from MEC and LEC, with multiple cells in the same slice, documented with high quality images. The study of the LVb translaminar connectivity via a direct comparison of postsynaptic responses in neurons in different layers in the same slice is the gold standard for this type of functional connectivity analysis. There is also an investigation of mixed excitatory-inhibitory postsynaptic response sequences, and evidence for a dorso-ventral gradient in LVb-to-LVa connectivity in MEC is given.

The study combines TG mice, immunolabeling, retrograde labeling, morphological analysis and in vitro electrophysiology with optogenetic photo-stimulation. While it builds on already published work by the same group and others, by comparing the local target neurons of LVb in MEC and in LEC, the manuscript provides a unique contribution to the literature on the laminar circuit organization in the Entorhinal Cortex. In view of the central position of this area in the hippocampal memory systems of the rodent brain, these results are of interest to a broader neuroscience audience. It is also a nice example of a bottom-up approach, where data on the entorhinal translaminar connectivity may influence and constrain theories of hippocampal-cortical processing.

Major Comments:

1) Almost all TG labelled neurons are positive for PCP4 but not so vice versa, only 45.9 and 30.P% of PCP4 + neurons in LEC and MEC are labeled in the TG mouse (page 5) leaving open the possibility that the TG mouse labels a (specific?) subset of LVb neurons. Did you test whether TG labeled LVb cells co-localize with Ctip2 ?

2) The direct comparison of translaminar connectivity of LVb neurons is very convincing. But if your main conclusion (title) concerns the difference of LVb-to-LVa connectivity between MEC and LEC, it would have been more appropriate to test that in the same slice. While the data strongly support conclusions on the laminar differences of LVb connectivity, the evidence for differences in LVb-to-LVa connectivity between MEC and LEC is a bit weaker and more indirect.

3) Postsynaptic responses (in mV) in LEC are about twice as high in amplitude as in MEC (Fig. 4E vs Fig 5E), across all layers. Please discuss possible reasons, and possible impact on the circuit function. Is the probability to initiate action potentials higher in LEC ?

4) Give the onset latencies of postsynaptic excitatory potentials induced by LVb photostimulation. Are latencies monosynaptic? Or also polysynaptic? Ideally this could be tested by applying a cocktail of TTX-4-AP.

5) Figure 4 S3, Fig 5 S2. Analysis of inhibition. What is the cut-off criteria to say inhibition is present or not? It might be more appropriate to give the I/E ratio.

Reviewer #3:

The manuscript explores ageing-associated changes in the Drosophila escape-response (Giant Fiber, GF) circuit and the circuits converging onto the GF. This a convenient system amenable to detailed physiological analyses and the authors made a good effort in extracting a large amount of useful information using a wide range of electrophysiological readouts. The authors identified several physiological parameters that are potentially useful for indexing ageing progression in flies such as ID spike generation and ECS-evoked seizure threshold. The host lab is well-known for its expertise in the field of GF physiology; consequently, the experiments were done with a high level of technical competence and presented (mostly) in a clear and informative manner. There is, however, one major issue that could restrict the usefulness of the data presented in the manuscript (please, see major comment 1).

Major comments:

1) Standards for conducting ageing studies in Drosophila and other model systems have gone significantly up in the last ~15 years following experimental evidence that genetic background can (and does) have a significant effect on the outcome of 'ageing' experiments (see Partridge and Gems, Nature, 2007). Today, 'backcrossing' relevant lines into a reference wild-type strain multiple times (to remove any second-site mutations) is a gold standard for virtually all ageing studies in Drosophila. Furthermore, this approach is being widely adopted even in the studies investigating physiological properties in developing flies (for example, in Imlach, Cell, 2012, the authors obtained very different electrophysiological results after 'isogenizing' the genetic background via backcrossing, and concluded that "the previous finding may have been due to a second site mutation"). As this important step is not mentioned in either the main text or in 'Methods' section, it is reasonable to conclude that the authors did not perform this step prior to conducting the experiments. Recent papers, one of which was referenced by the authors (Augustin et al PloSBiol 2017 and NeuroAging 2018) repeatedly demonstrated a significant, age-associated increase in the short-response (TTM and DLM) latency in the GF circuit following a strong stimulation of the GF cell bodies in the brain. It is likely that these age-related changes in the GF circuit remained undetected in the flies with non-uniform genetic background likely used in this work. The same problem affects the paper (Martinez, 2007) referenced by the authors throughout the manuscript.

It is difficult to say which of the findings reported here are most affected by the variability in the genetic background, but any kind of correlation between the lifespans (Figure 1B) and physiological parameters should be taken with a high dose of scepticism.

2) The manuscript is entirely 'phenomenological' in the sense that it does not investigate the causes of the observed physiological changes. The manuscript (with minor exceptions) does not discuss the possible reasons behind the functional readouts or speculate about what makes the (sub)circuits differentially susceptible to the effect of ageing. For example, when mentioning the effects of temperature and Sod mutation on the fly physiology, the authors limit their comments to generic and obvious statements such as 'oxidative stress exerts strong influences differentially on some of the physiological parameters and the outcomes are distinct from the consequences of high-temperature rearing'. Some of the possible questions the authors could ask are: could changes in the kinetics of relevant ion channels explain some of the results obtained under different temperatures; could the previously demonstrated effect of ROS on voltage-gated sodium channels explain some of the Sod1 phenotypes, etc?

Reviewer #3:

This manuscript presents data in support of a model whereby neurons harboring a YAC bearing 128 CAG repeats of the Huntingtin protein show disrupted Ca2+ handling via the endoplasmic reticulum in axons and nerve terminals. Unfortunately, my enthusiasm for the manuscript is relatively low for the following reasons:

1) It is unclear at this point whether YAC-based models are really appropriate since they lack the appropriate genomic control of transcription. This may be why for example one of the stronger phenotypes, the increase in mEPSC frequency, is greatest at DIV14 and diminishes some by DIV18 and is absent by Div21. This of course is not the same trajectory of the disease impairment itself. The authors speculate that the reversal of the phenomenology with older cultures may be from degeneration but there is no data to back up this claim. There seems little reason at this point in time not to use HD knockin mice.

2) The analysis for synapse "density" (Supplement) was only carried out at Div18, a time point where the impact of the YAC is already diminished. Unfortunately, the high degree of variability associated with measuring all possible puncta on a dendrite is not likely to easily uncover what amounts to a ~30% change in mEPSC frequency. I am not convinced therefore that the data in figure 1 cannot be explained in part by synapse density.

3) The underlying physiological perturbations driven by the YAC are deciphered almost entirely using pharmacological approaches, many of which are in themselves ambiguous in interpretation. Ryanodine is a complex drug as it potentiates receptors at low doses and blocks at higher doses. Confounding all of this is the fact that the literature has incubation times that span tens of minutes to hours (and not specified in this manuscript). I was disappointed that the authors did not at least repeat the pharmacology experiments with different aged neurons (DIV14, 18, 21). If disrupted ER Ca or RyR function lies at the basis for the change in spontaneous exocytosis, the pharmacology experiments should at the very least track this phenomenology. Similarly high/inhibiting doses of ryanodine should presumably lead to opposite effects, and this at the very minimum should have been done in the control and YAC neurons.

4) The reported changes in resting Ca2+ are highly suspect. The use of ionomycin should drive the sensor to saturation, and then from the saturated value and knowledge of the dynamic range of the probe, affinity constant, and the Hill coefficient, one can extrapolate back to what the resting concentration is. This has been done with GCaMPs in the past and predicts resting values in the 100-150 nM range (in broad agreement with many previous Ca measurements in live cells). In the experiments here the ionomycin never convincingly reaches saturation, as the response merely rises and recovers making the data uninterpretable.

5) The central problem with the approach here is that there is a lot of inference with what happens to ER Ca2+ in the YAC cells but no direct measurements were made. There are a number of genetically-encoded probes that have been used in the last 5 years to examine the ER Ca in neurons (CEPA1ER, ER-GCCaMP-150, D1ER), and experiments using one of these probes should be done to inform the science here.

6) The experiments claiming suppression of AP-evoked release are very difficult to interpret as there is no control over the stimulus itself. The authors simply rely on removing TTX to let APs fire randomly, something that will be driven significantly by network density, synaptic connectivity, and the balance of excitatory versus inhibitory drive in the cultures. The authors should simply study evoked release by stimulating the neurons expressing physin-GCaMP6m directly and examining the response sizes in YAC versus control neurons.

7) iGlusnFr is a potentially powerful tool to assess glutamate release, but to be interpretable it too needs to be treated in a quantitative fashion. The size of the signal will be proportional to the fraction of GlusnFr present on the cell surface and the amount of glutamate released. If for some reason expression of the CAG repeat led to a smaller fraction of expressed sensor reaching the surface of the neuron, this would artificially lead to changes in apparent DF/F. In order to use this probe in an interpretable fashion the authors need to carry out experiments whereby they correct for the surface fraction of the probe across experiments.

As it stands, this manuscript reports largely hard to interpret phenomenology owing to the narrow tool kit they have applied to the problem (mostly pharmacology and inference).

Other important details:

• There is no mention in the methods (or anywhere else) regarding the temperature of the experiments.
• A more meaningful graphical representation would be showing median +/- IQR rather than mean +/- SD.
• It would be helpful to show the effects of inhibition of RyR on WT (confirm ability to decrease mEPSC by inhibiting RyR) and YAC128 (additional proof that RyR contributes to YAC128 pathology).
• The data on single bouton physin-GCaMP6m need to be extracted for all boutons and then reported as fraction of boutons showing the fluctuations. As it stands, it is unclear if there is a selection bias.
• What was the percentage decrease in iGluSnFr signal at the last time point?

Reviewer #3:

The authors have conducted a very challenging study. The paper is clearly written and the topic of neural function under anesthesia is interesting. However, a significant limitation is that many of the analyses presented here do not provide clear insights into the processes the authors are studying.

-A key issue is that the authors aim to predict who is more or less sensitive to general anesthesia. However, each individual subject was given a different target plasma concentration of propofol, based on clinical scoring. So any difference in behavior may reflect different dosing rather than different behavioral sensitivity to a particular drug concentration.

-The interpretation of increased functional connectivity is challenging in the context of anesthesia, which modulates vessel dilation and systemic physiology. These analyses would benefit from additional information about the fMRI signal characteristics, e.g. amplitude and physiological signals.

-Fig. 3 is used to portray comparisons of wakefulness vs. sedation, implied in the text, but does not include direct statistical tests of the difference between the two conditions, and contrasting p<0.05 with p>0.05 does not indicate a significant difference. The suggestion of reduced cortical responses to auditory stimuli makes sense given that the participants are sedated, but the analysis does not seem to provide information about which aspect of auditory processing is modulated by sedation.

-The statements about response time not being mediated by age may reflect an underpowered study, as age is a strong modulator of anesthetic sensitivity and one group has an n=6.

-While many interesting MRI studies can be done with quite small n, depending on the question being asked (e.g. Midnight Scan Club, high-resolution individual studies), this study aims to conduct structure-based predictions of individual differences in behavior. This type of analysis requires more than the n=6 slow responders used for Fig. 5, as there are many other features that likely vary in a group this small. I appreciate that the authors have conducted a very challenging study, and it is not easy to collect more data, but while many interesting analyses can be done on this type of data, this is not an appropriate sample size for assessing GMV-individual differences associations. Larger samples sizes or within-subjects analyses are needed for robust GMV effects.

-Cluster correction method in 'Analyses of fMRI data' should be specified (and checked, Eklund et al.). The precise statistical method used to assess FDR corrected activity correlations with individual subject response times is not clear; it seems that the ANOVA resulted in non-significant results that are nevertheless being reported as differences using Hedges d?

-The presented evidence does not sufficiently support the authors' conclusion that they "provided very strong evidence that individual differences in responsiveness under moderate anaesthesia related to inherent differences in brain function and structure within the executive control network, which can be predicted prior to sedation.". I would commend the authors on their interesting and challenging experiment, and recommend refocusing the analyses.

Reviewer #3:

Jack and colleagues report that SARS-CoV-2 interacts with RNA to form phase-separated liquid compartments, similar to P bodies and nucleoli, shown here as blobs. The authors then perturbed the system in numerous ways, showing that: i) different nucleic acids give rise to different blobs; ii) that protein cross-linking and mass spec suggests that the phase-separated N is in a different tertiary or quaternary conformation than the soluble N; iii) that some N domains (e.g., PLD, R2) are important for blob formation, particularly when the protein is phosphorylated (by an unknown kinase); and iv) some small molecules can affect the number and size of the blobs. Overall, this story is at a very early stage phenomenology and lacks clear demonstration of physiological relevance. Certainly, the claim that "nilotinib disrupts the association of the N protein into higher order structures in vivo and could serve as a potential drug candidate against packaging of SARS-CoV-2 virus [sic] in host cells" ought to be tested - it would be easy enough to do, though I don't think this would complete the story.

Major comments:

1) Figure 1 is difficult to interpret with the information provided. In panel A, the colors seem to be important, but readers are not given a clue as to what. In panel B, how were the Y axes calculated? What are we really looking at in Figs. 1C and D? Were these on glass slides? Plastic? Was the surface coated, passivized, or otherwise derivatized in any way? What kind of microscope was used? What do the white signals (blobs) come from? Is there a fluorescent label involved? Is this phase contrast? In panel D, please include a buffer only control (no protein) to demonstrate blobs are not simply a buffer artefact. Finally, what N:RNA molar ratios were used in this Figure?

2) For the polymeric RNAs, what were the average chain lengths?

3) In describing Figure 1, the authors state "The shapes of these asymmetric structures were consistent with remodeling of vRNPs into 'beads on a string', as observed by cryoEM." This is wishful thinking. I see blobs of different shapes, but there is no way to know whether these represent N protein "beads" on RNA "strings." Reference 6 cited in the manuscript and showing "beads on a string" model has a scale bar of 50 nm = 0.05 µm, and even there, the N:RNA complex is very obscure.

4) My greatest concern of this work is that no information was provided about the N protein that was used for the in vitro studies. How pure was it? What steps were taken to remove co-purifying nucleic acids? Was it monodisperse? Aggregated? Please include DLS data and show silver stained SDS-PAGE.

5) Similarly, how did the mutant forms of N (Fig. 3A) behave? Were they properly folded? Did the authors check them by CD or SEC? And what concentrations of mutant proteins were used? Without these data, the rest of Fig. 3 is uninterpretable.

1. B. Could the authors please explain what the numbers on the Y axis are and how they were calculated. Also, their disorder prediction predicts dimerization regions to be highly disordered, would they consider a problem with the prediction method?

7) C, D, E what is the N: RNA molar ratio?

8) Could the authors please explain the calculation method used to calculate the % surface area covered by droplets?

9) Fig, 4A and B. Why is [N] so low? In other experiments the authors usually used 18.5 µM, whereas here the concentration was 7.8 µM, almost invisible blobs as observed in other figures provided by the authors (and below ksat, or very close to it).

10) Fig, 4C. What is 1.5 M N RNA? [N] is set to 57.6 µM, much higher than in Fig. 4 A-B assays. Is there a reason?

11) Fig. 4D is missing control cells transfected with GFP only (no N).

Reviewer #3:

The Aizenman lab has previously demonstrated the utility of Xenopus tectum as a model to examine neuronal, circuit and behavioral manifestations of VPA treatment, a teratogen associated with autism spectrum disorder in humans. In Gore et al., they demonstrate that the deficits induced by VPA treatment, including enhanced spontaneous and evoked neuronal activity, are blocked by pharmacological or morpholino based inhibition of MMP9. Inhibition of MMP9 also reverses the effects of VPA treatment on seizure susceptibility and the startle habituation response. Over-expression of MMP9 pheno-copies the effect of VPA, and inhibition of MMP9 in single tectal neuronal blocks the expression of experience-dependent structural plasticity. The results are convincing and add mechanistic insight into circuit and behavioral dysfunction induced by VPA signaling, as well as an expansion of the repertoire of plasticity mediated by MMP9 signaling.

Minor points:

-The time course for the introduction of VPA and MMP9 inhibitors should be reiterated in the results section.

-Fig 1 Please report the number (or %) of tectal neurons in which MMP9 was over-expressed following whole-brain electroporation.

-Does MMP9 transfection change the E/I ratio, as previously reported for VPA?

-Does VPA or MMP9 inhibition change the initial large amplitude/short latency evoked response?

Figure 2: please report statistics for total number of barrages or barrage distribution across experimental groups (latter also for Fig 3).

Figs 3 and 5: The presentation of the immunoblots should clarify if raw or normalized (to Ponceau Blue) data were quantified.

Fig 4: Please report a post hoc comparison following the repeated measures ANOVA

Fig 5: Total growth and growth rates could also be included in the results section.

Minor comments: -The discussion considers a broad range of potential targets of MMP9, including cell surface receptors, growth factors, adhesive proteins, and extracellular matrix components, many of these are left out of the abstract and introduction.

-The statement of page 6 "Increased synaptic transmission observed in MMP9 over-expression tectal neurons is consistent with dysfunctional synaptic pruning" appears at odds with a body of literature in mouse hippocampus, included many papers cited in the discussion, demonstrating the role of MMP9 in spine elongation, synaptic potentiation and synapse maturation.

Reviewer #3:

Lang and col. used mouse models to address the impact of the light and dark cycle and of myeloid conditional knockout of BMAL1 and CLOCK in susceptibility to endotoxemia. As expected, mortality rate increased in animals housed in constant darkness (DD). The mortality rate remains dependent on the circadian time in DD mice and, more intriguingly, independent on myeloid BMAL1 and CLOCK, with persistent circadian cytokine expression but loss of circadian leukocyte count fluctuations. The study is mainly descriptive without mechanistic explanation, which leaves the reader a bit frustrated.

1) Please revise the result section and the legends (for example legends of Figures 3 and 5) to explicitly mention whether experiments with conditional knockouts were performed with LD or DD mice.

2) Line 15 and 80. Saying that DD mice show a "three-fold increased susceptibility to LPS" is true for very specific conditions only, and should not be used as a general statement.

3) Line 99-. Please be more precise in describing cytokine levels (for example, in LD, TNF peaks at ZT10, IL-18 at ZT14 or ZT22 but not ZT18, and IL-10 but not IL-12 peaks at ZT14).

4) Line 105-106. Referring to Figure 1E, it is not straightforward for the reader to understand what is meant by "free-running and entrained" conditions.

5) Figure 2C and 3G. There is a substantial decreased mortality in LysM-Cre+/+ versus WT mice. Any explanation?

6) Figure 5 depicts a protocol with LD and DD mice. Yet, it seems that only DD mice were analyzed. Is that correct? LD mice should be analyzed in parallel as controls.

7) Figure 5 and Sup Figure 5. There are huge differences in leukocytes counts between LysM-Cre+/+ and WT mice. Without being exhaustive, LysM-Cre+/+ display much more macrophages in bone marrow, spleen and lymph nodes, DCs in lymph nodes, NK cells in spleen and lymph nodes at both CT8 and CT20. This is very puzzling and questions about the pertinence of these "control" mice. Additionally, one might expect from these observations that LysM-Cre+/+ mice are more sensitive to endotoxemia, which is not the case (point 5).

8) Line 257. The effect of IL-18 is not totally surprising, since both detrimental and protective effects of the cytokine have been reported in the literature. This could be briefly mentioned.

9) Sup Figure 5A. The gating strategy has to be shown for each organ, separately.

10) Sup Figure 5D. The peritoneal cavity contains not only different macrophage populations with different inflammatory properties, but also different B cell populations including anti-inflammatory B-1a cells (plus NK cells, DCs...). Considering that LPS is injected i.p., more thorough analyses of the peritoneal cavity should be performed to properly interpret results of cytokine and mortality.

11) It is not clear whether endotoxemia was addressed with BMAL1 and CLOCK myeloid conditional knockout mice kept LD. Since time-of-day dependent differences in mortality were much less in DD mice (line 74), we probably expect only marginal differences in DD mice.

Reviewer #3:

This work provides a computational model to explain the change of grid cell firing field structure due to changes in environmental features. It starts from a framework in which self-motion information and those related to external sensory cues are integrated for position estimation. To implement this theoretical modeling framework, it examines grid cell firing as a position estimate, which is derived from place cell firing representing sensory inputs and noisy, self-motion inputs. Then, it adapts this model to explain experimental findings in which the environment partially changed. For example, the rescaling of an environment leads to a disruption of this estimation because the sensory cue and self-motion information misalign. Accordingly, the model describes mechanisms through which the grid cell position estimate is updated when self-motion and hippocampal sensory inputs misalign in this situation. The work also suggests that coordinated replay between hippocampal place cells and entorhinal grid cells provide means to realign the sensory and self-motion cues for accurate position prediction. Probably the strongest achievement of this work is that it developed a biology-based Bayesian inference approach to optimally use both sensory and self-motion information for accurate position estimation. Accordingly, these findings could be useful in related machine learning fields.

Major comment:

The work seems to provide a significant advance in computational neuroscience with possible implications to machine learning using brain-derived principles. The major weakness, however, is that it is not written in a way that the majority of neuroscientists (who do not work in this immediate computational field) could benefit from. It often does not explain why/how it came to some conclusions or what those conclusions actually mean - for example, right in the introduction, "This process can also be viewed as an embedding of sensory experience within a low-dimensional manifold (in this case, 2D space), as observed of place cells during sleep". It also does not provide a sufficiently detailed qualitative explanation of the mathematical formulations or what the model actually does at a given condition. So my recommendation would be to carefully rewrite the work to make it readable for a wider audience. I also fear that the work also assumes significant a priori neuroscience information, so people in machine learning fields would not benefit from this work in its current form either.

It is not clear why place cell input was chosen as sensory input. Place cells also alter their firing with geometry, sensory and contextual changes. Although grid cells require place cell input, place cell firing represents more than just sensory inputs. In fact, they may be more sensitive to non-sensory behavioral, contextual changes than grid cells. Moreover, like grid cells, they are sensitive to self-motion inputs, e.g., speed-sensitivity and, at least in virtual environments, head-direction sensitivity. This point would deserve a detailed discussion.