CoNMoR: 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 introducea self-supervised learning framework named COntrastive Neuron MOrphology Representation learning (CoNMoR) to learn features without the need of humanexperts’ annotations.
We test CoNMoR on 2403 high-quality 3D neuron reconstructions of mouse brains from three different public resources.
Our results showthat CoNMoR is effective in both classifying major brain cell-types and identifyingsub-types.
To our best knowledge, CoNMoR is the very first to explore learningthe representation of neuron tree morphology with contrastive learning.
It has agreat potential in shedding 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 [50].
With recent advances in imaging [9, 11, 46] and computational methods [20, 25, 41] 3D reconstruction of whole neuron trees with high-throughput is now accessible for mouse brain [26, 43].
However,due to the lack of effective measurement, most analysis of these 3D reconstructions are still largelybased on biologists’ visual inspection.
Considering the enormous number of neurons in the brainand the 3D formation of neuron morphology, such visual inspection is subjective, biased, and of lowthroughput.
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 [32].
Only a few attempts have been made in learning deep representations ofneuron morphology.
In [51], 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 [21], 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 [31], 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.
In this paper, we propose a new SSL framework to directly learn neuron morphological featuresfrom 3D dendrite reconstructions without the need of any prior knowledge of a neuron such ascell type annotation. The framework is named COntrastive Neuron MOrphology Representationlearning (CoNMoR).
It first maps neuron trees input to a deep embedding space by our proposedneuron encoder, and then incorporates a SSL pipeline composed by neuron tree augmentations and acontrastive loss to learn representations.
By testing the framework on 2403 neuron reconstructionsfrom 3 different data resources, we are excited to find that CoNMoR can clearly delineate neuronmorphology in the embedding space (Figure 1).
Comparison with prior arts shows that CoNMoR canachieve premium results than supervised methods even without annotations .
15 79  79 56 7 356 1 1 (a) Original (b) Dropped (7) (c) Skipped (5) (d) Swapped (3,7)
[Figure]
contraction
[Figure]
Background and Related Works
Neuron tree reconstruction and heuristic representation.
Neuron reconstruction is usuallyrepresented by a tree structure in a “swc” format [3] as illustrated in Figure 2(a).
The root ofa tree is placed at the soma and the nodes (vertices) with 3D coordinate as well as other nodeproperties are connected to the parent node.
To quantitatively describe the neuron morphology, aset of heuristic features and related tools namely L-measure [32] 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 shapes (examples in Appendix).
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 [32] to predict or clustercell types [39, 23].
In some recent works, researchers started to adopt tree-based model [51, 22] andsequence-based model [21] to classify neuron morphology.
These methods take raw neuron data asinput and predict cell-types in an end-to-end fashion.
Despite competing performance than classicmethods, training of these models requires manual annotations of cell types.
Though [21] can bepre-trained without labels, the performance is limited as the adopted seq2seq model ignores criticaltree structure of neurons.
[31] adopted a triplet-loss to learn consistent embeddings between differentviews of the same neuron branch (Neuron2vec).
The learned embeddings can identify subcellularcompartments and the cell types of neuron reconstructions.
However, this method mainly focuses onlocal shape and organelle.
Such information requires nano resolution imaging techniques that are notapplicable for whole neuron analysis.
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 a decade ago [13] or earlier, and has recently drawn significant interestwithin and across different communities [44, 24, 17, 47, 16, 6, 5].
Some research efforts have beenmade in graph contrastive learning (GCL) [36, 28, 15, 48, 45].
Particularly, GraphCL [48] studies thegraph data augmentations systematically for GCL, and InfoGCL [45] provides a theoretical analysisfor it.
However, to our best knowledge, no work has studied the tree graph, a special instance ofgraph, within the context of CL.
As we discuss later, tree graph is too special to share the same setupsas general GCL.
We subsequently propose several tree-graph-oriented data augmentations that arehelpful for tree CL.
We hope this work could provide a useful data point, a missing piece of jigsaws,for tree graph representation learning.
In addition, we believe we are the very first to explore CL inneuron tree morphology representation learning, which we expect to be a new powerful technique foranalyzing neurons both quantitatively and qualitatively.
Input Tree Υ =
( X ,𝒢) q k
Tree Augmentation
Neuron Encoder
Projection head
Contrasting Σ Σ Σ Σ Σ Σ Σ Σ ˜ x
k z k h k , h k , c k , Σ Σ Σ Σ ˜ x
j ˜ x i z ′ k h
′ k , h ′ k , c ′ k , ˜ X
= ϕ
( X ) z
= η (˜ X ,𝒢) || h ( z ) ||
Projection Augmentation Υ′ =
t (Υ) t ∼ 𝒯 t ′ ∼ 𝒯
LSTM-based Tree Path Processor a b z i h j ,2 c j , h j , z j h i , h i , c i , z ′ i h ′ j ,2 c j , h ′ j , z ′ j h ′ i , h ′
i , c ′ i ,  Methods3.1 Tree graph representation of neuron In this paper, we define a neuron tree as Υ ≜ ( X , G ) , where G represents the tree topology and X = { x k } Nk =1 is an N × (3 + Q ) matrix that is from N nodes with 3D coordinates and Q -dimadditional features.
Intuitively, the reconstruction of a neuron tree can be directly used as model input. However, most of the vertices in raw neuron reconstructions are 1-degree nodes along the branchwhich are less informative but overwhelm bifurcation nodes. Also, the number of such 1-degreenodes is biased to neuron reconstruction pipeline and raw data resolution. We define a completesequence of 1-degree nodes between two bifurcation nodes or a bifurcation node and a tip nodeas a branch .
To normalize neuron tree graphs across data source, we replace branches with edgesdirectly connecting the root node, the bifurcation nodes, and the tip nodes in the graph (Figure 2(b)). To preserve the branch shape information, we calculate related features and concatenate them withthe coordinate property of its downstream node (bifurcation node or tip node). This procedure alsoreduces the complexity of neuron tree graphs and benefits training efficiency. For branch features, length and contraction have been computed.
Branch length L is the accumulateddistance between adjacent vertices along a branch: L = (cid:80) i
||
V i − V
i +
|| 2 .
Branch contractionis the ratio between the Euclidean distance D
=
||
V start − V end
|| 2 and the length L of a branch: C = D/L . Length measures the size of a branch while contraction measures the curviness.
Notably,both measurements are invariant to the order of edges and thus cannot differentiate certain patterns(details in Appendix).
To allow a comprehensive encoding of branch pattern, we resample eachbranch with a fixed number of vertices (8 in this paper) and take the sequence of the edge
vectors⃗E i =
V i − V i +1 as the its shape feature: F E ∈ R 24 .
In addition, the branch orientation is unifiedbefore extracting feature vectors such that the start and end points are on the x-axis and the principledirection is aligned with y-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 [37], to process such data.
Recall the hidden state and the cell state at time t are usually denoted as h t and c t in LSTMs [18].
Here we denote them similarly, but indexed by tree node, as h k and c k at node k .
To aggregateinformation from multiple child nodes, we simply sum over the input states.
The transition equationscan 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).
Ourtree-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[44, 16, 6, 5, 12], has been seen in many other fields, including graph contrastive learning [45, 48]— an overall under-explored direction.
Attempts have been made to study self-supervised graphrepresentation learning in terms of data augmentation [48], network architecture, and overall pipelines[45].
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 [48].
Below we firstintroduce our CoNMoR 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 CoNMoR.
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 [44, 5, 13].
We follow asuccessful exemplar, MoCo [16, 6], in visual contrastive learning to build our tree contrastive learningframework for self-supervised pre-training.
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 from allother negative keys k − .
When using the dot-product as similarity measurement, a form of contrastiveloss function based on InfoNCE [44] becomes:
[Equation]
where τ is a temperature hyper-parameter [44].
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 further map theneuron representations to another latent space using a non-linear projection head h ( · ) and normalizethem by q = || h ( z q ) || [-
 and k = h ′ ( z-]2 , k
= ||
h ′ ( z k )
|| 2 .
Here, f ′ and h ′ stand for the momentum copies of f and h ,named momentum encoder [16].
Therefore, q and k + in Equation 5 are naturally generated from asingle sample, and k − ’s are from a queue [16] that stores features from previous batches.
Discussion: data augmentation on graphs, trees, and neuron morphology.
Data augmentationis known to be essential for self-supervised contrastive learning, where its absence would affect thequality of learned representations significantly [5].
Current successful data augmentations for graphs[48] mainly include (1) “node dropping” that randomly drops graph nodes and their associated edges,(2) “edge perturbation” that randomly adds or drops edges, (3) “attribute masking” that acts likedropout [35] to node features, and (4) “subgraph sampling” that uses random walk to sample a partialgraph.
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.
For example, thenode dropping at a rate of 0.2 (default in [48]) would produce a significantly 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 toperturbations — the golden rule applicable in many cases.
For neuron data, such perturbations mayoccur in (i) node coordinates, (ii) overall morphology, (iii) attribute masking and (iv) topology.
Thuswe 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.
We also include attribute masking (8) used in general graph augmentation.
For (iv) topology, wepropose three different augmentations.
The first is (9) “truncated dropping subtrees” that recursivelyvisits nodes from the root to leaves and drops a subtree with a small probability ( e.g. , 0.05).
Thename “truncated” means at most m trees 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.
We empirically set m = 10 in this paper.
Figure2 (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/ ) [26],(2) Janelia MouseLight (JML, http://mouselight.janelia.org/ ) [43], and (3) Allen Cell Types(ACT, https://celltypes.brain-map.org/ ) [1].
In total, 3358 three-dimensional mouse brainneuron reconstructions are collected (refer to Table 2 for details).
To facilitate analysis, we excludecell types with less than 50 reconstructions, yielding 2403 neurons in total.
Some of the samplesinclude both dendrite and axon reconstructions.
For simplicity, we only keep dendritic arbors inthis 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 [40]). The scales of the reconstructions are the same.
Meanwhile, the absolute coordinates and orientations ofregistered neurons could also carry cell-type information that are related to brain anatomy instead ofneuron morphology.
To eliminate such potential information leakage, all dendrites are shifted androtated such that the root is placed at the origin and the X/Y/Z coordinates are determined basedon principle component analysis (PCA).
Data are then processed and analyzed following the stepsintroduced in the Section 3.1.
In the interest of evaluating CoNMoR’s capability of classifyingknown cell types across data sources, we specifically use the cell types with rich coverage among 3datasets only (grayed cell types in Table 2).
We denote these sub-datasets as BIL-6, JML-4 and ACTrespectively.
For supervised training, 80% samples in each dataset are randomly picked as trainingset and 20% are reserved for testing.
For CoNMoR pre-training, we use all samples except thosefrom class “others”, and evaluate performance in BIL-6, JML-4 and ACT datasets. [Table]
Implementations and evaluation.
For implementation, we set the layer sizes of embedding MLPto 256 and 128, and the cell state size of LSTM-processor to 128. For CoNMoR, we use a queuelength of 1024, a momentum of 0.99 to update momentum encoder and a temperature τ = 0 . 1 for
InfoNCE loss.
For both supervised and self-supervised pre-training, we use a batch size of 128and train for 100 epochs. For data augmentation, we use all the listed augmentations by default. More implementation details and augmentation details such as probability and hyper-parameters areprovided in Appendix.
For evaluation, we report the sample-wise accuracy.
During pre-training, weevaluate the K Nearest-Neighbor (KNN) accuracy that uses training samples to vote for test samples.
We set the number of neighbors to 20 for BIL-6 and ACT datasets, and 5 for JML-4 datasets since aminority class might only have 12 samples in total in JML-4.
4.2 CoNMoR delineates neuron morphology in embedding space
In this subsection, we demonstrate that CoNMoR can learn neuron tree representations that manifestdifferent characteristics of neuron morphology without any human labels .
To that end, we first followprocedures described in Section 3.3 to conduct CoNMoR pre-training and use the pre-trained neuronencoder to extract features from all three datasets (BIL, JML and ACT) and project them into a 2Dspace by t-SNE [38]. Figure 1 and 4 shows the thumbnails of all neurons placed at their corresponding2D t-SNE coordinates. Visual inspection of cell types delineation.
By visual inspection, it is evidenced that CoNMoRcan separate major cell-types such as pyramidal neuron (Figure 1(b-c)) and interneuron (Figure1(d-e)) in the embedding space.
Pyramidal neurons are the primary long-range output neurons ofthe brain [34] while interneurons mainly sculpt local network dynamics through a variety of circuitmotifs.
The asymmetric long projecting apical tuft is a unique feature that differentiates pyramidalneuron’s morphology with interneuron’s.
CoNMoR can further delineate subtypes of pyramidalneuron and interneurons.
In Figure 1 and 4(a), we can clearly see the separation of layer 2/3 (Figure1(c)) and layer 5 (Figure 1(b)) pyramidal neurons.
Layer 2/3 neurons can be distinguished by theirshorter apical dendrites with extensive oblique branching [42] while layer 5 neurons feature extendedapical dendrites with extensive horizontal branching in layer 1 and sparse oblique branching inlayer 2/3 [14].
The two primary classes of inhibitory interneurons (parvalbumin-expressing basketcells and somatostatin-expressing Martinotti cells) are also clearly distinguished with CoNMoR.
Parvalbumin-expressing basket cells have extensive branching field that extends radially around thesoma (Figure 1(d))
In contrast, somatostatin-expressing Martinotti cells tend to have unipolaror bipolar dendritic branching that extends vertically above and below the soma (Figure 1(e))
It should be noted that such delineation is purely self-driven without any human labels or any priorknowledge of cell types. [Table] [Table] [Figure]
[Table]
[Figure]
[Table] [Figure] [Table] Refer to Appendix for high-resolution figures.
Quantitative evaluation.
Here we quantitatively evaluate CoNMoR by a pre-training & fine-tuningprotocol [5] in classifying the known cell-types.
For this setting, we pre-train a neuron encoder injoint dataset (merging three datasets) for 100 epochs and fine-tune it on each individual datasets.
Table 3 report the results, with the best KNN evaluation accuracy during pre-training included.
It canbe observed that CoNMoR trained without label information can already achieve better performancethan supervised MorphVAE [21] and TRNN [51].
Fine-tuning the pre-trained neuron encoder furtherimproves cell-type classification.
This is mainly because MorphVAE [21] is based on random walk tocapture topology information, which ignores tree architecture.
While TRNN [51] has less descriptionpower than our neuron encoder in capturing high-order interaction between features.
CoNMoR 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 CoNMoR.
These morphology attributes have not been explicitly provided as input features but it turns out that
CoNMoR 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 CoNMoR’s representation space, showing CoNMoR’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 as well as to understand how different augmentations contribute to it.
Then weablate on several design choices in our neuron encoder.
To systematically study the effects of different categories of data augmentations, we first group theproposed augmentations in Table 1 by their categories except the topology augmentation, the onewe are more interested in.
Then, we apply them individually or in pairs to CoNMoR frameworkwhen pre-training.
For ablation purpose, in this experiment we train on the joint dataset for 50epochs.
Table 4 shows the KNN evaluation results under each individual category or a composition ofdata 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, 48], we find in neuron morphology representationlearning certain kinds of augmentations are good enough for pre-training ( e.g. , point transformation, skipping parent node).
Besides, compared to Node Dropping [48], even our “truncated dropsubtrees” applies under a significantly smaller probability (0.05 v.s. 0.2 in [48]) and a truncationconstraint, the resulted performance (drop) is sometimes unsatisfactory, showing the importance ofdata augmentation designs with respect to tree structure data.
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 it interesting that BN becomes moreimportant in contrastive self-supervised learning than in supervised learning.
No BN in the neuronencoder would give significantly worse results (first line).
Further adding BN in the projection head ishelpful for CoNMoR.
This behavior has also been reported in non-contrastive SSL methods before,e.g. , BYOL [12, 29], where removing BN causes model collapse.
[Table]
Conclusion
In this work, we propose CoNMoR — a new SSL framework for tree structure representationlearning.
To our best knowledge, CoNMoR is the very first to explore learning the representationof neuron tree morphology with contrastive learning.
Our results on 2403 neuron reconstructionsshow that CoNMoR has the unique potential to identify unbiased cell-types.
Modern cell-typeclassification is heavily dependent on transcriptomic data [49, 8].
However, even neurons withinthe same mouse possessing identical genetic identity and birth-date demonstrate extremely variabledendritic morphology and synaptic distribution [19].
This variability underlies the need for highthroughput neuronal morphology classification techniques to probe for the existence of novel subtypeswithin classically established neuronal identities [4].
CoNMoR has a great potential to fill this needand to explore dendritic architecture not only in space, but also in time: especially to identify stages ofdevelopment and disease progression [30].
These findings could aid in the development of treatmentsfor neurological disorders characterized by progressive dendrite degeneration such as Alzheimer’sdisease and depression [10, 7].
Taken together, CoNMoR has immense potential to benefit humanhealth by providing more rigorous output measurements for drug discovery experiments and possiblyeven diagnostic tools for human patients as noninvasive brain imaging continues to improve.
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