PCOMPBIOL-D-24-01569

Filter-based models of suppression in retinal ganglion cells: comparison and generalization across species and stimuli

PLOS Computational Biology

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PLOS Computational Biology

Daniele Marinazzo

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PLOS Computational Biology

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At this stage, the following Authors/Authors require contributions: Neda Shahidi, Fernando Rozenblit, Mohammad H. Khani, Helene M. Schreyer, Matthias Mietsch, Dario A. Protti, and Tim Gollisch. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #1: In this study the authors compare the performance of several models of suppression at explaining quantitatively how ganglion cells respond to random fluctuations of contrast, for a stimulus that is spatially uniform. They find that suppressive and divisive models are better than a spike history filter model. This is a solid study with interesting results.

My concerns:

1) My understanding is that the feedback model is just implementing the kind of spike history filter usually found in GLM models (Pillow et al). There are other models that perform at this, and have been used before. For example, the gain control model used in Berry et al, 1999, but also in Chen et al, 2013,2014 ; Leonardo and Meister, etc. This gain control model has a divisive component. It seems necessary to compare the performances to this type of model too.

2) The main result is that the divisive and the subtractive models are not doing exactly the same thing. Their performance depend on stimulus frequency, a result I find very interesting. It would be nice to have more intuition about why this is the case.

3) A caveat of this study, acknowledged by the authors, is that everything they do is restricted to spatially uniform stimuli. The lack of substantial difference between the divisive and subtractive model could come from this. I don’t expect the authors to address the issue of spatially detailed stimuli, but a bit more discussion on this would be helpful, especially given the other works in the field.

4) Related to this, the discussion contains very little discussion about other works in the field (eg ref 1 of the paper). This is an issue to better identify the novelty of this work.

5) Some parts of the paper are a bit unclear. In particular, the paragraph that includes the sentence: In the divisive model, on the other hand, the shape of the nonlinearity in the suppressive branch led to a unilateral, rectified suppression with essentially no sensitivity to filter activations near to or smaller than zero

is hard to understand for me and deserves a proper explanation. The first panel of the figure 3 might benefit from being expanded. The non-linearity is learned but this is not obvious from the text. Also, the NL learning seems to hit the bounds imposed. What happens if they are pushed back ?

Reviewer #2: This is a thorough and well-executed exploration of different classes of combined excitation and suppression in neurons. The work focuses on retinal ganglion cells and taps three species (axolotl, mouse, marmoset) to add a comparative angle to the study. Models are well-characterized and compared for two main classes of input stimuli - full-field flicker, chirps, and contrast sweeps. Results show that divisive suppression is the most versatile motif, with good reproduction of responses across the widest range of stimuli. Subtractive suppression was only the clear winner in slow axolotl cells. A large number of recordings were made, the methods are sound and well-explained, and the text flows well and is well-referenced.

Major comment:

The only way this paper could be found lacking is in a clearer or more thoroughly explored functional consequence to the findings. The Discussion certainly hints at some ideas surrounding the differences in these types of suppression, but more could be made about:

1) the differences between axolotl and mouse/primate: is this a result of "more processing" happening in the amphibian retina compared to mammalian species? Does this connect to behavioral repertoire or environmental statistics in some potentially interesting way?

2) how divisive suppression might be better suited to dynamic environments. Some of the Discussion touches on adaptive mechanisms, but a bit more could be said here.

This points could better expand the reach of the work beyond the retinal community, though the work stands as a useful and solid set of insights for cortical neurophysiologists. A more explicit connection to non-retinal processing in the Discussion could be useful.

Minor comment:

On page 10, is "motives" meant to be "motifs"?

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Dear Gollisch,

We are pleased to inform you that your manuscript 'Filter-based models of suppression in retinal ganglion cells: comparison and generalization across species and stimuli' has been provisionally accepted for publication in PLOS Computational Biology.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Matthias Helge Hennig, Ph.D.

Academic Editor

PLOS Computational Biology

Daniele Marinazzo

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have addressed all my questions.

Reviewer #2: I am very pleased with the extensive revisions made to the text. The authors have addressed all of my concerns.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: None

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No