A comprehensive neuroanatomical survey of the Drosophila Lobula Plate Tangential Neurons with predictions for their optic flow sensitivity

  1. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
  2. Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
  3. Department of Neurological Sciences, Larner College of Medicine, University of Vermont, USA

Peer review process

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, and public reviews.

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  • Reviewing Editor
    Albert Cardona
    University of Cambridge, Cambridge, United Kingdom
  • Senior Editor
    Albert Cardona
    University of Cambridge, Cambridge, United Kingdom

Reviewer #1 (Public Review):


This study describes all tangential neurons of the lobula plate (LOPs) of the fruit fly Drosophila melanogaster. Importantly, this is done in a complete manner, for the first time in any species. This means that for the first time, all neurons involved in transmitting wide-field optic flow information to the central brain are known. Exploiting known structure-function relations in these neurons (which are based on solid physiological data in different species of flies), the authors provide estimates of the physiological properties of all described neurons. Combined with transmitter predictions of these cells, this yields a full account of what information about wide-field motion is available to the central fly brain in order to derive behavioral commands from. The study goes one step further and includes anatomical descriptions and physiological property predictions for all major downstream target cells of LOPs.

Main strengths:

The paper is exceptional in three ways. First, it is the first comprehensive account of all tangential neurons of the lobula plate of an insect. This now provides the ground truth for similar studies in other insects. In particular, these results will allow neurons emerging in other species to be confidently described as novel/different from Drosophila, if they were not found in the current study. This is a major change from previously, when confidence in the non-existence of neuronal cell types in this system was impossible, as that system was not fully described.

Second, the rigorous prediction of physiological characteristics (flow-field encoding) in all anatomically described neurons provides a solid basis for system-wide modeling of optic flow encoding in Drosophila. Importantly, the presented physiological predictions include the downstream partner cells of the LOPs in the central brain, neurons for which only very few physiological descriptions exist, but which are essential for transforming optic flow input into behavioral outputs. This paper therefore opens a path towards closing the gap between sensory processing and behavior not only for a few identified and well-studied pathways, but for all wide-field motion processing that exists in a species.

Third, the connectomics work is not only based on one individual sample, but incorporates two EM volumes, analyzed with two different methods (manual tracing and auto segmentation/proofreading), using interhemispheric correspondence and inter-individual correspondence to validate the obtained neuron catalogue. Additionally, light microscopical data was used to validate the EM data. All of this provides exceptional levels of confidence in the presented results.

Main weaknesses:

While the authors compare their results with data from both larger flies and other work in Drosophila, a recent paper (Henning et al 2022) that presented novel data on the distribution of preferred motion directions in the fly lobula plate is not mentioned. This is unfortunate, as the claim of that paper is that the lobula plate contains six instead of four main tuning directions, both at the level of LOPs and T4/T5 input cells - a claim that could likely be directly confirmed or dismissed, or at least incorporated in the data presented in the current study. How would the flow-field predictions change if the data from Henning et al on T4 neurons was used as an input for the modeling rather than the classic four tuning directions?

While the authors nicely perform comparison to other fly species, a more general discussion of how the found cells relate to other insects, e.g. cells known from bees (e.g. Honkanen et al., 2023) or older work from locusts, could give the data more general relevance. While the comparison can likely not be done on a cell type level, given that the structure of the lobula complex is very different between those insects, the types of projections found and their physiologies, i.e. the overall patterns of how wide field motion is sent to the central brain, might be comparable and informative for highlighting general principles of motion processing.

Reviewer #2 (Public Review):


In this study, Zhao, Nern, et al. investigate a population of neurons in the optic lobe of Drosophila melanogaster that process optic flow, relative motion between the insect's eyes and its environment that is generated during flight and provides useful information to the fly about its own self-motion. Although a sample of these Lobula Plate Tangential (LPT) neurons has been studied across Diptera in prior work, the full population has not been exhaustively and thoroughly cataloged in a single species, limiting our understanding of how LPT tuning properties across the population convey features of optic flow fields relevant to downstream motor regions.

Through extensive manual reconstructions in a fly electron microscopy volume, the authors of this study identify 58 LPT neurons in the fruit fly encompassing previously studied Horizontal and Vertical cells and novel cells that have not been previously characterized. Using the detailed anatomy of each cell and knowledge of upstream T4/T5 selectivity, the authors derive the predicted motion pattern map (PMPM) of each neuron. To understand how optic flow field tunings of individual LPTs align with global optic flow patterns flies are expected to encounter during flight such as translation and rotation, the authors compute the average angular difference between each PMPM and idealized rotation and translation optic flow fields. The authors also map individual LPTs to their counterparts in a second fly brain to explore LPT-LPT connectivity and downstream connectivity to central brain neuropils. They find that distinct groupings of LPTs have diverse downstream connectivity patterns and that downstream neurons align more closely to global optic flow fields that are expected during flight. This study is a valuable resource to researchers studying motion vision in the insect brain and is of interest to researchers studying sensorimotor processing by providing hypotheses for how optic flow information is integrated downstream to guide fly behavior.


A key strength of this study is the thoroughness with which the authors comprehensively identify individual LPT neurons in the FAFB volume. They not only conduct an impressive number of careful manual reconstructions to recover individual LPTs, but they also attempt, and often succeed, to map each individual neuron to its counterpart in light microscopy, studies across Diptera, and available auto-segmented connectome datasets such as FlyWire, FAFB-FFN1, and Hemibrain. The authors are similarly thorough when surveying individual LPT properties such as neurotransmitter identities, in some cases using multiple datasets to reconcile ambiguous neurotransmitter predictions. The care with which the complete LPT population has been identified establishes this study as a useful resource for future studies of insect motion.

In addition to providing a comprehensive catalog of individual LPTs, the authors also contextualize their findings within broader sensorimotor circuitry by considering connectivity between LPTs and from LPTs to downstream regions. Exploration of structure in downstream connectivity suggests that optic flow information is directed to various central brain neuropils through specific groups of LPTs. With some additional analyses, these results broaden the scope of this study by providing useful hypotheses for sensorimotor circuit organization.


A novel method introduced in this study is the derivation of individual LPT-predicted motion pattern maps (PMPMs) using T4 preferred directions and LPT morphology. Although this method underlies core findings in this study, such as alignment to global optic flow fields and properties of downstream integration, aspects of the methods used to derive PMPMs are not explained sufficiently well, particularly in the main text. For example, in the Methods, the authors briefly describe the process of computing a weighted sum of T4 preferred directions to obtain the PMPM for each LPT, but a detailed understanding of these preferred directions combined is missing in Figure 2 or the associated descriptions in the main text. It is also not clear how PMPMs are derived in cases where LOP layer coverages are overlapping (for example VS 13-1 in Figure 3) to yield smooth PMPMs. In addition, it is not clear how the PMPMs of bilateral LPTs such as LPT-45 and LPT-50 in Figure 4 were integrated to compute downstream target composite PMPMs. Finally, all the PMPMs were derived from the T4 preferred direction that relies on the ommatidial viewing directions ("Eyemap") introduced in Zhao et al. 2022. It is also important for the current study to give an indication of how sensitive their results are to possible inaccuracies in this map and derived T4/T5 direction selectivities.

Although the authors explore some features of connectivity from LPT to downstream partners (Figure 6), there is a lack of reconciliation of these findings with individual LPT properties explored earlier in the study, such as those presented in Figures 2-4. In that sense, there is a disconnect between the two parts of the manuscript (and a missed opportunity). For instance, an important follow-up analysis would be to use knowledge about LPT-LPT connectivity to better predict effective PMPMs of LPTs taking into account network effects. This extension would lead to a better understanding of how LPT-LPT interactions shape optic flow responses in the LOP. In addition, in Figure 6 Supplement 2 (which I recommend to move to the main figures), the authors show that LPTs can be grouped together based on similarity of output connectivity (Panel B-D) and that this structure corresponds to output synapses located in different groups of central brain neuropils. However, they do not attempt to explicitly link these groupings with individual LPT PMPMs, alignment to global optic flow patterns, LPT layer enervation, cell morphologies, and input connectivity patterns. Such an analysis would be an important step to bring the manuscript together and to get a better understanding of the organization of the whole system.

Reviewer #3 (Public Review):


The fruit fly visual system has provided a powerful context in which to investigate fundamental questions in neural development, phototransduction, and systems neuroscience. Of recent interest is motion processing, particularly how visual motion cues are estimated locally, and then pooled to derive behaviorally meaningful signals. Many of these pooling operations have been shown to take place in the wide-field neurons in the lobula plate, cell types that have been explored using electrophysiological recordings for more than 50 years in a variety of Diptera. However, our understanding of the diversity and connectivity of these cells remains incompletely understood, and is of interest to many.

In this context, Reiser and colleagues describe the anatomy and connectivity of the complete set of Lobula Plate Tangential neurons in Drosophila, using a careful and systematic reconstruction of the FAFB dataset. Leveraging a previous study of retinal geometry, combined with their characterization of the anatomical inputs to the elementary motion detectors, T4 and T5, they then predict the motion sensitivities of each cell, their neurotransmitter identities, and map the connections of many of these cells into the central brain and contralateral optic lobe.


The quality of the connectomic analysis is exceptional, and the quantitative analysis that links connectivity to function is rigorous and impressive. This paper will be an important resource for the community.


Some of the findings could be better linked to previously published work in this field, and there may be a minor limitation to the predicted optimal motion axes, given one of the simplifying assumptions made.

  1. Howard Hughes Medical Institute
  2. Wellcome Trust
  3. Max-Planck-Gesellschaft
  4. Knut and Alice Wallenberg Foundation