Awake responses suggest inefficient dense coding in the mouse retina

Article activity feed

1. 

   Version published to 10.7554/elife.78005 on eLife

   Nov 3, 2023
2. 

   eLife

   Sep 7, 2023

   Author Response

   Evaluation Summary:

   The manuscript shows that retinal ganglion cell light responses in awake mice differ substantially from those under two forms for anesthesia and previously attained ex vivo recordings. This difference is central to our understanding of how ganglion cell responses relate to behavior. There are a few technical issues and issues about how the work is presented that could be strengthened.

   We thank the reviewers for their constructive comments. We have addressed all the issues, and added substantially more data and analysis results in the revised manuscript, further supporting our findings that awake responses are larger, faster, and more linearly decodable in the mouse retina than those responses under anesthesia or ex vivo.

   Reviewer #1 (Public Review):

   This paper compares output signals from the …

   Author Response

   Evaluation Summary:

   The manuscript shows that retinal ganglion cell light responses in awake mice differ substantially from those under two forms for anesthesia and previously attained ex vivo recordings. This difference is central to our understanding of how ganglion cell responses relate to behavior. There are a few technical issues and issues about how the work is presented that could be strengthened.

   We thank the reviewers for their constructive comments. We have addressed all the issues, and added substantially more data and analysis results in the revised manuscript, further supporting our findings that awake responses are larger, faster, and more linearly decodable in the mouse retina than those responses under anesthesia or ex vivo.

   Reviewer #1 (Public Review):

   This paper compares output signals from the mouse retina in three conditions: awake mice, anaesthetized mice, and isolated retinas. The paper reports substantial differences, particularly between awake and either of the other conditions. Retinal signaling has been well studied using ex vivo preparations, with an assumption that the findings from those studies can be carried over to how the retina operates in vivo. The results from this paper at a minimum indicate a need to be cautious about that assumption. There are several technical issues that need testing or further explanation, and several issues about the presentation that could be clarified.

   Spike sorting

   The paper does not describe any control analyses that test for contamination in spike sorting. These are needed to evaluate the work.

   We have reported the details of our spike sorting procedure in the revised manuscript (Data Analysis section in Methods and Figure 1). In short, single-units were identified by clustering in principal component space, followed by manual inspection of spike waveform (triphasic as expected from axonal signals; e.g., revised Figure 1F-H; Barry, 2015) as well as auto- and cross-correlograms (minimal inter-spike interval above 1 ms for a refractory period; e.g., revised Figure 1I-K). A small fraction of visually responsive cells (20/282, awake; 21/325, isoflurane; 1/103, FMM) had a small fraction of interspike intervals below 2 ms; but, whether or not including them in the analysis did not affect our main conclusions.

   Light levels

   The paper argues that differences in light level cannot account for the results. According to the methods, light levels were about two-fold higher at the retina in array recordings as compared to the front of the eye for in vivo recordings. The main text indicates that they differ less, it's not clear why the numbers in the main text and methods are different. Aside from this issue, this comparison does not consider the loss of light between the front of the eye and the retina. It is crucial that the paper provide a more detailed description of light levels. This should include converting those light levels to units that include the spectral output of the light source used (e.g. to isomerizations per rod or cone per second).

   The maximum light intensity of our in vivo setup was 31.3 mW/m2 (with 15.9 mW for UV LED and 15.4 mW/m2 for blue LED). Following the suggestion by the reviewer, we calculated the photon flux on the mouse retina in vivo by taking into account the loss of light by the eye optics. In short, assuming 50% and 68% transmittance at 365 nm and 454 nm, respectively (Jacobs & Williams 2007), the pupil size of 1 mm and the retinal diameter of 4 mm with the stimulus covering 73° in azimuth and 44° in elevation, we obtained the photon flux on the mouse retina in vivo as 3.81×103 and 6.64×103 photons/s/μm2 for UV and blue light, respectively. Assuming a total photon collecting area of 0.2 μm² for cones and 0.5 μm² for rods (Nikonov et al. 2006), and a relative sensitivity of rods, S- and M-cones to be [UV, Blue]=[25, 60], [90, 0], [25, 60]%, respectively (Jacobs & Williams 2007), we then estimated the photoisomerization (R) rate as: 2.5×103 R/rod/s, 0.7×103 R/S-cone/s, and 1.0×103 R/M-cone/s.

   In contrast, the maximum light intensity of the in vitro set up was 36 mW/m2 as reported in Vlasiuk and Asari (2021). The photon flux on the isolated retina was then estimated to be around 9×104 photons/s/μm2 (under the assumption that the white light from a CRT monitor is centered around 500 nm). Assuming the sensitivity of rods, S- and M-cones to be 40, 2 and 40%, respectively, we then obtained 4×104 R/rod/s, 2×103 R/S-cone/s, and 4×104 R*/Scone/s.

   Thus, the light intensity level was about ten times larger for the in vitro recordings than for the in vivo recordings. The amount of light reaching the retina in the awake condition should also be somewhat smaller than that under anesthesia due to pupillary reflexes. Past studies suggest that the darker the stimulus is, the slower the kinetics is and the smaller the response is for RGCs in an isolated retina (Wang et al 2011). Thus, the light intensity difference cannot simply account for the higher firing and faster kinetics in the awake condition than ex vivo or in the anesthestized condition.

   We have revised the manuscript accordingly.

   Comparison with other work

   The authors accurately point out that there is not much prior work on retinal outputs in awake animals. The paper, however, minimally describes the work that does exist. The Hong et al. (2018) paper, in particular, should be discussed. There are several differences between the results of that paper and the present paper. These include the fraction of recorded cells that are DS cells, and the maintained firing rates (though this does not appear to be studied systematically in Hong et al.).

   In the discussion section of the revised manuscript, we clarified connections to the existing studies on the retinal activity in vivo. To our knowledge, none of the past studies provided descriptive statistics on the awake RGC response properties (Hong et al., 2018; Schroeder et al., 2020; Sibille et al., 2022). Nevertheless, consistent with our study, we can see high baseline activity in the reported examples from C57BL6 mice (Figure 3C, Schroeder et al. 2020; Figure S7h, Sibille et al. 2022).

   Hong et al (2018), in contrast, reported somewhat different as pointed out by the reviewer. Firstly, they found a relatively low baseline activity in RGCs of albino CD1 mice. We think that this is likely due to general impairments of the vision/retina associated with albinism. While equipped with normal electroretinogram signals, CD1 mice showed no optomotor response and a reduced number of rods (Abdeljalil et al 2005; Brown et al 2007). This suggests a certain level of retinal dysfunction in these mice. Secondly, Hong et al (2018) reported a higher fraction of direction-selective RGCs in their recordings (>50% at a DS index threshold of 0.3). This is even higher than one would expect from anatomical and physiological studies ex vivo on BL6 mice (about a third; Sanes and Masland, 2015; Baden et al., 2016; Jouty et al 2013). Besides the effect of albinism, we think that this overrepresentation of DS cells in Hong et al (2018) arose as a consequence of the low baseline activity. As discussed above, the higher the baseline activity, the lower the DS/OS index by definition (Eq.(3) in Methods). Indeed we found much more cells with high DS/OS index values in our anesthetized data than in awake ones (42-54% vs 17% at an index value threshold of 0.15; Figure 2), even though these recordings were done in the same experimental set up.

   A related issue is that there are a few comparisons of ex vivo RGC responses with behavioral sensitivity. Smeds et al. (2019) is one example. More generally, the long-standing observation that dark-adapted sensitivity approaches limits set by Poisson fluctuations in photon absorption, and that prior RGC measurements are consistent with this result, is hard to explain if the RGCs are firing at high spontaneous rates under these conditions. RGC responses will certainly change with light level, but this merits discussion in the paper.

   As the reviewer pointed out, the retina may employ different coding principles under different light levels. In a scotopic condition, ex vivo studies reported a high tonic firing rate for OFF RGC types (~50 Hz, OFF sustained alpha cells in mice; Smeds et al 2019; ~20 Hz, OFF parasol cells in primates; Ala-Laurila and Rieke, 2014), while a low tonic firing for ON cell types (<1Hz for both ON sustained alpha in mice and ON parasol in primates). These ON cells were shown to be responsible for light detection by firing in the silent background, hence compatible with the sparse feature detection strategy. In contrast, our recordings were done in a high mesopic / low photopic range where both rods and cones are supposedly active. Unlike the scotopic condition with rod vision, we then found high firing in awake recordings in general, indicating that no visual feature can be readily detectable as brief firing events in the silent background. To explore the implications of such firing patterns on visual coding, we took a modelling approach in the revised manuscript. We found that a latency-based temporal code was not preferable in the awake condition (Figure 7); and that a linear decoder worked significantly better with the population responses in the awake condition to capture the presented random fluctuation of the light intensity (Figure 8). While we have not tested any behavioural relevance in our study besides correlation to locomotion/pupil size, it is then possible that the retina may work in different modes under different light intensity regimes (Tikidji-Hamburyan et al 2015).

   We clarified these points in the revised discussion section.

   Sampling bias

   The paper argues that sampling bias is not likely to contribute substantially to the results because of the wide variety of cell types recorded (line 431). This does not seem like a particularly strong argument, especially given the large degree of overlap in the distributions of most quantities across preparations. The argument about many cell types could be made more strongly if the distributions were completely separated, but that is not the case.

   We cannot deny the presence of a sampling bias in our datasets, and as the reviewer pointed out, we made comparisons only at a population level, but not at the level of individual cells or cell-types. However, the anesthetized and awake recordings were done with the same recording setup and techniques, and thus subject to the same sampling bias. Hence, the difference in the RGC response properties between these conditions cannot be explained by the sampling bias per se.

   Sensitivity

   The firing rates in response to 10% contrast sinusoids are quite low, as are the maximal firing rates for high contrast sinusoids. Relatedly, the modulation produced by the noise stimuli, particularly for the array recordings, is weak. This raises concerns about the health of some of the preparations.

   To our knowledge, in vivo contrast responses reported here were comparable to ex vivo data in previous reports (mouse, Jouty et al 2018, Pearson and Kerschensteiner 2015; rat, Jensen 2017, 2019). Likewise, the static nonlinearity and its upper bound for ex vivo responses were comparable between this study and previous reports (Santina et al. 2013; Kerschensteiner et al 2008; Cantrell et al 2010; Trapani et al 2022).

   We also examined batch effects in the response to the noise stimuli. We found certain variabilities across preparations in each recording condition, but not to the extent to discard any particular data as an obvious outlier (Figure 6 – figure supplement 1). While it is difficult to tell the health status of preparations retrospectively, we thus believe that the effects were negligible.

   Efficient coding

   Sparse firing is not a universal property of retinal ganglion cell responses. Primate midget RGCs, for example, have pretty high maintained firing rates as shown in many past studies. Mouse RGCs have also been reported to operate in a mode similar to the high firing rate On cells reported here (Ke et al. 2014). A more balanced discussion of this past work is needed.

   As the reviewer pointed out, some retinal ganglion cells show high firing under certain conditions. In a scotopic condition, for example, OFF cells have high firing rates, while ON cells fire virtually nothing unless a light stimulus is presented (Ke et al 2014; Smeds et al 2019). At the behavoural level, a single-photon detection above chance level nevertheless relies on the information from the ON but not the OFF pathway (Smeds et al 2019). Thus, the sparse coding framework still works as a valid strategy here, if not universal.

   This is, however, very different from what we report here. In a high-mesopic/low-photopic light level, we found a general increase of firing across all cell categories in the awake condition, compared to the anesthetized or ex vivo recordings (Figures 3 and 6). While this lowers information transfer rate (bits/spike; Figure 7), we found that the awake responses were more linearly decodable than the responses in the other conditions (Figure 8). We also ran a simulation and showed that a latency-based temporal code is not preferable for the awake responses (Figure 7 – figure supplement 1). These results suggest that the retina in awake condition is in favor of a rate code, though we have not tested all light levels or any behavioural relevance here.

   We clarified these points in the revised manuscript.

   Role of eye movements

   Could eye movements be at least partially responsible for the differences in response properties? Specifically, small fixational eye movements might produce a constantly varying input that could modulate firing.

   As described above (Essential Review item #2), eye movements were rarely observed during the head-fixed awake recordings. Eliminating those events from the analysis did not change our overall conclusions, and thus their contributions should be minimal in this study. It should also be noted that we mainly used full-field stimulation, and thus microsaccades should not substantially affect the amount of light impinging on the retina. We clarified these points in the revised manuscript.

   Reviewer #2 (Public Review):

   The technical achievements presented in the manuscript represent a tour de force, as optical tract recordings in awake mice have only rarely been done before. The substantial number of neurons recorded in both awake and anaesthetized conditions form a precious and worldwide unique dataset. However, since the recordings represent a non-standard approach, it would be, in my view, highly beneficial to show more details about the success of the method. How did the authors post-hoc identify electrode contacts located in the optical tract, how did the spike waveforms look like, what were the metrics of spike sorting quality, etc.

   We added more details about our recording and analysis methods in the revised manuscript. Below are answers to the reviewer’s specific questions:

   • The probe was coated with a fluorescent dye (DiI stain) and its location was verified histologically after the recordings (Figure 1E).

   • Spike waveforms typically had a triphasic shape (e.g., Figure 1F-H) as expected from axonal signals (Barry, 2015).

   • Single-units were identified by clustering in principal component space, followed by manual inspection of spike shape as well as auto- and cross-correlograms. Most units had a minimum interspike interval above 2 ms (93%, awake; 94%, isoflurane; 99%, FMM); and no units had the interspike intervals below 1 ms for a refractory period (e.g., Figure 1I-K), except for 1 (out of 103) for FMM-anesthetized recordings.

   We then selected visually responsive cells (SNR>0.15; see Eq.(1) in Methods) for the analyses.

   The authors go a long way in characterising the functional response properties of the recorded neurons and relating them to previous ex-vivo recordings. Based on the responses they find, the authors claim that they identified "... a new response type [which] likely emerged due to high baseline firing in awake mice". Regarding this claim, how do the authors rule out that it corresponds to any of the previously described cell types? For instance, the very sharp transient or brief modulations by the contrast part of the stimulus might have been missed in previous classifications based on calcium responses (e.g. Baden et al. 2016), where a number of cell types seem to respond equally strong to grey and white and have an elevated response throughout the sinusoidal modulation of contrast. I acknowledge that the authors touch upon the possibility that the newly described OFFsuppressive ON cells correspond to a known cell type in the discussion, but I would recommend changing the phrasing of the results to avoid potential misunderstandings.

   We agreed with the reviewer and revised the manuscript accordingly. Here we have two possibilities. Firstly, as the reviewer pointed out, this kind of response dynamics could be overlooked previously because of a difference in the recording modality (Ca imaging; Baden et al 2016) or clustering methods (Jouty et al 2019). Secondly, these cells may belong to one of the cell-types described in the past ex vivo studies, but exhibited distinct response dynamics in vivo as an emerging property of the awake condition. This is an interesting topic to pursue in future studies.

   The manuscript makes the interesting suggestion that "the retinal output characteristics [...] observed in vivo, [...] provide a completely different view on the retinal code". Given that this conclusion would change the way we should think about and do retinal neuroscience, in my view, the authors should take a few more steps to quantitatively demonstrate the implications of their findings on retinal coding, e.g. how much lower is the information transmitted per spike, how much does a temporal code based on spike timing suffer with the latencies observed in vivo. If the authors could quantify through computational modelling approaches the consequences of the observed differences, they might also be able to revise their title / main message, i.e. that "Awake responses SUGGEST inefficient dense coding in the mouse retina".

   To explore functional implications of our findings, we performed three more analyses as suggested by the reviewer. Specifically,

   1. We showed that the information transmitted per spike was significantly lower in awake condition, while the total information rate was comparable (Figure 7).

   2. We tested the performance of a linear decoder applied on the firing rate in response to full-field noise, and showed that it worked significantly better for the awake population responses (Figure 8).

   3. We simulated RGC responses to a full-field contrast change at different intensities in different conditions, and showed that a latency coding did not work well with awake responses, compared to ex vivo or anesthetized responses (Figure 7 – figure supplement 1).

   These results strengthened our conclusion that awake response dynamics were different from anesthetized or ex vivo responses, all arguing against the sparse efficient coding principles at least at a light level we examined. We nevertheless kept the title as is because we have not explored the retinal coding properties per se. Our main claim stays on the visual response characteristics of retinal outputs in awake mice.

   Reviewer #3 (Public Review):

   The manuscript by Boissonnet, Tripodi, and Asari compares retinal ganglion cell (RGC) light responses in awake mice (recorded in the optic nerve) with those under two forms for anaesthesia and previously attained ex vivo recordings. This is a well motivated study looking at a question that is really critical to the field.

   The presentation is generally clear and compelling. My suggestions are relatively minor and aimed at improving an already very strong article.

   1. More cells in the awake condition would help strenghten the conclusions. Only 51 cells are reported, and mouse RGCs comprise more than 40 different types. The authors are well aware of the possible confound of sampling bias, and the best way to mitigate this issue in this experimental paradigm is simply to record more cells. The anesthsia conditions each have about 100 cells, which is better.

   We made substantially more recordings in the awake condition, reaching 282 cells (in 15 animals) in total in the revised manuscript. This does not yet allow for a full cell-type classification as in the past ex vivo studies. Nevertheless, we did our best to broadly classify visual responses, and showed that the overall conclusions remained the same: awake RGCs had higher baseline firing and faster response kinetics in general. For details, see above our response to the Essential Revision item #1.

   1. It took me longer than it should have (had to look up the previous paper cited) to figure out that the ex vivo comparison data were recorded at 37{degree sign}C. This is an important detail since most ex vivo recordings are at 32{degree sign}C. The authors should make this clear in the text and perhaps say something in the Discussion about comparisons to the larger body of literature of ex vivo studies at 32{degree sign}.

   We are aware that most ex vivo studies on the retina were performed at 32 °C, which is lower than physiological body temperature (37 °C). However, the temperature of the ocular surface is around 37 °C (Vogel et al 2016), suggesting that the retina should operate at 37 °C in vivo. This is why we decided to perform ex vivo experiments at 37 °C in our previous study (Vlasiuk and Asari, 2021), allowing us to make a fair comparison between the ex vivo and in vivo recordings.

   We clarified the point in the revised manuscript.

   1. Direction and orientation selectivity should be separated in Fig. 2 and not combined into the confusing term "motion sensitive." Motion sensitivity has another meaning in the literature for RGCs that respond preferentially to moving over static stimuli without direction or orientation preference (Kuo et al., 2016; Manookin et al., 2018)

   We agree with the reviewer. In the revised manuscript, we separated the direction and orientation selective cells (Figure 2), and avoided the term “motion sensitive.”

   1. While I am certainly sympathetic to the argument that the RGC spike code is "inefficient" in the sense that it does not conform to efficient coding theory (ETC), I think it's oversimplified to claim that the present data is a key argument against ETC. Plenty of ex vivo data has already shown ETC to be incomplete at best, and misguided at worst, since it includes the implicit assumption that image reconstruction is the retina's objective function (or even that the experimenter has any idea what that objective function is). For example, OFF sustained alpha (OFF delta in guinea pig) RGCs are not quite sparse feature detectors even ex vivo, and they seem to be optimized to transmit contrast with high SNR (Homann and Freed, 2017). In general, the enormous coverage factor of the RGC population seems to make ETC untenable to begin with, as discussed in (Schwartz, 2021) and elsewhere. I realize that there are still people attached to simplistic forms of ETC as a key principle of retinal computatiion, so I am not asking for the authors to completely remove this angle. Rather, a more nuanced treatment of the issue both in the introduction and the discussion is warranted.

   We totally agree that we are not the first to argue against the efficient coding principles in the retina (Schwartz, 2021). The main argument in this study is that certain aspects of the RGC activity are distinct in an awake condition, such as the baseline firing and response kinetics, and thus we cannot simply translate our knowledge obtained from ex vivo studies into awake animals. To explore the implications on retinal computations, we showed in the revised manuscript that 1) awake responses have a comparable total information transfer rate (in bits per second; Figure 7A) but are less efficient (i.e., lower bits per spikes; Figure 7B); 2) awake responses are not in favor of a latency-based temporal code (Figure 7 – figure supplement 1); and 3) a linear decoder worked significantly better with awake responses (Figure 8), even though an image reconstruction is not necessarily the objective function of the retina. These results point out a need to rethink about retinal function in vivo, including the efficient coding theory.

   We thank the reviewer for the suggestion, and revised the manuscript accordingly.

   References

   Homann, J., and Freed, M.A. (2017). A mammalian retinal ganglion cell implements a neuronal computation that maximizes the SNR of its postsynaptic currents. Journal of Neuroscience 37, 1468-1478.

   Kuo, S.P., Schwartz, G.W., and Rieke, F. (2016). Nonlinear Spatiotemporal Integration by Electrical and Chemical Synapses in the Retina. Neuron 90, 320-332.

   Manookin, M.B., Patterson, S.S., and Linehan, C.M. (2018). Neural Mechanisms Mediating Motion Sensitivity in Parasol Ganglion Cells of the Primate Retina. Neuron 97, 13271340.e4. Schwartz, G.W. (2021). Retinal Computation (Academic Press).

   Read the original source
3. 

   eLife

   Apr 11, 2022

   Evaluation Summary:

   The manuscript shows that retinal ganglion cell light responses in awake mice differ substantially from those under two forms for anesthesia and previously attained ex vivo recordings. This difference is central to our understanding of how ganglion cell responses relate to behavior. There are a few technical issues and issues about how the work is presented that could be strengthened.

   (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

   Read the original source
4. 

   eLife

   Apr 11, 2022

   Reviewer #1 (Public Review):

   This paper compares output signals from the mouse retina in three conditions: awake mice, anaesthetized mice, and isolated retinas. The paper reports substantial differences, particularly between awake and either of the other conditions. Retinal signaling has been well studied using ex vivo preparations, with an assumption that the findings from those studies can be carried over to how the retina operates in vivo. The results from this paper at a minimum indicate a need to be cautious about that assumption. There are several technical issues that need testing or further explanation, and several issues about the presentation that could be clarified.

   Spike sorting
   The paper does not describe any control analyses that test for contamination in spike sorting. These are needed to evaluate the work.

   Light levels
   

   Reviewer #1 (Public Review):

   This paper compares output signals from the mouse retina in three conditions: awake mice, anaesthetized mice, and isolated retinas. The paper reports substantial differences, particularly between awake and either of the other conditions. Retinal signaling has been well studied using ex vivo preparations, with an assumption that the findings from those studies can be carried over to how the retina operates in vivo. The results from this paper at a minimum indicate a need to be cautious about that assumption. There are several technical issues that need testing or further explanation, and several issues about the presentation that could be clarified.

   Spike sorting
   The paper does not describe any control analyses that test for contamination in spike sorting. These are needed to evaluate the work.

   Light levels
   The paper argues that differences in light level cannot account for the results. According to the methods, light levels were about two-fold higher at the retina in array recordings as compared to the front of the eye for in vivo recordings. The main text indicates that they differ less, it's not clear why the numbers in the main text and methods are different. Aside from this issue, this comparison does not consider the loss of light between the front of the eye and the retina. It is crucial that the paper provide a more detailed description of light levels. This should include converting those light levels to units that include the spectral output of the light source used (e.g. to isomerizations per rod or cone per second).

   Comparison with other work
   The authors accurately point out that there is not much prior work on retinal outputs in awake animals. The paper, however, minimally describes the work that does exist. The Hong et al. (2018) paper, in particular, should be discussed. There are several differences between the results of that paper and the present paper. These include the fraction of recorded cells that are DS cells, and the maintained firing rates (though this does not appear to be studied systematically in Hong et al.).
   A related issue is that there are a few comparisons of ex vivo RGC responses with behavioral sensitivity. Smeds et al. (2019) is one example. More generally, the long-standing observation that dark-adapted sensitivity approaches limits set by Poisson fluctuations in photon absorption, and that prior RGC measurements are consistent with this result, is hard to explain if the RGCs are firing at high spontaneous rates under these conditions. RGC responses will certainly change with light level, but this merits discussion in the paper.

   Sampling bias
   The paper argues that sampling bias is not likely to contribute substantially to the results because of the wide variety of cell types recorded (line 431). This does not seem like a particularly strong argument, especially given the large degree of overlap in the distributions of most quantities across preparations. The argument about many cell types could be made more strongly if the distributions were completely separated, but that is not the case.

   Sensitivity
   The firing rates in response to 10% contrast sinusoids are quite low, as are the maximal firing rates for high contrast sinusoids. Relatedly, the modulation produced by the noise stimuli, particularly for the array recordings, is weak. This raises concerns about the health of some of the preparations.

   Efficient coding
   Sparse firing is not a universal property of retinal ganglion cell responses. Primate midget RGCs, for example, have pretty high maintained firing rates as shown in many past studies. Mouse RGCs have also been reported to operate in a mode similar to the high firing rate On cells reported here (Ke et al. 2014). A more balanced discussion of this past work is needed.

   Role of eye movements
   Could eye movements be at least partially responsible for the differences in response properties? Specifically, small fixational eye movements might produce a constantly varying input that could modulate firing.

   Read the original source
5. 

   eLife

   Apr 11, 2022

   Reviewer #2 (Public Review):

   The technical achievements presented in the manuscript represent a tour de force, as optical tract recordings in awake mice have only rarely been done before. The substantial number of neurons recorded in both awake and anaesthetized conditions form a precious and world-wide unique dataset. However, since the recordings represent a non-standard approach, it would be, in my view, highly beneficial to show more details about the success of the method. How did the authors post-hoc identify electrode contacts located in the optical tract, how did the spike waveforms look like, what were the metrics of spike sorting quality, etc.

   The authors go a long way in characterising the functional response properties of the recorded neurons and relating them to previous ex-vivo recordings. Based on the responses they find, …

   Reviewer #2 (Public Review):

   The technical achievements presented in the manuscript represent a tour de force, as optical tract recordings in awake mice have only rarely been done before. The substantial number of neurons recorded in both awake and anaesthetized conditions form a precious and world-wide unique dataset. However, since the recordings represent a non-standard approach, it would be, in my view, highly beneficial to show more details about the success of the method. How did the authors post-hoc identify electrode contacts located in the optical tract, how did the spike waveforms look like, what were the metrics of spike sorting quality, etc.

   The authors go a long way in characterising the functional response properties of the recorded neurons and relating them to previous ex-vivo recordings. Based on the responses they find, the authors claim that they identified "... a new response type [which] likely emerged due to high baseline firing in awake mice". Regarding this claim, how do the authors rule out that it corresponds to any of the previously described cell types? For instance, the very sharp transient or brief modulations by the contrast part of the stimulus might have been missed in previous classifications based on calcium responses (e.g. Baden et al. 2016), where a number of cell types seem to respond equally strong to grey and white and have an elevated response throughout the sinusoidal modulation of contrast. I acknowledge that the authors touch upon the possibility that the newly described OFF-suppressive ON cells correspond to a known cell type in the discussion, but I would recommend changing the phrasing of the results to avoid potential misunderstandings.

   The manuscript makes the interesting suggestion that "the retinal output characteristics [...] observed in vivo, [...] provide a completely different view on the retinal code". Given that this conclusion would change the way we should think about and do retinal neuroscience, in my view, the authors should take a few more steps to quantitatively demonstrate the implications of their findings on retinal coding, e.g. how much lower is the information transmitted per spike, how much does a temporal code based on spike timing suffer with the latencies observed in vivo. If the authors could quantify through computational modelling approaches the consequences of the observed differences, they might also be able to revise their title / main message, i.e. that "Awake responses suggest inefficient dense coding in the mouse retina".

   Read the original source
6. 

   eLife

   Apr 11, 2022

   Reviewer #3 (Public Review):

   The manuscript by Boissonnet, Tripodi, and Asari compares retinal ganglion cell (RGC) light responses in awake mice (recorded in the optic nerve) with those under two forms for anaesthesia and previously attained ex vivo recordings. This is a well motivated study looking at a question that is really critical to the field.

   The presentation is generally clear and compelling. My suggestions are relatively minor and aimed at improving an already very strong article.

   1. More cells in the awake condition would help strengthen the conclusions. Only 51 cells are reported, and mouse RGCs comprise more than 40 different types. The authors are well aware of the possible confound of sampling bias, and the best way to mitigate this issue in this experimental paradigm is simply to record more cells. The anesthsia …

   Reviewer #3 (Public Review):

   The manuscript by Boissonnet, Tripodi, and Asari compares retinal ganglion cell (RGC) light responses in awake mice (recorded in the optic nerve) with those under two forms for anaesthesia and previously attained ex vivo recordings. This is a well motivated study looking at a question that is really critical to the field.

   The presentation is generally clear and compelling. My suggestions are relatively minor and aimed at improving an already very strong article.

   1. More cells in the awake condition would help strengthen the conclusions. Only 51 cells are reported, and mouse RGCs comprise more than 40 different types. The authors are well aware of the possible confound of sampling bias, and the best way to mitigate this issue in this experimental paradigm is simply to record more cells. The anesthsia conditions each have about 100 cells, which is better.

   2. It took me longer than it should have (had to look up the previous paper cited) to figure out that the ex vivo comparison data were recorded at 37{degree sign}C. This is an important detail since most ex vivo recordings are at 32{degree sign}C. The authors should make this clear in the text and perhaps say something in the Discussion about comparisons to the larger body of literature of ex vivo studies at 32{degree sign}.

   3. Direction and orientation selectivity should be separated in Fig. 2 and not combined into the confusing term "motion sensitive." Motion sensitivity has another meaning in the literature for RGCs that respond preferentially to moving over static stimuli without direction or orientation preference (Kuo et al., 2016; Manookin et al., 2018)

   4. While I am certainly sympathetic to the argument that the RGC spike code is "inefficient" in the sense that it does not conform to efficient coding theory (ETC), I think it's oversimplified to claim that the present data is a key argument against ETC. Plenty of ex vivo data has already shown ETC to be incomplete at best, and misguided at worst, since it includes the implicit assumption that image reconstruction is the retina's objective function (or even that the experimenter has any idea what that objective function is). For example, OFF sustained alpha (OFF delta in guinea pig) RGCs are not quite sparse feature detectors even ex vivo, and they seem to be optimized to transmit contrast with high SNR (Homann and Freed, 2017). In general, the enormous coverage factor of the RGC population seems to make ETC untenable to begin with, as discussed in (Schwartz, 2021) and elsewhere. I realize that there are still people attached to simplistic forms of ETC as a key principle of retinal computation, so I am not asking for the authors to completely remove this angle. Rather, a more nuanced treatment of the issue both in the introduction and the discussion is warranted.

   References
   Homann, J., and Freed, M.A. (2017). A mammalian retinal ganglion cell implements a neuronal computation that maximizes the SNR of its postsynaptic currents. Journal of Neuroscience 37, 1468-1478.
   Kuo, S.P., Schwartz, G.W., and Rieke, F. (2016). Nonlinear Spatiotemporal Integration by Electrical and Chemical Synapses in the Retina. Neuron 90, 320-332.
   Manookin, M.B., Patterson, S.S., and Linehan, C.M. (2018). Neural Mechanisms Mediating Motion Sensitivity in Parasol Ganglion Cells of the Primate Retina. Neuron 97, 1327-1340.e4.
   Schwartz, G.W. (2021). Retinal Computation (Academic Press).

   Read the original source
7. 

   Version published to 10.1101/2022.02.15.480512 on bioRxiv

   Feb 17, 2022