MoGen: Detailed Neuronal Morphology Generation via Point Cloud Flow Matching
Authors: Franz Rieger
Jan-Matthis Lueckmann
Viren Jain
Michal Januszewski
Venue: ICLR 2026 Poster (2026)
Keywords: neuroscience
connectomics
neuron reconstruction
generative modelling
point clouds
flow matching
Primary area: applications to neuroscience & cognitive science

Abstract: Biological neurons come in many shapes. High-fidelity generative modeling of their varied morphologies is challenging yet underexplored in neuroscience, and crucial for the subfield of connectomics. We introduce MoGen (Neuronal Morphology Generation), a flow matching model to generate high-resolution 3D point clouds of mouse cortex axon and dendrite fragments. This is enabled by an adaptation that injects local geometric context into a scalable latent transformer backbone, allowing for the generation of high-fidelity, realistic samples. To assess MoGen's generation quality, we propose a dedicated evaluation suite with interpretable geometric and topological features tailored to neuronal structures that we validate in a user study. MoGen's practical utility is showcased through controllable generation for visualization via smooth interpolation and a direct downstream application: we augment the training set of a shape plausibility classifier from a production connectomics neuron reconstruction pipeline with millions of generated samples, thereby improving classifier accuracy and reducing the number of remaining split and merge errors by 4.4%. We estimate this can reduce manual proofreading labor by over 157 person-years for reconstruction of a full mouse brain.

Published as a conference paper at ICLR 2026
MOGEN: DETAILED NEURONAL MORPHOLOGY GENERATION VIA POINT CLOUD FLOW MATCHING
Franz Rieger!:2 Jaa-Jan-aaiii Lueckmann' Viren Jain' & Michal Januszewski! 'Google Research
Max Planck Institute for Biological Intelligence
riegerfrebi mpg. de, {janmatthis, viren, mjanusz)@@ooglleee com
ABSTRACT
Biological neurons come in many shapes. High-fidelity generative modeling of their varied morphologies is challenging yet underexplored in neuroscience, and crucial for the subfield of connectomics. We introduce MoGen (Neuronal Mor- phology Generation), a flow matching model generate high-resolution 3D point clouds of mouse cortex axon and dendrite fragments. This is enabled by an adap- tation that injects local geometric context into a scalable latent transformer back- bone, allowing for the generation of high-fidelity, realistic samples. To assess MoGen's generation quality, we propose a dedicated evaluation suite with inter- pretable geometric and topological features tailored to neuronal structures that we validate in a user study. MoGen's practical utility showcased through con- trollable generation for visualization via smooth interpolation and direct down- stream application: we augment the training set of a shape plausibility classifier from a production connectomics neuron reconstruction pipeline with millions of generated samples, thereby improving classifier accuracy and reducing the num- ber of remaining split and merge errors by 4.4%. We estimate this can reduce manual proofreading labor by over 157 person-years for reconstruction of a full mouse brain.
INTRODUCTION
Modeling the intricate three-dimensional shapes of neurons is a fundamental challenge in neuro- science. Neuron morphologies exhibit tremendous diversity in size, thickness, and branching pat- terns, as well as compartment-specint properties like dendritic spine density. Variations exist not only between neurons of different species but also within a single brain and even within a single cell type (Ramón y Cajal] [194] [Ascoli] (002). While generative modeling has revolutionized fields like natural language processing (Brown et al.] (020)), image generation (Rombach et al. 2022). and protein structure prediction (Abramson et al. 2024), its application to neuronal morphology remains nascent. Previous data-driven approaches have focused on simplified representations like sparse skeletons (Laturnus & Berens, 2021; Yang al. (024) coarse occupancy maps (Hansel et al. 2024), failing to capture the detailed neuronal surface details crucial for many applications.
This gap is particularly acute in connectomies, the field dedicated acquiring and analyzing brain wiring diagrams from large, nanometer-resolutionn microscopy volumes (Sievers et al., 2024 Shapson-Coo et al. 2024: The MICrONS Consortium 2025 Tavakoli al. (025). A core part of building these wiring diagrams is neuron reconstruction. Substantial progress has been made in au- tomating the underlying segmentation process, but residual errors persist. With individual volumes reaching the petabyte scale, manual correction of these segmentation mistakes ("proofreading") has become a primary bottleneck in the field. with estimated costs reaching billions of USD for an up- coming single mouse brain (Jefferis et al.) (023). One of the barriers to improving these automated methods the limited amount of high-quality training data for the algorithmic components that assess the local plausibility of neuron shapes during reconstruction, process that includes segmen- tation and post-processing steps (Januszewski et al. 2025)
Work performed Student Researcher at Google Research.

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generation
interpolation
real data
extraction
90%
piausible
implausible
1
4.4%
synthelic data
10%
generation
SHAPE classifier
Split Merge
Figure 1: Overview. (A) Flow matching on point clouds iteratively transforms Gaussian noise into neuronal shapes. (B) MoGen allows for smooth interpolation, here between axon (left) and dendrite (right) fragments. (C) Real neuron fragments from an electron microscopy volume are extracted as point clouds. Synthetic examples augment the training data for downstream shape plausibility classifier which must distinguish between plausible and implausible neuronal morphologies. Co- training improves the classifier, leading to fewer errors in the final neuron reconstruction, which decreases the amount of manual proofreading needed.
This paper directly addresses this data scarcity problem. Neurons have intricate, topologically tree- like structures that extend through large volume while occupying very little space themselves. For instance, while a neuronal cell body might only he a few tens of microns in diameter, its axon can reach locations that lie many millimeters away (Ascoli] 2002). This geometry makes dense, voxel- based 3D generative approaches computationally and memory-intensive for high-resolution model- ing, motivating the of more efficient representations such point clouds. We introduce gener- ative model, MoGen, capable of producing high-resolution biologically plausible 3D neuron frag- ment morphologies. MoGen models neuron morphology as point clouds and employs flow matching for generation (Lipman al., (023). To our knowledge, this is the first work to generate high- resolution neuronal morphologies and demonstrate their utility in a critical downstream connec- tomics task within production-scale neuron reconstruction pipeline, PATHFINDER (Januszewski ct al. 2025). Specifically, we show that by co- co-training with synthetic data, a strategy that has proven effective other domains (Azizi et al., 2023), it is possible improve the accuracy of the SHAPE plausibility classifier used in the PATHFINDER system, leading to a reduction of the number of errors in the final automated reconstruction and directly alleviating the prohibitive costs of manual proofreading (Figure D.
Our contributions are fourfold:
MoGen:
a flow matching model for neuronal point clouds: We adapt a latent trans- former backbone to incorporate local geometric context and use flow matching with cus- tom schedule. This enables generation of high- fighliiiliity morphologies.
Dedicated neuron evaluation suite: rics more topologically tailored to neurites than standard distance metrics used in the point cloud literature. We show in user study that they correlate well with visual quality.
We introduce expressive set of interpretable met-
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Controllable generation: We demonstrate that MoGen enables smooth interpolation be- tween morphologies with control over different structural aspects (e.g. branchiness or ex- tent) and diversity of the generated samples through tunable faithfulness to the condition- ing.
Direct impact on a real-world connectomics pipeline: We improve state-of-the-art neu- ron shape plausibility classifier by co-training with millions of MoGen-generatee samples, reducing the number of remaining segmentation errors by 4.4%. At the scale of a complete mouse brain, this corresponds to estimated savings of about 157 person-years years of manual proofreading work - several times more than the total proofreading budget that was used for recently mapped insect brains (Scheffer al. 2020 Dorkenwald et al. 2022).
2 RELATED WORK
2.1 AUTOMATED RECONSTRUCTION FOR CONNECTOMICS
Modern connectomics pipelines (Lee et al. 2017 Januszewski et al. 2018 Sheridan et al. 2023 Chen et al. 2025) automate the segmentation of neurons but are prone errors like incorrect split- ting or merging of distinct neurites (Jefferis et al. 2023). To mitigate these errors, often stem- ming from limited field of view of the segmentation models, some downstream methods lever- age larger-scale shape reasoning (Troidl et al. 2024 Januszewski et al., 2025), or neurite direc- tionality (Schmidt al.) 2024) for automated error correction. In particular, the PATHFINDER pipeline (Januszewski et al. 2025) uses classifier, called SHAPE, to assess the biological plausi- bility of agglomerated segments by operating directly on 3D point clouds sampled from the neurite fragment meshes. This process starts with an over-segmented volume, where single neurons are ini- tially broken into multiple smaller segments. This volume is then iteratively agglomerated (merged); the classifier is used to decide which merges are plausible. This classifier is trained to distinguish plausible "positive" examples from implausible "negative" ones created by artificially merging two distinct neurite fragments. As shown by Januszewski al. (2025) Fig. S2.D), the performance of these critical components is limited by the amount of available high-quality, proofread training examples bottleneck MoGen directly addresses.
2.2
GENERATIVE MODELS FOR NEURONAL MORPHOLOGY
Data-driven generative modeling of neuron morphology is an emerging field (Farhoodi & Kording,
2018 Tang et al. 2020 Kanari et al. 2022 Much prior work has relied on procedural, rule-based generation (McCormick & Mulchandani 1994) or biophysical simulators (Breitwieser et al.) 2002). without taking full advantage of learning from data. Recent deep learning approaches have fo- cused on simplified representations. Many global neuron topology modeling approaches focus on neuron skeletons (Cuntz et al.) 2010 Zhu et al. 2025). Tang et al. (2020) generate images neu- ron skeletons to aid segmentation. MorphGrower (Yang al. (024) and Morph (Laturnus & Berens 202T) generate sparse skeletons, capturing branching topology but not detailed 3D morphol- ogy. PointNeuron represents neuron skeletons connected points with radius coarsely model membrane surfaces (Zhao et al. 2023). Similarly, Wiesner et al. (2022) also model dynamic cell surfaces with implicit neural representations and MorphOce generates 3D occupancy maps, again missing fine detail during generation (Hansel et al.] (024). A key distinction is that these methods typically generate full neurites, e.g., a whole dendritic branch, whereas we focus on much higher- resolution fragments with detailed local (surface) shape, as shown in Figure 2 Accurate surface modeling vital for simulations (Eyal al. 2018) and reconstruction tasks where high-frequency details (e.g.. spine heads, caliber changes) determine connectivity. Our work is, to our knowledge, the first to generate high-resolution point clouds of detailed neuron fragments and to demonstrate its utility in neuron reconstruction from volume electron microscopy imagery, downstream scientific application.
2.3 GENERATIVE MODELS FOR 3D POINT CLOUDS
We choose point clouds representation. as they efficiently encode the sparse, figh-frequency spatial information of neuronal membranes without the cubic memory cost of voxel grids. The effi- cacy of point-based learning for detailed neuronal geometry processing has been previously demon-
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MorphGrower
MorphOcc
MoGen (Ours)
100 um
100 um
10 μm
Figure 2: Comparison with prior work. Previously proposed generative models of neuron mor- phology such as MorphGrower (adapted from Yang et al. 2024) and MorphOce (adapted from Hansel al,, (224)), generate coarse representations (skeletons and occupancy maps) and not capture fine geometry. In contrast, neurite fragments generated with MoGen show superior details such as dendritic spines. Scale bars are approximate.
strated in reconstruction tasks (Zhao al. 2023); ; extend this paradigm generative modeling. While generative approaches like generative adversarial networks, variational autoencoders, and dif- fusion models have been adapted for point clouds (Li al. 2018 Anvekar al. 2022 Luo Huj
2021]. recent flow matching approaches offer simpler training objectives and efficient inference for
high quality generation (Lipman et al. 2023 Yushi al. 2025). However, many general-purposs models rely on architectures with quadratic computational complexity and do not scale well to the large point counts (here, 8192) required for detailed neuronal features. This makes direct quanti- tative comparison with many existing baselines computationally infeasible at target resolution. The PointInfinity backbone, initially introduced for diffusion, is a notable exception and considered state of the art for large point clouds, achieving linear scaling by using cross-attention between point tokens and a fixed-size set of latent tokens (Huang et al.] (2024). MoGen adapts the PointInfinity architecture with modifications (as outlined below) that improve generation quality of high-fidelity neuronal morphologies. For reference, we also include a comparison to a transformer baseline on downsampled dataset in Appendix F.I
METHOD
We generate neuron morphologies as point clouds in a two-stage process: a flow matching model generates the 3D point coordinates, optionally followed by regression model predicting per-point features (see Appendix A.4
3.1 POINT CLOUD GENERATION VIA CONDITIONAL FLOW MATCHING
We employ flow matching, for which a vector field Va transports samples from a simple prior dis- tribution Po (e.g., standard Gaussian) to a complex real data distribution Pi. Generation involves integrating the Ordinary Differential Equation (ODE) dx. = va(x,, t, c) from = 0 to t = 1, start- ing from a noise sample Xa Po. Here, is an optional conditioning vector. We train the model va to predict the constant-velocity vector v = Xo for interpolated samples x, (1 1)xp + /x1, where X ~ P1 is a real data sample. This yields simple mean squared error objective (Lipman al. 2023).
Ex, ||v vo(x,, c)]l
relative to which optimize the model's parameters 6. We sample the timestep using modified cosine schedule (see Appendix A.3) and use a midpoint ODE solver for inference.
3.2 IMPROVING MORPHOLOGICAL FIDELITY BY APPENDING RELATIVE COORDINATES
Our vector field model Ve is built upon the scalable PointInfinity architecture (Huang et al. 2024 We identified a critical limitation in the architecture for our domain: its global cross-attention mech- anism means each point token's 's contribution to the latent tokens is independent of its geometric neighbors, causing information bottleneck (see Appendix A.2 for details). This lack of direct
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Tokenizer
Latent Transformer
injectioo
predicted direction
Figure Architectural adaptation. MoGen injects local geometric context by finding the k- Nearest Neighbors (k-NN) for each point and appending their relative coordinates as features. This information, along with the point's own coordinates, processed into tokens that attend to set of latent tokens in transformer architecture, enabling the generation of high-fidelitg morphology. The model predicts for each point the direction it will move into during integration.
local communication detrimental, resulting point clouds with artifacts such as detached (groups of) points. We introduce simple yet effective modification: for each input point, find its k- Nearest Neighbors (k-NN) and append their relative 3D coordinates as additional features to its token (Figure 3 This well-established principle from the point cloud literature (Qi et al. 2017, Qian et al. 2022) provides a strong local geometric inductive bias that proved essential for this ar- chitecture generate high-fidelity point clouds. This modification is computationally lean, adding only small (< 15%) increase in training time for a substantial gain in quality. This gain is quantifi- able both by metrics and the model loss in the low-noise regime where fine details are formed (see Table
3.3 CONTROLLABLE GENERATION
To enable fine- tne-grained control, train conditional model where (projected) vector c is concate- nated to the latent stream tokens, similar to the conditioning tokens in PointInfinity (Huang et al. This allows global shape properties to be manipulated, including (Figure 4):
2024
Position & Spread (9 features): The 3D mean and the 6 unique elements of the covariance matrix the point clouds, computed after mean-centering.
The number of leaves in a Minimum Spanning Tree (MST) on 256-point subsample (via farthest point sampling), serving proxy for the number of terminal branches.
Branching Complexity (1 feature):
Neurite Type (if present, 1 feature): An encoding for axon dendrite (e.g., +1 or -1).
To enable unconditioned generation and conditioning on a subset of the vector we also provide binary mask indicating which dimensions of c are active (details in Appendix B.T). This provides smooth control over the generated shapes, as shown in the interpolation experiments (Figure
DATA AND EVALUATION
4.1 NEURON FRAGMENT DATASET
previously reported (Januszewski al. 2025 electron microscopy volume of mouse cortex tissue segmented with flood-filligg networks (Januszewski et al. 2018). All neurites in this volume have been proofread, and we followed the SHAPE training/validattonn setup used by Januszewski et al. (2025), with 1,795 axons used for training and 263 for validation. Same as for SHAPE, the volumetric axon reconstructions were meshed with marching cubes (Lorensen & Cline 1998) and skeletonized with TEASAR (Sato et al. 2000). We then randomly sampled points from the triangular faces of the mesh restricting the sampling to 10 p radius spheres centered at the nodes of the skeleton. This center node becomes the origin (0,0,0) of the point cloud, and coordinates are normalized such that unit corresponds to the 10 pm radius. This defines a local coordinate system relative to the fragment center, allowing the model to learn local morphology independent of absolute volume coordinates. Every point cloud was subsequently resampled to standard size of 8, 192 points with farthest point subsampling or random point repetition.
For our experiments,
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The axons used to create two datasets of "positive" (plausible) fragments taken from proofread reconstructions and "negative" (implausible), artificially merged examples for training the SHAPE classifier. We also curated a set of 2,200 dendrites. which we split into training (2,000) and valida- tion (200) sets. We added the dendrites to the "positive" axons to get "mixed" dataset of plausible neurites. This combined dataset provides challenging and realistic benchmark for generative mod- cling of cortical cell morphology.
We trained different MoGen models for different purposes: general model on the mixed set for most visualizations and ablations, and separate model trained only on negative axon examples (see Figure 14) for the downstream SHAPE co-training task.
4.2 EVALUATION PROTOCOL:: A CUSTOM METRIC SUITE
Standard metrics for point cloud generation (Huang et al. 2024), like Chamfer Distance (which, for two point clouds, averages the distance from each point in one cloud to its nearest neighbor in the other), are poor indicators of quality for neurons. neurite can branch in numerous valid ways, and small, biologically plausible change in the angle of branch can lead to a large Chamfer Dis- tance, unfairly penalizing a high-quality sample (see Appendix D.I for examples). Other common metrics, like Fréchet Inception Distance (FID) on rendered images or features from pre- pre-trained net- works (Yushi et al. 2025). J may suffer from domain shift and not explicitly capture the specific topological and geometric properties critical for neurons.
We therefore designed custom evaluation suite that compares the distribution of 10 interpretable features between real and generated sets. Because neuron fragments in our dataset have no canonical orientation and we use rotation augmentations, our evaluation features are designed to be rotation- invariant. We use Maximum Mean Discrepancy (MMD) (Gretton et al. 2012) as it requires fewer assumptions than Fréchet Distance (Jayasumana et al. 2024). The features, which we selected based on empirically observed generation failures in early experiments, are (see Figure
Global Shape features): Distance of the center of mass to the origin; standard deviation of point coordinates along the first, second, and third principal components. These capture overall size and elongation.
Local and Global Point Density (4 features): Mean and standard deviation of the dis- tance to the nearest neighbor (capturing local geometric regularity) and farthest neighbor (capturing compactness).
Topology (2 features): As neurites are tree-like structures: total weight (a proxy for total path length) and longest edge (sensitive fragmentation) of Minimum Spanning Tree (MST) on all points.
Crucially, we validate that our MMD score (see Appendix D.2 for details) a meaningful proxy for generation quality. In early experiments we found that the MMD score correlates well with the model's training loss, indicating that the metric captures model improvement. Furthermore, the features themselves are biologically salient: a UMAP (Mcinnes et al.) 2018) projection of our 10D feature vectors for real data (see Figure 9 in the Appendix) shows clear separation between axon and dendrite morphologies, with a simple linear classifier achieving 99.3% accuracy. This confirms that our features capture meaningful morphological differences. Finally, the MMD score also correlates strongly with human perception of quality, as detailed in our user study (Figure the Appendix).
4.3 DOWNSTREAM TASK SETUP
We demonstrate the practical utility of MoGen by extending the training set of the SHAPE classifier from the PATHFINDER pipeline (Januszewski et al.) 2025) with synthetic examples. We replicate the original training setup (we start with the same segmentation, and evaluate the number of split and merge errors in the final reconstruction after the SHAPE-guided search for an optimal agglomera- tion) but randomly sample 10% of the negative examples (as this class of erroneous merges is more complex and diverse) from a pool of 126M MoGen MoGen-generated generated point clouds. All generated samples for this data augmentation were unconditional to maximize diversity. We report further details in-

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Covariance
Origin to Mean
Farthest Neighbor
Nearest Neighbor
MST Edge
Longest MST Edge
Node
Leaf Node
Figure 4: Evaluation and conditioning feature visualization. Our interpretable features di- vided into three categories. Global Shape features (orange) describe the overall size and elon- gation, including the distance of the center of mass from the origin, the covariance of the point coordinates, and the mean and standard deviation to the farthest neighbors. Local Point Density features (blue) measure surface regularity, such as the mean and standard deviation of distances to the nearest neighbors. Topology features (black) capture the tree-like structure, using properties of a Minimum Spanning Tree (MST) like its total weight (path length), the length of its longest edge and the number of leaves. Different subsets of the features are used for evaluation and conditioning with formal definitions in Appendix
After training the SHAPE plausibility classifier we use improve the quality the axon reconstruction as described (Januszewski al. 2025).
cluding checkpoint selection in Appendix F.
5 RESULTS AND ANALYSIS
Our experiments validate two key claims: (1) our local context injection and cosine schedule im- prove generation of high-fidelity morphologies, and (2) our synthetic data provides tangible, sub- stantial improvement in real-world scientific application.
5.1 GENERATION QUALITY AND CONTROLLABILITY
Quantitative Comparison. As shown in Table I our full model with k-NN context substantially outperforms the Pointlnfinit-innppree baseline, achieving lower MMD score (3.54 vs. 4.27). This indicates a more faithful approximation lower loss in the low-noise regime (1 {0.8, 0.9)), which is responsible for refining fine details. We further evaluated the effect model width and depth. Reducing either the model's width or depth by half leads degradation in performance, confirming that sufficiently large model is necessary to capture the complexity of the data distribution. Furthermore, a full training run of million steps (compared to 500k for ablations) improved the MMD score of our final model 3.08. A qualitative comparison of real and generated samples is provided in the appendix in Figure 12 with details in Appendix A.
the real data distribution. The model also achieves a
Table 1: Generation quality evaluation. Architectural and feature- based ablation results, showing the impact of k-NN context injection, model architecture, and scheduling generative quality. Each configuration represents single change from the full MoGen setup. All models were trained for 500k steps.
Configuration
MMD
Loss (t E [0.8, 0.9])
Ours (Full MoGen)
3.54
0.709
w/o k-NN (Baseline)
4.27
0.712
Half Width
6.97
0.711
Half Depth
9.94
0.711
w/ Linear schedule
5.12
Controllability. MoGen allows for smooth, controllable interpolation between shapes by varying the conditioning vectors, as demonstrated in Figure 5 (with details in Appendix [B.2). This capa- bility serves powerful visualization tool for neuroscientists explore the morphological space,

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Figure 5: Controllable interpolation. Smooth interpolation is achieved by varying individual con- ditioning features. From top to bottom, pairs of rows show interpolation of: mean x-coordinate (blue), spatial extent (covariance diagonal, orange), branching complexity (#MST leaves, green), and rotation (covariance matrix, red). Note the biologically plausible emergence of fine details, such as dendritic spines, during the interpolation towards more complex dendritic morphologies.
for instance by smoothly interpolating from a simple axon to a complex dendrite with emergent dendritic spines. This demonstrates that the model has learned a structured latent space where high- frequency details are manipulatable. This biologically relevant as brains contain axon-dendrite hybrids (Goaillard et al. 2020), and it allows neuroscientists (e.g.. "what would this dendrite look like with higher spine density?") for hypothesis generation. We provide interactive 3D viewers and generation and interpolation animations in the supplementary material for additional qualitative assessment.
perform counterfactual analysis
As in other domains, using classifier-free guid- ance (Ho & Salimans, 2022) (which uses the difference between conditional and an uncon- ditional prediction to guide generation), we also see a trade-off between condition following and diversity in Table 2 (details in Appendix B.3
Table Impact of classifier-free guidance scale on generation faithfulness and diversity. Lower ranks are better for both metries. The highest guidance scale of 1.0 provides the best faithful- ness (MSE rank), while being less diverse (diver- sity rank).
5.2 IMPACT ON DOWNSTREAM RECONSTRUCTION PLAUSIBILITY
Guidance
Faithfulness. 4
Diversity
-1.0
5.90
3.70
-0.9
4.80
3.25
Having established MoGen's generation qual- ity, we now evaluate its real-world impact.
-0.8
3.75
3.20
-0.5
3.15
3,40
Results. We trained SHAPE models using only real examples as reported before (Januszewski et al. 2025) and with 10% examples synthe- sized with MoGen (see Appendix E22 for de- tails). We then used these models in the PATHFINDER pipeline to perform a combina-
0.0
2.20
3.35
1.0
1.20
4.10
torial search for optimal reconstruction of all axons in the volume. The final automated
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construction is compared manually proofread version of the volume, and the total number of remaining split and merge errors is counted. Figure 6 shows that data augmentation with MoGen leads to a decrease in both merge and split errors per millimeter of path length. In the optimal con- figurations, minimizing the sum of split and merge errors, the error rate is reduced by 4.4% from 0. 0.7947 to 0.7595 errors/mm.
Economic Impact. To contextualize this result, a mm volume of mouse cortex is estimated to contain several kilometers of wires (Braitenberg & Schüz, 2013), which corresponds to millions of errors that need to be manually corrected. A whole mouse brain is 500 times larger still. As detailed in Appendix E.4 the improvement due MoGen thus corresponds to up to 157 person-yess of saved manual work in a whole mouse brain project.
10"
real data only
real MoGa+MoGen
8x0n mergers mm
isochrone
10
10
10"
splits / mm
Figure 6: Error reduction in the final automated reconstruction. Co-training SHAPE models with MoGen examples simultancously improves split and merge eπror rates in PATHFINDER re- constructions and establishes new Pareto frontier. Stars indicate points that minimize the total number of errors (0.7947 mm-1 for real data only and 0.7595 mm for real data + MoGen). The curves are obtained by varying a threshold SHAPE score below which an example is considered to have implausible morphology. The dotted line (isochrone) indicates error levels requiring esti- mated time of 10 h to correct.
Synthetic Data Replacement Fraction. For the downstream task, we tested different fractions of synthetic data used to replace real examples during training. Table in the Appendix shows that 10% replacement fraction yields the best performance. This suggests that while synthetic data provides diverse examples that aid generalization, the original data distribution contains unique information essential for achieving peak performance. This phenomenon, where performance de- grades if the synthetic data fraction is too high, has also been observed in other domains Azizi et al., 2023).
DISCUSSION
This work serves as case study for generative AI not merely as a tool for creating realistic me- dia, but an engine for accelerating fundamental scientific research. The ability generate vast, diverse, and controllable synthetic datasets opens new possibilities for training more robust and accurate models for neuroscience.
Limitations. Our primary limitation is the occasional generation of topologically incorrect frag- ments, such as those with unrealistic cycles or disconnected components (see Appendix Figure [1] The latter can be reduced by post hoc filtering on the longest MST edge. These errors likely stem from the fact that the flow matching loss does not explicitly enforce topological correctness and that there are some topologically disconnected samples in the real data caused by imprecision of the un- derlying automated reconstruction and image artifacts. Furthermore, our k-NN modification, while improving quality, makes the model iisolution-dependen PointInfinity architecture (Huang et al.) (2024). However, we show in Appendix F2 that the model generalizes to some degree to higher resolutions not seen during training.
breaking the invariance of the original

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Synergies and Future Applications. The potential of this work extends beyond training classifiers. MoGen can be used for visual answers to counterfactual questions such as "what would a neurite with twice the average number of branches look like?" (Figure|5). Furthermore, Appendix F55 that MoGen can perform semantic editing of real neuron fragments (e.g.. increasing branching on existing axon) via inverse integration. It could also serve visualization tool for future datasets with more features such as cell types per neurite or for counterfactual genera- tion for discovering biologically relevant features similar to Eckstein et (2024). Additionally, could be used for interpreting embedding models trained on other modalities such gene expres- sion (Gouwens al.) 2019 Yao et al., 2023). where conditioning on a transcriptomic embedding could be translated into full 3D morphology. If extended to generate multiple neurite fragments a volume, MoGen could also enable the generation of synthetic segmentation and correspond- ing EM imagery for augmenting segmentation models (Rieger et al. 2024). A challenge is scaling from fragments of approx. 10 um radius with 8,192 points to complete, celttiimillimete--onnn neu- rons. This may require hierarchical approaches that can handle millions of points while maintaining global coherence. Finally, future work could also focus on generating maximally helpful co-training samples instead of just unconditioned ones (Such et al. 2020).
demonstrate in
7 CONCLUSION
We introduced MoGen, model for generating detailed, controllable 3D neuron morphologies. We demonstrated its direct, practical value by improving a critical component in a real-world neuron reconstruction pipeline, substantially reducing manual proofreading work, corresponding to over hundred person-years of saved effort at the scale of whole mouse brain. Our architectural insight of injecting local geometric context proved beneficial for generating detailed morphology. We val- idated our approach's 's fidelity and diversity with a custom metric suite and an extensive user study. This work lays foundation for new generation of data-driven tools to tackle the immense scale and complexity of connectomics.
REPRODUCIRIL STATEMENT
To ensure the reproducibility of our results, we release the code, together with the pre-trained model weights and the generated neuron fragment data at hhtpp:: /ttpsss//mmggnn///// //mogooonrnrree leas.. web. app/ The appendix provides detailed information on hyperparameters and the training setup.
LLM USAGE
We used Gemini 2.5/3 Pro to rephrase text at the sentence and paragraph level.
ACKNOWLEDGMENTS
We thank Peter Li for feedback and Jörgen Kornfeld for support. We gratefully acknowledge the data providers who made this work possible: the Hess lab and the FlyEM team at Janelia Research Campus (for mouse cortex and Drosophila data, respectively), and Jörgen Kornfeld and Winfried Denk (for zebra finch data). F.R. is supported by the Max Planck Society.
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A METHODOLOGY DETAILS
A.1 FLOW MATCHING DETAILS
Using JAX, we trained our models on 16 NVIDIA A100 GPUs using the Prodigy opti- mizer (Mishchenko & Defazio 2024) with learning rate of 0.5 and a global batch size of 512 for 500k steps for ablations or million steps (approx. 3 weeks) for the full trainings for the visualiza- tions and downstream application. To stabilize training, we used a global gradient clipping norm of 0.1. An exponential moving average (EMA) with a decay of 0.999 was applied to the model param- eters, a common practice for stabilizing training in generative models (Ho et al.] 2020). We report results for the checkpoint with the best MMD, evaluated every 5,000 steps. Following Januszewski al.] (025), as data augmentations, we used random rotations and applied a small amount (16 nm stdev.) of Gaussian jitter the point coordinates. Instead of, in Huang et al. (2024), training a lower number of points than generating, we use the same number in both to reduce domain shift. The key hyperparameters for MoGen, yielding 10.7 M trainable parameters, are listed in Table
While for PointInfinity, Huang et al. (2024) describe a latent recursion mechanism for inference, found that initializing latent tokens with learned values each forward pass was sufficient for high-quality generation.
Table 3: Key model and training hyperparameters.
Parameter
Value
PointInfinity Architecture
Point Token Dimension
128
Latent Token Dimension
256
Number of Latent Tokens
256
Number of Stages
4
Number of Transformer Blocks per Stage
2
Number of Attention Heads
8
k for K-NN Context
16
Training Parameters
Optimizer
Prodigy
Global Batch Size
512
Learning Rate
0.5
Gradient Clip Norm
0.1
Max Training Steps
500k (ablations) IM (full)
EMA Decay
0.999
A.2 INJECTING LOCAL GEOMETRIC INDUCTIVE BIAS
While the PointInfinity architecture can model global shape (Huang et al. 2024). its treatment of the input as a set of points can create a bottleneck for generating fine-grained details. The model's "read" cross-attention mechanism aggregates information from all individual point tokens into a fixed-size latent representation. This requires the latent state to compress not only the global structure but also all local surface properties, which can result in loss of fidelity. To generate high-quality details, the model needs a more direct way to reason about local geometry.
We address this by introducing strong local inductive bias (Qi et al. 2017). Instead of represent- ing each point p; E 3R3 independently, we augment features with information about local neighborhood. Let N(p.) = [p,,. ... P be the (distance sorted) list of the & nearest geometric neighbors of point p.. We compute the relative coordinates for each neighbor:
Apijm = P
Pi: for m = 1,...,k
(2)
The augmented feature vector f; for the point is then the concatenation of its own coordinates and these relative vectors:
co, Apin -.., Apij.)
(3)
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This richer feature vector f, is then projected into input token t, This explicit encoding of local patches provides the flow matching model with direct evidence of local structure, such as density, curvature and orientation. It simplifies the learning task, the model no longer needs to infer these properties solely from the global latent state. This alleviates the information bottleneck, allowing the latent representation to focus on global shape coherence while the augmented input tokens handle local consistency.
A.3 MODIFIED COSINE SCHEDULE FOR TIMESTEP SAMPLING
For sampling the timestep during training, we use a modified cosine schedule following Nichol & Dhariwal 2021). It is defined by:
C(u) (
0.5(1 - cos(+ 0.5(1 cos())))?
i 0.5
0f5 < u
We sample u uniformly from [0, 1] and then set the timestep t C(u). This transforms the uniform distribution to that concentrates sampling density the beginning and end of the interval, which we found empirically to balance the learning of global structure (high noise, low f) and fine local midpoint ODE solver with 100 integration steps
detail (low noise, high t). For inference, and this schedule to determine the integration step size.
A.4 FEATURE PREDICTION MODEL
For downstream tasks that require per-point features originally computed from the source mesh, e.g., pointwise surface normals and local curvature in PATHFINDER'S s SHAPE models (Januszewski jet al., 2025), we train a separate, simple regression model. This model takes generated (or real) point cloud of 3D coordinates as input and predicts the desired features for each point. It uses the same PointInfinity backbone with k-NN context injection but without time or feature conditioning and is trained with a standard L2 regression loss the target features. This two-stage approach decouples the complex task of geometry generation from the simpler task of feature prediction. We trained separate models for positive and negative fragments for 100k steps each. In early exper- iments we found that jointly generating coordinates and features with flow matching resulted in substantially worse MMD scores and that omitting features entirely from the downstream SHAPE classifier reduced its Fl score. We found that predicting curvature was challenging for the regres- sion model; using these noisy predictions in the downstream classifier slightly hurt performance compared to omitting them entirely. We retained the curvature feature in the baseline experiment using only real examples.
B CONTROLLABLE GENERATION DETAILS
B. CONDITIONING DETAILS
To enable unconditioned generation, the conditioning vector e set to zero vector 80% of the time during training. To enable conditioning only on a subset of the vector, we also provide a binary mask concatenated to the Il-dimensional c, indicating which dimensions masked independently with a probability uniformly sampled from [0, 1] for each sample, resulting in 2x I-dimensional conditioning vector.
active. Each dimension is
B.2
CONDITIONING INTERPOLATION DETAILS
Smooth interpolations are generated by varying the conditioning vector e while keeping the initial noise sample Xp constant for the entire sequence. For each animation, we define start and end conditioning vectors, Cstan and Cend. and generate intermediate point clouds by linearly interpolating between them. A binary mask, concatenated to c, indicates to the model which feature dimensions are being actively controlled.
The specific value ranges for the interpolations shown in Figure 5 are:
Mean x-coordinate: Interpolated from -0.75 0.75.
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Spatial Extent: The three diagonal elements of the covariance matrix interpolated from 0.02 to 0.12.
Branching Complexity: The MST leaves feature is interpolated from 0 to 256.
Rotation: Two diagonal covariance elements are interpolated in opposite directions, one from 0.0 to 0.5 and the other from 0.5 to 0.0, control elongation and orientation.
Each row in the figure strate that the controllable transformation robust across different initializations.
different, randomly chosen but fixed, initial noise sample demon-
B.3 CLASSIFIER-FRES GUIDANCE ABLATION
To enhance control over the generation process, we can employ classifier-fres guidance (Ho & Sal-] imans 2022). It adjusts the predicted vector field by combining the outputs of a conditional and an unconditional model prediction. The guided vector field v' is computed as
(5) where w is the guidance scale, is the conditioning vector, and 0 represents no conditioning. Setting 0 corresponds standard conditional generation, w -1 recovers unconditional generation. Higher values of w encourage stronger adherence to the conditioning signal, often at the cost of sample diversity.
(x t, c)
v)ve c) wV
We performed a detailed ablation study on the classifier-free guidance scale to analyze the trade- off between faithfulness to the conditioning signal and the diversity of the generated samples. The experiment involved testing six different guidance scales across 20 distinct conditioning settings. These settings were derived by taking five discrete steps along each of the four smooth interpolation paths demonstrated in Figure 3 (translation, scaling, branching, and rotation). For each of these 20 conditions, we generated 16 unique samples by starting from different initial noise vectors, allowing us measure the variance in the output.
We evaluated the results using two metrics, ranking the performance of the six guidance scales for each of the 20 conditions separately:
Faithfulness Rank (MSE): To measure how well generated sample adheres to its con- ditioning vector, we first compute the conditioning features from each of the generated samples. We then calculate the Mean Squared Error (MSE) between these computed fea- tures and the target conditioning features. lower MSE indicates better faithfulness.
Diversity Rank: To measure the variety of outputs for a fixed condition, we compute our 10-dimensional evaluation feature vectors for each of the 16 samples. We then calculate the standard deviation along each feature dimension, normalize these values by the standard deviation of the training set, and average them into single diversity score. A higher standard deviation indicates greater diversity.
The final ranks shown in Table 2 are the average ranks across all 20 conditioning settings. The results confirm the expected trade-off:: high guidance scale of 1.0 achieves the best faithfulness, but also the least diverse samples.
C FORMAL DEFINITIONS OF GEOMETRIC AND TOPOLOGICAL FEATURES
Let point cloud be represented by set of N points P (P1, (Pr..... pv where each point p; E RR3 We define the features used for conditioning (control) and evaluation as follows.
C.1 CONTROL FEATURES
These features are used as the conditioning vector c to guide the generation process.
Position Spread (9 features) These features describe the location and spatial extent of the point cloud.
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Position (Center of Mass); The mean vector R is the average of all points:
N
1
Σ
P
(6)
N
Spread (Covariance Matrix): The covariance matrix R describes the variance and covariance of the point coordinates. After mean-centering the point cloud, it is com- puted as:
Σ == - N-1 pp p) -p))
As Σ is symmetrie, we use its 6 unique elements (3 variance terms on the diagonal and covariance terms in the upper triangle) as features.
Branching Complexity (1 feature) This feature serves proxy for the topological complexity of the neurite fragment.
Number of MST Leaves: We first construct complete graph where the vertices all the points P for evaluation or for conditioning a 256-point subsample obtained via farthest point sampling. The edge weights find the Minimum Spanning Tree (MST) of this graph, denoted T. The number of leaves, L(T),, the count of vertices in T with degree of 1:
the Euclidean distances between points. We then
L(T) = Kp; E P degree, = 1H
Neurite Type (1 feature)
A categorical feature encoding the biological identity of the neurite.
Axon vs. Dendrite Encoding: A scalar value indicates the neurite type:
[+1
for axon
Ctype [-1
(9)
for dendrite
C.2 EVALUATION FEATURES
These 10 rotation-invariant features used to compute the Maximum Mean Diserepancy (MMD) score between sets of real and generated samples.
Global Shape (4 features) point cloud.
These features capture the overall size, position, and elongation of the
Distance of Center of Mass to Origin: The Euclidean norm of the mean vector μa:
darigin
|| = v 1C + n + 4
(10)
Standard Deviation along Principal Components (3 features): These describe the extent of the point cloud along its principal axes of variation. They the square roots of the eigenvalues (A1 2 A 2 Ag) of the covariance matrix Σ:
= />j for j = 1,2,3
(11)
Local Point Density (4 features) These features measure the quality and regularity of the point cloud surface and extent respectively.
Mean and Std. Dev. of Nearest Neighbor Distance: For each point p,, we find the distance to its nearest neighbor, dI:) minzzs |P; Pyll2- We then compute the mean (PIN)) and standard deviation (aINN) of these distances over all points:
N
JANN
1
Σ
dINN(p.)
(12)
N
CNNN
(dINN(p:) -MINN)
(13)
N
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Mean and Std. Dev. of Farthest Neighbor Distance: For each point p;, find the distance to its farthest neighbor, (p.( = max, |Ip: - Pyl12- then compute the mean
(Ptrr) and standard deviation (Faar) of these distances:
N
Mfar
dear (p:)
(14)
N
==1
iar
(pi)
M
(15)
Topology (2 features) Based on an MST constructed on the full point cloud, these features capture the tree-like structure. Let I' be the MST and w(e) be the weight of an edge T.
Total MST Weight: The sum of all edge weights in the MST, a proxy for the total path length of the neurite skeleton:
W(T)
Σ w(e)
(16)
cET
Longest MST Edge: The maximum edge weight in the MST, which is sensitive to discon- nected components or large gaps:
Wmax
max w(e)
(17)
eE2
D EVALUATION PROTOCOL DETAILS
D.1 CHOICE OF EVALUATION METRIC
Figure illlstrates why standard point cloud generation evaluation metrics like Chamfer Distance (CD) are ill-suited for evaluating neuron morphology. Morphologically plausible fragments that differ only by a small change branch angle can have an increasingly high CD. Our MMD-based metric interpretable features is more robust to these kinds of valid topological variations. Fur- thermore, comparing generated samples to a database of known realistic neuron shapes is intractable due to the combinatorial explosion of possible branching patterns that would need to be included in the database.
Reference
15*
30
45
Chamfer: 0.4564
1.7783
3.4155
Mean 1-NN: 0
0
A MST Weight: 0
0
0
MSST Edge: 0
0
Figure 7: Why Chamfer Distance is ill suited. Some of our metries are invariant to small changes in branching angles while CD increases.
D.2 MMD DETAILS
To compute the MMD score, compute 10-dimensional feature vectors for 16,384 real validation and training samples each and 512 generated samples and divide them by the dimension-wen wise stan- dard deviation of the train set. The MMD is then computed between the normalized embeddings of the real and generated samples. We report MMD scores on the validation set, which closely track performance on the training set.
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D.3
METRIC VALIDATION VIA USER STUDY AND UMAP
0.90
0.90
0.75
0.75
Accuracy
F1 Score
0.60
0.60
User
User
User
User
10"
10'
MMD
MMD
Figure 8: User study: correlation between MMD and manual real/generated classification per- formance. This plot shows the correlation between our quantitative MMD metric (x-axis) and the results of human evaluation study (y-axis, accuracy/FI score), demonstrating that lower MMD scores correspond to higher perceived realism. The study involved 4 human evaluators classifying mix of real samples and 64 generated samples from each of three model variants (ablations with higher MMD scores). To avoid bias, the ratio of real to fake was balanced such that random guessing would yield 50% accuracy. The plot shows a significant correlation between lower MMD (higher model quality) and higher perceived realism.
E DOWNSTREAM TASK DETAILS
E. SHAPE MODEL TRAINING
models using 16 A100 GPUs with a batch size of 512, using the AdamW (Loshchilov & Hutter 2019) optimizer and learning rate of 5.12 10-4, Training ex- amples were sampled with equal probability from the positive and negative classes, with 10% of the negative examples generated with MoGen. For every checkpoint saved during training (every 1,000 steps) we computed an FI score using small subset of the validation set (100 batches of positive and negative examples each). We then took the 20 top scoring checkpoints and performed inference using the full validation set. Unless noted otherwise, we trained and evaluated as de- scribed above three differently seeded replicas of every SHAPE model in parallel. We then took the checkpoint with the highest validation Fl score per replica, and used the median checkpoint for the computationally most expensive step of full volume inference and combinatorial search in PATHFINDER (Januszewski al. 2025).
We trained SHAPE
E.2 SPECIALIZED GENERATORS FOR DOWNSTREAM TASK
For the PATHFINDER experiments, the final co-training ratio (10% corresponding to 2.5 M syn- thetic examples seen during training) and the choice of the optimal model checkpoint were de- termined via grid search over held-out validation set. In early experiments we found that the downstream benefits are maximized when co-training with synthetic examples added to the negative class only. We attribute this to asymmetry in label noise risk. If the generator creates Positive sample with an artifact (c.g., a disconnected component), introduces problematic False Posi- tive into the training set. Conversely, if the generator creates Negative (implausible merge) that
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Figure 9: Metric features validation via UMAP. UMAP plot of our 10D feature vectors for real axon (blue) and dendrite (orange) fragments shows clear separation, validating our features' ability to capture meaningful morphological differences. linear binary classifier achieves 99.3% accuracy on this separation task.
slightly imperfect, it typically remains a valid negative example. Thus, augmenting the negative class with diverse, hard examples is safer and more effective than augmenting the positive class (sec Figure 13p.
E.3 SYNTHETIC DATA REPLACEMENT FRACTION ABLATION
E.4 MANUAL LABOR CALCULATION
follow Januszewski al. 2025 when calculating the expected savings of manual effort needed to eliminate the reconstruction errors via manual proofreading. We take the lower 300,000 person- years workload estimate of Jefferis et al. (2023), reduce by a factor of 84 due to the use of PATHFINDER (Januszewski al. 2025), and multiply the result by the 4.4% error reduction shown in our experiments (Figure to account for the additional gains from the use of MoGen- -egnerreeee examples.
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Table 4: Ablation on synthetic data replacement fraction for co-training. score achieved by the model in online evaluation (see Appendix E.1
We report the highest F1
Replacement fraction
Fl score (%) t
0% (only real)
93.91
1%
93.93
5%
94.10
10%
94.64
50%
93.65
90%
93.43
100% (only synthetic)
66.70
ADDITIONAL EXPERIMENTS AND QUALITATIVE RESULTS
F.1 COMPARISON LOWER RESOLUTION BASELINE
To situate MoGen within the broader literature point cloud generation, performed compar- ison against a full transformer baseline inspired by point cloud diffusion approaches (Luo & Hul 2021] down-sampled version of our dataset (2048 points), where this baseline is computation- ally tractable. As shown in T MoGen performs competitively while retaining the linear scaling necessary for the full-resolution task. This provides reference point and highlights the practical necessity of a highly scalable architecture for this scientific application.
The transformer baseline consists of 8 blocks with 256-dimensional token embedding. Due the quadratic memory complexity of its self-attention mechanism, deeper models were not feasible at the same batch size and hardware used for MoGen.
Table Comparison with a baseline on a down-sampled (2048 points) dataset. MoGen remains competitive in quality while being uniquely suited for scaling to 8192 points. For fair comparison, the MMD evaluation features were also computed on 2048-point clouds. Both models were trained for 500k steps.
Model
MMD (Total)
Transformer Baseline
5.78
MoGen
4.94
F.2 GENERALIZATION TO HIGHER RESOLUTIONS
As noted, our k-NN context injection breaks the formal rnsolutioniivvariance of the PointInfinity backbone. While this is an architectural trade-off for achieving high surface quality, we found that in practice the model generalizes also some degree higher point cloud resolutions not seen during training. Figure T0 shows examples of high-quality fragments generated with double (works) and quadruple (breaks down), more than the training resolution of 8, 192 points, demonstrating practical flexibility despite the theoretical limitation.
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Figure 10: High-resolution generation. Example fragments generated at double (top) and quadru- ple (bottom) the training point count. The model with k-NN context (left) demonstrates an ability to generalize beyond its training resolution, while struggling with larger domain shifts.
F.3 TYPICAL FAILURE MODES
Figure 11: Failure modes. Note typical failure modes, such small disconnected "dust" fragments and unnatural morphologies from a smaller model.
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F.4 QUALITATIVE COMPARISON OF REAL AND GENERATED SAMPLES
Figure 12: Real vs. generated mixed samples. Top: Real samples from the training set. Bottom:
Unconditional samples generated by MoGen. The model captures a wide variety of morphologies.
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t
Figure 13: Real (top) vs. generated (bottom) positive axon samples. The generated fragments exhibit high morphological fidelity, matching the diversity of real axons.
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Figure 14: Real (top) vs. generated (bottom) negative axon samples. Both real and generated negative samples exhibit specific artifacts such as disconnected components or unnatural mergers, which the SHAPE classifier must learn reject.
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F.5 EDITING REAL SAMPLES
Inspired by methods that use generative models for semantic editing (Rout et al.) 2025), we demon- strate that MoGen can be used modify real neuron fragments without any changes to the model architecture. This process involves a "reconstruct and edit" pipeline. First, a real sample X1 is in- verted by integrating the learned ODE backward in time from sample Xo in the prior distribution. A standard forward integration from Xo faithfully reconstructs the original sample, x X1. However, by introducing conditioning vector с during this forward pass and leveraging classifier-free guidance, we can edit the original sample's properties. As shown Figure [5] this allows for the creation of counterfactual morphologies, such as increasing the branchiness or altering the spatial extent of a real neuron fragment, providing powerful tool for exploring morphological variations.
O, yielding a noise
F.6 ADDITIONAL DATA EXPERIMENTS
To demonstrate the generalizability of MoGen beyond mouse cortical fragments, we extended our experiments to datasets from different species and acquisition volumes. We prepared point cloud datasets from three additional sources: (1) Drosophila (fruit fly) male central nervous system (Berg et al.. 2025) at 10 um radius, and (2) Zebra finch (bird) basal ganglia (volume j0251) (Rother et al., 2025 at both 10 um and 50 um radii. The point cloud extraction followed the same protocol as be- fore (see Section 4.1) sampling 8, 192 points within the specified radius centered on random skeleton nodes. Unlike the proofread mouse axon dataset, the zebra finch data was sampled from automated segmentations and includes cell bodies (somata).
We fine-tuned our general MoGen model (pre-trained for I million steps on the mixed mouse dataset) these new datasets. We continued training with the same optimizer state, swapping the data loader to the new target dataset. As shown in Figures [77 and [8] the model adapts well to these domain shifts. It also successfully generates sarger-scale structures (Figure [6] and captures distinct morphological features such as somata and the dense branching of medium spiny neurons, which were absent in the original training data.
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Figure 15: Editing real samples. We interpolate the classifier-free guidance scale from w -1 (left, corresponding to the reconstructed real sample) to strong condition adherence at w = 1 (right). The rows illustrate editing specifie morphological features: Mean x-coordinate (blue) (shifted +0.2), Spatial extent (yellow) (reduced to 0.25x original diagonal covariance), and Branching complexity (green) (maximizing MST leaves). Note how the morphology progressively adapts the target condition as guidance strength increases.
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Figure 16: Generalization to zebra finch (50 microns). Top: Real samples extracted from the volume. Bottom: Generated samples from the fine-tuned model. Note the successful generation of somata (cell bodies) and characteristic medium spiny neuron morphologies, features not present in the original mouse training set, demonstrating MoGen's 's capacity to adapt to larger radii and distinct structural classes.
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Figure 17: Generalization to zebra finch (10 microns). Top: Real samples. Bottom: Generated samples. The model successfully adapts to the specific branching statistics and local geometry of this different species, maintaining high surface fidelity despite the domain shift from the mouse cortex data.
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Figure 18: Generalization to Drosophila (10 microns). Top: Real samples. Bottom: Generated samples. This demonstrates cross-species generalization to invertebrate neuronal morphologies, capturing the distinct, often more jagged and thin structural properties of Drosophila (fruit fly) neu- rites.
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Reviews:
[{"review_id": "ixCbbJwiCm", "reviewer": "Reviewer_SXdV", "summary": "This paper introduces MoGen, a flow-matching generative model for synthesizing high-fidelity 3D neuronal fragments, including axons and dendrites, represented as point clouds. The method adapts the PointInfinity architecture by injecting local geometric context into the latent cross-attention module, improving structural coherence and reducing discontinuities in generated morphologies. The model is trained on EM-derived fragments and evaluated using a custom set of interpretable geometric and topological metrics tailored to neuronal structures. Its main practical contribution lies in augmenting training data for a shape plausibility classifier within the PATHFINDER connectomics pipeline, which enhances automated neuron reconstruction performance.", "questions": "Please see weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is clearly written and well-structured, with informative visualizations that effectively illustrate the concepts and generation results.\n- The integration of MoGen into the PATHFINDER connectomics pipeline and the demonstrated reduction in reconstruction errors show practical real-world impact.\n- The evaluation suite provides interpretable and domain-relevant validation, and its consistency with expert judgments through a user study supports the reliability of the results.", "weaknesses": "- Lack of proper discussion with previous 3D neuron synthesis [1] and point cloud representation [2] methods.\n- The neuron generation method MoGen is only limited for training shape plausibility classifier within the PATHFINDER setting. How about its application in broader interest such as boosting segmentation/reconstruction directly? \n- Since the dataset originates from a specific mouse cortical volume, it is uncertain about MoGen's generalization ability across different datasets.\n\n\n[1] Tang, Zihao, et al. \"3D conditional adversarial learning for synthesizing microscopic neuron image using skeleton-to-neuron translation.\" 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020.\n\n[2] Zhao, Runkai, et al. \"PointNeuron: 3D neuron reconstruction via geometry and topology learning of point clouds.\" Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Enmp5Ex3Zh", "reviewer": "Reviewer_pA2d", "summary": "Paper introduces flow-matching method that allows to generate 3D point cloud neuron morphologies by adapting and augmenting standard flow-matching methods e.g. used in image generation. They show significantly improved generations compared to prior work and demonstrate downstream simplifications in human annotation in neuron reconstruction.", "questions": "what are other relevant use cases apart from synthetic data generation for tool improvement?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Well motivated problem\n- Well written paper\n- Thorough methods", "weaknesses": "- Rather niche and not of broad relevance to ML community. However, I am unsure whether this is a relevant weakness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dz5R3ZluVw", "reviewer": "Reviewer_C4vF", "summary": "This manuscript introduces a flow matching generative model for synthesising high-fidelity neurite (axon, dendrite) fragments in the mouse cortex using 3D point clouds. The model adapts the PointInfinity architecture to better model neuronal fragment structures at high resolution, whilst minimizing undesirable discontinuities. This is achieved by injecting local geometric context into the latent cross-attention mechanism that compresses individual point tokens to extract global geometric structures.  The primary practical contribution of this work is to generate additional axon fragment samples to train and improve the performance of a shape plausibility classifier, enabling more reliable merging decisions during neuron reconstructions from EM data.", "questions": "•\tWhy is MoGen only used to generate negative axon samples for the downstream SHAPE co-training task? This seems to suggest that there are enough positive axon examples for training. In this case, they should support a sufficient number of implausible combinations to produce enough negative samples, making the usage of MoGen seem redundant here?\n\n•\tFollowing from the above question, Page 18 states, “In early experiments we found that the downstream benefits are maximized when co-training with synthetic examples added to the negative class only”. Why is this the case? Does this not suggest a lack of fidelity in neuron fragments generated by MoGen? \n\n•\tThe justification for the 10% negative sample ratio in the downstream task is not clearly expressed. Specifically, what does it mean by “this class of erroneous merges”? Are there different classes of negative examples? If so, why do 10% of the negative examples originate only from one class?  \n\n•\tPlease explain how you define the classifier-free guidance mechanism. Why are there <1.0 values on the cfg?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "-\tThe idea of using 3D point clouds for high-fidelity neurite fragments generation is novel and interesting, intuitively suited to the sparse yet expansive volume of the neurites.\n\n-\tThis is a promising application that could be utilized to improve the final performance of a production-level reconstruction pipeline.\n\n-\tThe paper is generally well structured and presents the arguments in a logical order. The generation and interpolation visualisations demonstrate the effectiveness of MoGen.", "weaknesses": "-\tThe motivation of this paper is unclear. The paper would benefit from a detailed justification for its narrow focus on neurite fragment generation, neglecting the importance of full neuron topology in characterising neuron identity. From my domain knowledge, in single-neuron classification tasks, the overall structural organization of axons and dendrites often provides more discriminative information than local surface geometry. \n\n-\tThe value of interpolating from axon to dendrite in the morphological space needs more motivation from a neuroscience perspective. Could the authors explain why this is beneficial?  Biologically, axons would not develop into dendrites and vice versa. Do the smoothly interpolating axon and dendrite morphologies tend to co-exist in real neurons?  Please provide more ablation studies and analysis to address this concern.\n\n-\tIn the ablation study, the authors validate the effectiveness of classifier-free guidance (cfg). However, flow-matching generative models conventionally use cfg<1.0 to indicate that it doesn’t use the cfg mechanism for sampling. Please explain this further.\n\n-\tIt is unclear how the axons were categorised into “positive” and “negative” types in Section 4.1. It seems to suggest that negative cases are created by combining real axon morphologies in implausible ways. Additional visualization of these comparisons could help to understand.\n\n-\tThis manuscript only compares different configurations for the component of their proposed method. How are the comparison results of the existing methods, such as MorphVAE, MorphGrower, and MorphOcc?\n\n-\tThe downstream application task on improving the SHAPE classifier only supports the usefulness of generated axon fragments and neglects the arguably more intricate dendritic morphologies. Since assessing the model’s impact on a downstream system is another way to validate the generation quality of the neurite fragments, excluding the synthetic dendrites from such evaluation leads to a lack of more rigorous validation for its generation quality, beyond the custom metric via the MMD. The authors may provide further justification for this potential problem.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Voyai2u7Pa", "reviewer": "Reviewer_99iG", "summary": "This paper presents MoGen, a flow matching model for generating high-resolution 3D neuronal morphologies as point clouds. The authors injects local geometric context into a scalable transformer backbone, significantly improving the fidelity of fine details. The authors validate their model with a custom, interpretable evaluation suite and, most importantly, demonstrate its practical utility by augmenting a neural reconstruction pipeline's training data, which leads to a measurable reduction in neuron reconstruction errors.", "questions": "1. The cells generated in this paper seem much smaller than those in other papers like MorphGrower (as shown in Figure 2). Why is there such a big difference in size? Both are based on mouse brain data, but your generated structures look about ten times smaller.\n2. The experimental results show that using the generated data as negative samples for SHAPE classifier training leads to performance improvement. However, if the generated 3D point clouds were highly realistic and biologically plausible, introducing them as *negative* examples would be expected to confuse the classifier and cause performance degradation. The positive result suggests that the generated data **is not biologically realistic**. Instead, it provides a useful set of specific, artificial patterns for the classifier to learn.\n3. What is the biological justification for using the mean x-coordinate as a conditioning feature? The manuscript indicates an interpolation range from -0.75 to 0.75. Were all neuronal fragments decentralized during pre-processing? The absolute position of a neuron in the brain is tied to its specific brain region and is unlikely to be confined to such an arbitrary, normalized range. Please clarify the pre-processing steps and the neuroanatomical meaning of this feature.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper employs a state-of-the-art generative architecture, underpinned by a well-motivated modeling insight. The introduction of local geometric context effectively addresses a critical information bottleneck, leading to a demonstrable and significant improvement in the fidelity of generated morphological details.\n2. The proposed evaluation suite is a notable contribution in itself. It moves beyond inadequate standard metrics by introducing a set of biologically salient and highly interpretable features that are tailored to the domain-specific properties of neuronal structures, a claim robustly validated through a user study.\n3. The work provides a comprehensive and rigorous ablation study that systematically dissects the contribution of each core component. This thoroughly validates the necessity of the proposed architectural adaptations, the model's capacity, and the training schedule.", "weaknesses": "1. **The generated data represents a surface/volumetric structure rather than a topological tree.** The outputs of this model are dense point clouds representing surface geometry, which diverges from the classical definition of a neuronal morphology as a binary tree structure (skeleton) [1,2,3,4]. If the authors' goal is to generate detailed cellular surfaces with volume，the work should be more appropriately positioned and compared against prior works that also generate volumetric cell structures, such as [5]. \n2. **The evaluation protocol for topological features may be compromised by undersampling.** The authors compute two key topological metrics based on a Minimum Spanning Tree (MST) constructed from a heavily subsampled point cloud of only 256 points. This sparse sampling density is likely insufficient to faithfully capture the intricate branching patterns of neurites, potentially leading to the erroneous merging of distinct branches and a significant loss of fine-grained branch morphology details[2]. The validity and sensitivity of these metrics are therefore questionable.\n3. **There is no validation of the fundamental biological validity of the generated structures.** A core anatomical property of neurons is that their arborization (excluding the soma) forms a binary tree[6]. The authors do not provide any evaluation to guarantee that the generated point clouds adhere to this essential topological constraint. \n\n**Reference**\n\n[1] Cuntz, Hermann, et al. \"One rule to grow them all: a general theory of neuronal branching and its practical application.\" *PLoS computational biology* 6.8 (2010): e1000877.\n\n[2] Laturnus, Sophie C., and Philipp Berens. \"MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space.\" *International Conference on Machine Learning*. PMLR, 2021.\n\n[3] Yang, Nianzu, et al. \"MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generation.\" *International Conference on Machine Learning*. PMLR, 2024.\n\n[4] Zhu, Tianfang, Hongyang Zhou, and Anan Li. \"MorphoGen: Efficient Unconditional Generation of Long-Range Projection Neuronal Morphology via a Global-to-Local Framework.\" *Proceedings of the IEEE/CVF International Conference on Computer Vision*. 2025.\n\n[5] Wiesner, David, et al. \"Implicit neural representations for generative modeling of living cell shapes.\" *International Conference on Medical Image Computing and Computer-Assisted Intervention*. Cham: Springer Nature Switzerland, 2022.\n\n[6] Peng, Hanchuan, et al. \"Morphological diversity of single neurons in molecularly defined cell types.\" *Nature* 598.7879 (2021): 174-181.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)