TreeMoCo: Contrastive Neuron Morphology Representation Learning
Abstract
Morphology of neuron trees is a key indicator to delineate neuronal cell-types,analyze brain development process, and evaluate pathological changes in neurological diseases.
Traditional analysis mostly relies on heuristic features and visualinspections.
A quantitative, informative, and comprehensive representation ofneuron morphology is largely absent but desired.
To fill this gap, in this work,we adopt a Tree-LSTM network to encode neuron morphology and introduce aself-supervised learning framework named TreeMoCo to learn features without theneed for labels.
We test TreeMoCo on 2403 high-quality 3D neuron reconstructions of mouse brains from three different public resources.
Our results show that TreeMoCo is effective in both classifying major brain cell-types and identifyingsub-types.
To our best knowledge, TreeMoCo is the very first to explore learningthe representation of neuron tree morphology with contrastive learning.
It has agreat potential to shed new light on quantitative neuron morphology analysis.
Introduction
The central nervous system is composed of hundreds to thousands of neuronal subtypes.
Subtype isthe critical determinant of neuronal function within local and long-range circuits and is identified bya combination of gene expression patterns, dendrite/axon morphology, synaptic connectivity andelectrophysiological properties.
In particular, dendritic morphology is a central node of neuronalsubtype identity, as dendritic branches receive the vast majority of the presynaptic input onto eachneuron and directly shape the electrophysiological properties of the neuron that governs its firingoutput.
Delineating neuron cell-types based on their dendritic morphology is one of the core problemsof neuroscience for the past century [51].
Neuron dendritic morphology can be digitally represented by a 3D neuron reconstruction (Figure2(a)), a tree graph with 3D coordinates of each node; the root is placed at soma.
With recent advancesin imaging [9, 11, 47] and computational methods [20, 27, 42], 3D reconstruction of whole neurontrees with high-throughput is now accessible for mouse brain [28, 44].
However, due to the lack ofeffective measurement, most analyses of these 3D reconstructions are still largely based on biologists’visual inspection.
Considering the enormous number of neurons in the brain and the 3D formation ofneuron morphology, such visual inspection is subjective, biased, and has low throughput.
The past decade has witnessed the success of deep learning in learning representations of almostall kinds of data such as image, language, sound, and graph [2].
However, as a special type ofgraph or graphic data, most of the quantitative analysis of neuron trees is still based on simpleheuristic features [33].
Only a few attempts have been made in learning deep representations ofneuron morphology.
In [52], the authors proposed to encode neuron morphology with tree recurrentneural network (TRNN).
However, this method relies on manual annotations, limiting its applicationto supervised training only.
In [22], the authors proposed a generative self-supervised learning b c d e a d c b e
(SSL) method for neuron morphology representation learning, named MorphVAE, to reconstructoriginal neuron trees via a variational autoencoder and random walk sampling.
Since this methodcannot encode tree topology structure, its classification performance is limited and the method stillrelies on label information to generate reasonable predictions.
In [32], the authors adopt multi-viewbased SSL to learn representations of neuron morphology.
Though the work mainly focuses on thelocal components of neuron branch which is not available in whole neuron reconstruction data, itspromising results advocate the possibility of learning morphology representation without labels.
Inspired by the contrastive learning successes in visual representation, we experiment with adapting MoCo for neuron morphology representation learning, namely TreeMoCo. It can learn embeddingof 3D neuron reconstructions without needing prior knowledge such as cell type annotation.
Itfirst maps neuron trees input to a deep embedding space by our proposed neuron encoder, and thenincorporates an SSL pipeline composed of neuron tree augmentations and a contrastive loss to learnrepresentations.
By testing the framework on 2403 neuron reconstructions from 3 different dataresources, we are excited to find that TreeMoCo can clearly delineate neuron morphology in theembedding space (Figure 1).
Comparison with prior arts shows that TreeMoCo can achieve premiumresults than supervised methods even without annotations .
[Figure]
Background and Related Works
Heuristic features of neuron morphology.
To quantitatively describe the neuron morphology,a set of heuristic features and related tools namely L-measure [33] has been proposed and widelyused.
These features can be summarized as 4 categories including (1) distance-based measurements( e.g. , branch length, distance to soma); (2) angle-based measurements ( e.g. , bifurcation angle);(3) topology-based measurements ( e.g. , number of bifurcations, branch order); and (4) size-basedmeasurements ( e.g. , branch radius, surface area).
Statistics of these features such as mean or standarddeviation are usually adopted to describe neuron morphology.
Such representation is easy to computeand explain.
However, they cannot differentiate complex morphologies (examples in Appendix A.1).
Cell-type and neuron morphology classification.
As the primary function of dendritic branchesis to compartmentalize functionally related synaptic inputs, dendritic morphology can be used tocategorize neuronal subtypes based on total dendritic length, amount of branching across the dendritictrees, and localization of dendritic branch fields to different brain regions (such as layers of neocortex).
Most existing cell-type classification algorithms rely on hand-crafted features [33] to predict or clustercell types [40, 24].
In some recent works, researchers started to adopt tree-based model [52, 23] andsequence-based model [22] to classify neuron morphology.
Despite competing performance thanclassic methods, training of these models requires manual annotations of cell types.
Contrastive learning.
Contrastive learning (CL) is a SSL method that aims at bringing positivesample pairs closer and spreading away the negative sample pairs in the feature embedding space.
This method dates back to [13] or earlier, and has recently drawn significant interest across differentcommunities [45, 25, 17, 48, 16, 6, 5].
Some research efforts have been made in graph contrastivelearning (GCL) [37, 30, 15, 49, 46].
Particularly, GraphCL [49] studies the graph data augmentationssystematically for GCL, and InfoGCL [46] provides a theoretical analysis for it.
However, to our bestknowledge, no work has studied the tree graph, a special instance of graph, within the context of CL.
Methods3.1 Tree graph representation of neuron
We define a neuron reconstruction as T ≜ ( V , E ) , where V = { v k } ; v k = ( x k , y k , z k ) are nodes with3D coordinates and E = { e k } ; e k = ( v k , v parentk ) are edges connecting each node with its parent. As shown in Figure 2-(e), the number of 1-degree nodes could vary by reconstruction and imageresolutions, while the bifurcation/tip nodes preserve major topology information. For computationalefficiency, we remove 1-degree nodes and directly connect the root node, the bifurcation nodes, andthe tip nodes in T (Figure 2(b)).
To preserve the shape information of 1-degree node sequences(branch in Figure 2(e)), we calculate the related features and concatenate them with the coordinateproperty of its downstream node, as illustrated in Figure 2-(e,f). The processed tree graph is definedas Υ ≜ Process( T ) ≜ ( X , G ) , where G represents the tree topology and X = { x k
Input Tree Υ =
( X ,𝒢) q k
Tree Augmentation
Neuron Encoder
Projection head
Contrasting Σ Σ Σ Σ Σ Σ Σ Σ ˜ X
= ϕ
( X ) z
= η ( ˜ X ,𝒢 ) h ( z ) || h ( z )
|| 2 Projection Augmentation Υ′ =
t (Υ) t ∼ 𝒯 t ′ ∼ 𝒯
LSTM-based Tree Path Processor a b ˜ x k Σ Σ ˜ x j ˜ x i h j , z j h j , c j h i , z i h i , c i h k , z k h k , c k
Child Nodes i , j
Parent Node k
Branch feature is a Q -dim vector, of which two dimensions denote length and contraction.
Length L isthe accumulated distance between adjacent nodes along a branch: L = (cid:80) start ≤ i<end i
||
v i − v
i +
|| 2 ,which measures size.
Contraction is the ratio between the Euclidean distance D
=
||
v start − v end
|| 2and the length L of a branch: C = D/L , which measures the curviness.
Notably, both measurementsare invariant to the order of edges and thus cannot differentiate certain patterns (details in Appendix
A.1). To allow a comprehensive encoding of branch pattern, we resample each branch with a fixednumber of vertices (8 in this paper) and take the sequence of the edge vectors ⃗E
i = v
i − v i +1 as theits shape feature: F E ∈ R 24 .
In addition, the branch orientation is unified before extracting featurevectors such that the start and end points are on the x-axis and the principle direction is aligned withy-axis (Figure 2(c)).
Together, there are 29 features for each neuron node: x = [ x, y, z ; L ; C ; F E ] .
3.2 Neuron Encoder
We next propose a neuron encoder f to project the neuron tree graph into a feature space.
Our modelhas two components: a trainable MLP (multi-layer perceptron) embedding layer ( ϕ ) that maps pointcoordinates and other node-related features into the embedding space, and a LSTM-based tree-pathprocessor ( η ) that exploits tree topology information.
Formally, we define the neuron encoder asf ≜ η ◦ ϕ .
Figure 3 (a) shows an overview of our neuron encoder.
Embedding Layer ( ϕ ).
The embedding layer maps input features vector x into d -dim embeddingvectors ˜ x .
It is implemented by a three-layer perceptron with BatchNorm and ReLU attached tothe input and hidden layers.
We refer to the outputs of this embedding layer as node embeddings.
Formally, we compute ˜x k
= ϕ
( x k ) , where x k is the k -th node of a neuron, or jointly as ˜X
= ϕ ( X ) .
LSTM-based tree-path processor ( η ).
The original LSTM [18] architecture applies for strictlysequential data.
However, a neuron tree structure is more complicated than a simple sequence.
Therefore, we adopt a child-sum Tree-LSTM architecture, similar to [38], to process such data.
Recall the hidden state and the cell state at time t and layer i are usually denoted as h t,i and c t,i in LSTMs [18].
Here we denote them similarly, but indexed by tree node, as h k,i and c k,i at node k .
To aggregate information from multiple child nodes, we simply sum over the hidden states and cellstates.
The transition equations can be formulated as followings:
[Equation]
where C( k ) denotes the set of child nodes of node k , and z k is the final cell state.
We use two layersof LSTM cells (non-shared weights) to better capture the topology information (Eqns. 2, 4).
Our tree-path processor is designed in a bottom-up manner where the information is aggregated fromeach individual leaf nodes and flowed to the root node.
We then use the final cell state of the rootnode z , referred to as neuron representation , for downstream tasks, e.g. , predicting class labels byan additional single linear layer classifier or projected by a projection head [5, 16] for contrastivelearning.
Formally, we compute the representation of a neuron tree by z = η ( ˜X , G ) (the propagationpath is determined by G ).
Figure 3 (b) demonstrates how information is propagated along the path.
3.3 Contrastive learning of tree graph
Recent progress in self-supervised contrastive learning, stemmed from visual representation learning[45, 16, 6, 5, 12], has been seen in many other fields, including graph contrastive learning [46, 49]— an overall under-explored direction.
Attempts have been made to study self-supervised graphrepresentation learning in terms of data augmentation [49], network architecture, and overall pipelines[46].
Although the neuron tree is a special case of graph structure, we argue that it is too specialto share the same setups as traditional graphs in terms of contrastive learning [49].
Below we firstintroduce our TreeMoCo pipeline, then elaborate on the subtle but important difference between aneuron tree and a general graph, subsequently introducing our data augmentations for tree structure.
Tree Contrastive Learning. Figure 3 illustrates the overall pipeline of TreeMoCo.
The core ideaof contrastive learning is to maximize the similarity between positive sample pairs and minimizeit among negative sample pairs by optimizing a contrastive loss function [45, 5, 13].
We follow asuccessful exemplar, MoCo [16, 6], in visual contrastive learning to build our framework.
Conceptually, MoCo [16] abstracts contrastive learning as a dictionary look-up problem.
For eachencoded query q , there is a set of encoded keys { k 0 , k 1 , k 2 , ... } in a dictionary.
The task for a modelis to pull closer q and its matched positive key k + in the dictionary while to spread q away fromall other negative keys k − .
To that end, we follow the successful experience in [5, 16] to use thedot-product as similarity measurement, and the InfoNCE [45] contrastive loss:
[Equation]
where τ is a temperature hyper-parameter [45].
This loss is defined for one input instance, and thetotal loss is averaged over a batch.
To construct a pair of query and key, given a set of trees D and a set of possible augmentations T ,we first uniformly sample a tree Υ ∼ D and transform it into two augmented views Υ q = t (Υ) andΥ k = t ′ (Υ) , referred to as the query view and the key view, by applying two sampled augmentationst ∼ T , t ′ ∼ T .
Then, we compute their neuron representations using a neuron encoder defined in
Sec. 3.2, i.e. , z q = f (Υ q ) , z k = f ′ (Υ k ) .
Following the improvement in SimCLR [6], we furthermap the neuron representations to another latent space using a non-linear projection head h ( · ) andnormalize them by q = h ( z q ) / || h ( z q ) || {+
 and+} k = h ′ ( z k ) /
||
h ′ ( z k )
|| 2 .
Here, f ′ and h ′ stand for themomentum copies of f and h , named momentum encoder [16].
Therefore, q and k + in Equation5 are naturally generated from a single sample, and k − ’s are from a queue [16] that stores featuresfrom previous batches.
Discussion: data augmentation on graphs, trees, and neuron morphology.
Data augmentationis essential for self-supervised contrastive learning [5].
Current successful data augmentations forgraphs [49] mainly include (1) “node dropping” that randomly drops graph nodes and their associatededges, (2) “edge perturbation” that randomly adds or drops edges, (3) “attribute masking” that actslike dropout [36] to node features, and (4) “subgraph sampling” that uses random walk to samplea partial graph.
These augmentations can apply to general graph data without loss of generality.
However, some of them are unsuitable for tree-structure graphs without special consideration.
Forexample, the node dropping at a rate of 0.2 (default in [49]) would produce a pruned tree since allthe subtrees would be removed once their parent node was dropped.
Besides, edge perturbation isunlikely to be feasible in a tree structure if no strategical design is considered.
Two nodes in a neurontree may be spatially close to each other while having long “distance” in terms of topology ( e.g. ,number of hops from one node to another) and vice versa.
The perturbed edges would create cyclicgraphs on the one hand, and break the consistency between space (3d coordinates) and structure(nodes connectivity) on the other hand.
Therefore, we argue the special consideration is needed forthe design of data augmentations on tree-structured data.
Data augmentation for neuron morphology representation learning.
Regarding the characteristics of neuron data, we propose 11 available augmentations for neuron morphology representationlearning, which are briefly summarized in Table 1 and some implementations are provided in
Appendix.
The rationale behind our design is that neuron representation should be invariant toaugmentations — the golden rule applicable in many cases.
For neuron data, such augmentationsmay occur in (i) node coordinates, (ii) overall morphology, (iii) attribute masking and (iv) topology.
Thus we can augment neuron data from different perspectives as Table 1 shows.
Jittering (4, 6) meansadding random noise in a small range; deforming (7) means scaling each branch independently.
Wealso include attribute masking (8) used in general graph augmentation.
For (iv) topology, we proposethree different augmentations.
The first is (9) “truncated dropping subtrees” that recursively visitsnodes from the root to leaves and drops a subtree with a small probability ( e.g. , 0.05).
The name“truncated” means at most m revisesubtrees will be dropped.
Similarly, we have (10) “skipping parentnode” that at most m parent nodes will be replaced by one of their subtrees (respectively), and (11)“swapping sibling child” that picks a subtree from a sibling node and swaps that subtree with anothersubtree in the current node; all pickings are random.
For simplicity, we empirically use the samem = 10 for (9)-(11) in this paper.
Figure 2 (d) illustrates how topology augmentation acts like.
[Table]
Experiments
4.1 Experiment setups
Data.
We download data from 3 public resources: (1) BICCN fMOST data from Brain Image
Library (BIL, https://download.brainimagelibrary.org/biccn/zeng/luo/fMOST/ ) [28],(2) Janelia MouseLight (JML, http://mouselight.janelia.org/ ) [44], and (3) Allen Cell Types (ACT, https://celltypes.brain-map.org/ ) [1].
In total, 3358 three-dimensional mousebrain neuron reconstructions are collected (refer to Table 2 for details).
To facilitate analysis, weexclude cell types with less than 50 reconstructions, yielding 2403 neurons in total.
Some of thesamples include both dendrite and axon reconstructions.
For simplicity, we only keep dendritic arborsin this paper and leave the analysis of axons for future works.
All three datasets have registeredreconstructions to the 3D Allen mouse brain Common Coordinate Framework (CCFv3 [41]) and thustheir scales are the same.
Meanwhile, the absolute coordinates and orientations of registered neuronscould also carry cell-type information that are related to brain anatomy instead of neuron morphology.
To eliminate such potential information leakage, all dendrites are shifted and rotated such that theroot is placed at the origin and the X/Y/Z coordinates are determined based on principle componentanalysis (PCA).
Data are then processed and analyzed following the steps introduced in the Section3.1.
In the interest of evaluating TreeMoCo’s capability of classifying known cell types across datasources, we specifically use the cell types with rich coverage among 3 datasets only (grayed cell typesin Table 2).
We denote these sub-datasets as BIL-6, JML-4 and ACT, respectively.
For supervisedtraining, 80% samples in each dataset are randomly picked as training set and 20% are reserved fortesting.
For TreeMoCo pre-training, we pre-train on the joint training sets of BIL-6, JML-4, and ACTwhen evaluating the supervised downstream tasks and train on all samples except those from class“others” when analyzing the embedding space.
Implementations and evaluation.
We postpone the detailed implementations and settings ofhyper-parameters of TreeMoCo to Appendix B.1. For implementations of all the compared methods,we directly use the official codes and the default settings of MorphVAE [22], TRNN [52] and
GraphCL [52] to obtain the results.
Please refer to Appendix B.2 for their implementation details. [Table]
[Table] All models share the same 29-d input features and are trained for 100 epochs. For evaluation, wereport the sample-wise accuracy. During pre-training, we evaluate models every 5 epochs via a K
Nearest-Neighbor (KNN) classifier for unsupervised methods or their inherent classification headsfor supervised methods and report the best test set performance for all the studied methods. Thisstrategy mainly follows [45, 49].
We set the number of neighbors to 20 for BIL-6 and ACT datasetsand 5 for the JML-4 dataset since a minority class might only have 12 samples in total in JML-4.
4.2 TreeMoCo delineates neuron morphology in the embedding space
Visual inspection of cell types delineation. We first demonstrate that TreeMoCo can learn neurontree representations that manifest different characteristics of neuron morphology.
Self-supervisedtraining is conducted on the full dataset, and the distribution of the embeddings is visualized byt-SNE [39] in Figures 1 and 4.
By visual inspection, it is evidenced that TreeMoCo can separatemajor cell-types such as pyramidal neuron (Figure 1(b-c)) and interneuron (Figure 1(d-e)).
Pyramidalneurons are the primary long-range output neurons of the brain [35] while interneurons mainly sculptlocal network dynamics through a variety of circuit motifs.
The asymmetric long projecting apical tuftis a unique feature that differentiates pyramidal neuron’s morphology with interneuron’s.
TreeMoCocan further delineate subtypes of pyramidal neuron and interneurons.
In Figure 1 and 4(a), we canclearly see the separation of layer 2/3 (Figure 1(c)) and layer 5 (Figure 1(b)) pyramidal neurons.
Layer2/3 neurons can be distinguished by their shorter apical dendrites with extensive oblique branching[43] while layer 5 neurons feature extended apical dendrites with extensive horizontal branchingin layer 1 and sparse oblique branching in layer 2/3 [14].
The two primary classes of inhibitoryinterneurons (parvalbumin-expressing basket cells and somatostatin-expressing Martinotti cells) arealso clearly distinguished with TreeMoCo.
Parvalbumin-expressing basket cells have extensivebranching field that extends radially around the soma (Figure 1(d))
In contrast, somatostatinexpressing Martinotti cells tend to have unipolar or bipolar dendritic branching that extends verticallyabove and below the soma (Figure 1(e))
It should be noted that such delineation is purelyself-driven without any human labels or any prior knowledge of cell types.
In contrast, the delineationis less clear in GraphCL’s result and not observed in MorphVAE’s result (details in Appendix C).
Quantitative evaluation.
Here we quantitatively evaluate the quality of learned representationsof different methods in classifying the known cell-types.
For supervised methods, we directly trainthem on each dataset. For unsupervised methods, we pre-train them on the joint training sets.
Table3 reports the results, with the best KNN accuracy during unsupervised pre-training or the best testaccuracy during supervised training included.
It can be observed that TreeMoCo trained without labelinformation can already achieve better performance than supervised MorphVAE [22] and TRNN [52].
Fine-tuning the pre-trained neuron encoder further improves cell-type classification.
This is mainlybecause MorphVAE [22] is based on random walk to capture topology information, which ignorestree architecture.
While TRNN [52] has less description power than our neuron encoder in capturinghigh-order interaction between features.
GraphCL [49] outperforms TreeMoCo in the ACT datasetbut falls behind in BIL-6 and JML-4 datasets.
This coincides with the data quality differences (ACTreconstruction is less complete than BIL and JML, more details in Appendix A.2).
One explanationis that GraphCL pays more attention to local context and thus is less sensitive to global changes.
TreeMoCo manifests neuron morphology distribution.
Figure 4 shows the t-SNE plots of neuronrepresentations.
Each plot is colored by a different morphology feature, i.e. , average/total branchlength (c, d), average / standard deviation of contraction (e, f), number of nodes in a neuron (g) andthe height of a neuron tree (h).
Neurons that share similar morphology features are well clustered inthe embedding space (see (1) to (7)), demonstrating the good representations learnt by TreeMoCo. [Table] [Table]
These morphology attributes have not been explicitly provided as input features but it turns out that
TreeMoCo can mine such information by itself, without human labels .
In addition, we can see it bycross-referencing Figure 4 (a) and (b) that neurons of a same class but from different datasets areclustered together in TreeMoCo’s representation space, showing TreeMoCo’s unbiasedness to datasources — a much desirable property for neuron morphology representation learning.
4.3 Ablation on augmentation methods and network architectures
In this subsection, we first assess the importance of data augmentations in contrastive neuronmorphology learning.
Then we ablate on several design choices in our neuron encoder.
To systematically study the effects of different categories of data augmentations, we first groupthe proposed augmentations in Table 1 by their categories, except for the topology augmentation,the one we are more interested in.
Then, we apply them individually or in pairs to TreeMoCoframework with a higher applying probability due to the reduced number of augmentations (detailsin Appendix B.3.) when pre-training.
Note that this ablation does not aim to figure out the bestcompositions of presented augmentations, but to unravel how different augmentations contribute tothe final performance.
For ablation purposes, in this experiment we train on the full BIL-6 dataset for 50 epochs.
Table 4 shows the KNN evaluation results under each individual category or a compositionof data augmentations.
Here we do not specify the order of augmentations, thus resulting a symmetricperformance matrix.
Unlike the observation in visual or graph representation contrastive learningthat no single augmentation performs the best [5, 49], we find in neuron morphology representationlearning certain kinds of augmentations are good enough for pre-training ( e.g. , point transformation,skipping parent node).
Topology augmentations do not necessarily bring better performance. Thiscould be caused by the mechanism of Tree-LSTM, which aggregates the whole tree to generateembeddings and thus is sensitive to the global changes caused by topology augmentation. Betterneuron morphology representations might be excepted if a more proposer set of augmentations or abetter network architecture were applied under the TreeMoCo framework.
Then, we study the effects of different components in our neuron encoder.
For this experiment, wetrain neuron encoders for 100 epochs on three datasets and report their best test performance.
Table5 (left) shows the results, where a steady increasing trend of model performance can be observed.
We also include the KNN accuracy (Tab. 5 right) for 50-epoch pre-training on the joint dataset andevaluated in BIL-6 dataset just for ablation interest.
Here we find that batch normalization ( BN ) isbeneficial for both contrastive self-supervised learning and supervised learning.
No BN in the neuronencoder would give significantly worse results (first line of right table).
Further adding BN in theprojection head is helpful for TreeMoCo.
This behavior has also been reported in non-contrastive SSL methods before, e.g. , BYOL [12, 31], where removing BN causes model collapse.
[Table]
Conclusion and Limitations
In this work, we have explored contrastive learning for neuron morphology representation learningvia TreeMoCo.
Our results on 2403 neuron reconstructions show that TreeMoCo has the uniquepotential to identify unbiased cell-types.
Modern cell-type classification is heavily dependent ontranscriptomic data [50, 8].
However, even neurons within the same mouse possessing identicalgenetic identity and birth-date demonstrate extremely variable dendritic morphology and synapticdistribution [19].
This variability underlies the need for high-throughput neuronal morphologyclassification techniques to probe for the existence of novel subtypes within classically establishedneuronal identities [3].
TreeMoCo has a great potential to fill this need and to explore dendriticarchitecture.
These findings could lead to more rigorous output measurements for drug discoveryexperiments and aid in the development of treatments for neurological disorders characterized byprogressive dendrite degeneration such as Alzheimer’s disease and depression [10, 7].
We note that TreeMoCo is still a preliminary effort of its kind.
Certain limitations can be observedand need to be solved in the future.
For instance, Tree-LSTM aggregates the whole graph and thus issensitive to global changes.
This results in its performance decay on incomplete neuron reconstruction(ACT dataset) and its favor of node-feature-based augmentation over topology-based augmentation.
Moreover, compared to most image contrastive learning frameworks, TreeMoCo’s performance ondownstream tasks could oscillate after long training epochs on small datasets.
An effective earlystopping criterion is needed but absent when label information is not available. Nevertheless, the tree graph is a common data structure representing objects varying from arteryand lung vessels [21] to program source code [26] and HTML documents [4]. Another direction ofour future work is to extend TreeMoCo into a more general framework for learning representationsbeyond neuron morphology. Following the success of contrastive learning in image analysis problems,we believe TreeMoCo could be a powerful tool to solve unsupervised and few-shot learning problemsrelated to a wide range of tree graph data.
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