Eyewire II – A connectomic resource for resolving cell types and circuits of the mouse retina
Posted on: 31 August 2026
Preprint posted on 1 June 2026
How does the eye wire together? Eyewire II is a connectomics resource mapping the circuits behind vision
Selected by Phoebe Reynolds
Categories: cell biology, neuroscience
Background:
The sensory organs are crucial for understanding and interacting with the surrounding world, and the mammalian retina serves as a highly structured, well-understood model of sensory processing. Despite the apparent structural simplicity, the network is immensely complex. The mammalian retina has been estimated to contain around 130 distinct cell types. These diverse neurons with morphological, functional, and transcriptomic variety work congruently to encode salient visual features like motion, direction, and contrast (3). Therefore, to truly decode vision, we need to move beyond single-cell identities and map their functional circuits. Large-scale connectomics datasets derived from volumetric electron microscopy (EM) can provide this structural wiring diagram, revealing insights into the retinal network.
However, previous retinal EM datasets have had several limitations (4). Therefore, to bridge these limitations, the authors of this preprint developed Eyewire II. This spans nearly 1 mm² of the mouse retina while combining AI-assisted EM reconstruction with two-photon calcium imaging of the same tissue. It is complemented by comprehensive cell-type identification via extensive morphological classification, resulting in a mammalian retinal connectome that integrates cellular structure, function, and synaptic wiring.
Key findings:
The Eyewire II Resource
The preprint authors were successful in creating the Eyewire II resource. This EM volume covers approximately 1 mm² of the temporo-ventral mouse retina and is roughly 10 – 100 times larger than previous mouse EM retina datasets (Figure 1). This unprecedented scale enables large neurons, such as wide-field amacrine cells that span hundreds of micrometers and were significantly truncated in previous datasets, to be almost fully reconstructed. In addition, the tissue was processed to retain intracellular ultrastructure, with ribbon synapses clearly visible, and allows for the direct identification of synaptic connectivity, bypassing the need to rely on physical proximity or contact area as a synapse indicator.
To handle this large volume, the authors developed a high-throughput pipeline combining deep learning segementation with human proofreading. While complex cells still required manual correction, overall human proofreading effort was drastically reduced. Remarkably, bipolar cells (BCs) required a median of zero edits, dropping their proofreading time to just 2 minutes per cell, while more complex cells such as retinal ganglion cells (RGCs) required only around 39 minutes per cell. Therefore, the authors alongside community tracers successfully proofread over 25,000 out of an estimated 100,000 cells in the volume. However, because proofreaders initially focused on the most complex cells, this is estimated to represent around 35% of the total proofreading effort required.
Figure 1. Dataset overview. Scale comparison showing the 1 mm² area of Eyewire II relative to previous mouse retinal EM volumes, enabling full reconstruction of wide-field amacrine cell arbors. Taken from (Eyewire II, 2026), Fig. 1A. made available under a CC BY 4.0. International license
Morphological classification of retinal cell subtypes
Due to the large quantity of proofreading, the authors were able to demonstrate that morphology alone contained significant, extractable information regarding neuronal identity. Here, the retina as a resource comes into force, as it is known to have distinct morphology subtypes. After reconstruction, neurons were converted into flattened skeletons and analysed using quantitative morphology features, such as dendritic arbor density and branching topology. By exploring different features, the researchers showed that many well-known distinct cell types could be separated into distinct stratification layers and split within a two-dimension t-SNE embedding, resolving biologically relevant morphological variation. Using bipolar cells as a test case, as these are some of the ‘simplest’ cells within the retinal classes, the authors found all known bipolar cell classes (Figure 2). In addition, they used the idea of retinal mosaics (cell types spread evenly across the retina) for independent validation. Using morphological features of the BC types and mosaics, they found that the nearest-neighbour distance strongly correlated with axon terminal size and the expected spatial organisation. Similarly, because Eyewire II overcomes previous truncation issues, the team successfully obtained large, well-structured mosaics for many RGC types, including large alpha types.
Figure 2. Complete Bipolar Cell Retinal Mosaics. Complete spatial mosaics for all 15 bipolar cell types reconstructed across the volume, displaying characteristic, non-overlapping spatial distributions and accurate automated cell-typing. Taken from (Eyewire II, 2026), Fig. 4C-D. made available under a CC BY 4.0 International license.
Anatomy linked to function
One of the most powerful aspects of Eyewire II is the ability to directly link anatomy to function, due to the precise alignment of the recorded ganglion cell layer from prior two-photon imaging with the EM volume. This allows for morphology, connectivity, and function to be analysed congruently for almost 400 cells. Here, they found that while similar morphology generally predicts consistent physiological responses (such as in sustained ON alpha cells), the match is not always consistent. Interestingly, the authors observed that light responses for some cell types, notably ON starburst Acs, differed systematically across the five recording fields. Rather than attributing this to biological variability, they calculated the light exposure for the fields and modelled cumulative activation. They discovered that differences in light adaptation contributed to these variations, showing that neuronal identity does not alone determine function, but the state of the circuit can also alter the neuronal response.
Direct circuit reconstruction
As synapses are clearly visible, Eyewire II excitingly allows for structural circuit reconstruction. Therefore, the authors reconstructed the rod pathway, one of the most clearly understood individual circuits of the retina, as proof of principle (5). They mapped the connections of nine rod BCs, confirming that they mainly provided output to A17 and A2 Acs while receiving reciprocal inhibitory inputs predominantly from A17s (Figure 3). This successfully validated the dataset by accurately reproducing known retinal anatomy. Scaling this approach, they then used a deep learning model that automatically detected approximately 6.5 million ribbon synapses across the volume. This allowed for large-scale analysis of ribbon distribution, confirming that ribbon BCs possess fewer but larger ribbons than cone bipolar cells.
Figure 3. Circuit reconstruction of the rod pathway. 3D reconstructions showing representative ribbon synapses from bipolar cells (A) and conventional inhibitory feedback synapses (B). (C) Reconstructed connectivity graph of the canonical rod pathway, mapping inputs from nine rod bipolar cells onto AII and A17 amacrine cells. Taken from (Eyewire II, 2026), Fig. 8A-C. made available under a CC BY 4.0 International license .
Conclusions and Future directions:
Eyewire II is a detailed, integrative resource that combines structural connectivity with morphology and physiology. The larger dataset ensures that previously truncated neurons can now be fully reconstructed and expands the possible biological questions that can now be addressed, such as systematic identification of cell-type specific connectivity patterns and circuit motifs underlying retinal computations. The resource has a wide range of future applications, including identifying non-canonical cell types, comparing synaptic organisation, and investigating subcellular features such as ribbon synapses or mitochondrial distribution.
However, there are several limitations that must be considered. Firstly, the data is derived from a single temporo-ventral region of one mouse, and functional data is only available for a limited subset of cells, meaning caution must be taken when generalising morphology-function relationships. Secondly, the EM imaging does not yet fully extend into the outer plexiform layer, limiting the study of outer retinal computations. Finally, conventional inhibitory synapses currently are trickier to detect and can require future focus for the development of an automated, network-wide detection.
Why this preprint is interesting:
I find this preprint interesting because it shifts connectomics from a single study into a community resource that can enable future discoveries across the field of retinal neuroscience. Rather than answering one biological question, it provides a dataset to investigate retinal cell types, circuits, and function combined. In addition, the paper highlights how AI and deep learning are changing the scale of connectomics. Reconstructing neurons from EM has traditionally required extensive manual tracing, making large volumes difficult to analyse. Eyewire II demonstrates that effective tools along with human proofreading can make these previously insurmountable, large-scaled projects tracable, showing how AI can expand what is experimentally possible.
The integration of functional calcium imaging data with structural EM is another major strength. Historically, researchers have often had to infer how a neuron’s morphology relates to its functional response. By matching functional recordings to individually reconstructed neurons, Eyewire II provides direct evidence linking neuronal structure and activity, allowing researchers to ask how anatomy and connectivity relate to function.
Overall, I find this paper exciting because it brings together scale, anatomy, and function within a single resource. It provides understanding on not only properties of retinal neurons, but how their circuits are constructed for function.
Questions for the Authors:
- Eyewire II directly links morphology with physiological responses, but understandably functional data is available for only a small subset of the reconstructed cells, and within a single mouse. How representative do you think these cells and their morphology–function relationships are of broader retinal populations and individuals?
- Excitingly, your initial automated segmentation is accurate enough that many cells require little or no proofreading, but the paper does not report a single overall measure of segmentation accuracy. How do you quantify the remaining segmentation errors, in particular split and merge errors, and how confident are you that a proofread neuron is complete?
- I really like your cell-type classification pipeline using morphology. However, where do you think morphology reaches its limits as a definition of neuronal cell type, and how do you see transcriptomic information complementing morphology in future versions of Eyewire II?
- The rod bipolar circuit is a great proof of principle because the connectivity is already well established. Looking forward, which retinal circuit are you most excited that Eyewire II is now uniquely positioned to identify that previous approaches could not?
References:
- H Wässle and B B Boycott. Functional architecture of the mammalian retina. Physiol. Rev.,71(2):447–480, April 1991.
- Joshua Hahn, Aboozar Monavarfeshani, Mu Qiao, Allison H Kao, Yvonne Kölsch, Ayush Kumar, Vincent P Kunze, Ashley M Rasys, Rose Richardson, Joseph B Wekselblatt, Herwig Baier, Robert J Lucas, Wei Li, Markus Meister, Joshua T Trachtenberg, Wenjun Yan, Yi-Rong Peng, Joshua R Sanes, and Karthik Shekhar. Evolution of neuronal cell classes and types in the vertebrate retina. Nature, 624(7991):415–424, 14 December 2023. doi:10.1038/s41586-023-06638-9.
- Antinucci P, Suleyman O, Monfries C, Hindges R. Neural Mechanisms Generating Orientation Selectivity in the Retina. Curr Biol. 2016 Jul 25;26(14):1802-15. doi: 10.1016/j.cub.2016.05.035.
- Crystal L Sigulinsky, Rebecca L Pfeiffer, and Bryan William Jones. Retinal connectomics: A review. Annu. Rev. Vis. Sci., 10(1):263–291, September 2024. doi: 10.1146/annurev-vision-102122-110414.
- H Wässle, U Grünert, M H Chun, and B B Boycott. The rod pathway of the macaque monkey retina: identification of AII-amacrine cells with antibodies against calretinin. J. Comp. Neurol., 361(3):537–551, 23 October 1995. doi: 10.1002/cne.903610315.
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| List by | Helen Zenner |
FENS 2020
A collection of preprints presented during the virtual meeting of the Federation of European Neuroscience Societies (FENS) in 2020
| List by | Ana Dorrego-Rivas |
ASCB EMBO Annual Meeting 2019
A collection of preprints presented at the 2019 ASCB EMBO Meeting in Washington, DC (December 7-11)
| List by | Madhuja Samaddar et al. |
SDB 78th Annual Meeting 2019
A curation of the preprints presented at the SDB meeting in Boston, July 26-30 2019. The preList will be updated throughout the duration of the meeting.
| List by | Alex Eve |
Autophagy
Preprints on autophagy and lysosomal degradation and its role in neurodegeneration and disease. Includes molecular mechanisms, upstream signalling and regulation as well as studies on pharmaceutical interventions to upregulate the process.
| List by | Sandra Malmgren Hill |
Young Embryologist Network Conference 2019
Preprints presented at the Young Embryologist Network 2019 conference, 13 May, The Francis Crick Institute, London
| List by | Alex Eve |