Geometric trees are characterized by their tree-structured layout and spatially constrained nodes and edges, which significantly impacts their topological attributes. This inherent hierarchical structure plays a crucial role in domains such as neuron morphology and river geomorphology, but traditional graph representation methods often overlook these specific characteristics of tree structures. To address this, we introduce a new representation learning framework tailored for geometric trees. It first features a unique message passing neural network, which is both provably geometrical structure-recoverable and rotation-translation invariant. To address the data label scarcity issue, our approach also includes two innovative training targets that reflect the hierarchical ordering and geometric structure of these geometric trees. This enables fully self-supervised learning without explicit labels. We validate our method's effectiveness on eight real-world datasets, demonstrating its capability to represent geometric trees.

Changes in science practices are often perceived to be slow.It took about 10 years from the Collins and Tabak editorial on scientific reproducibility in 2014 [1] to see data management mandates implemented by US funding agencies [2].However, open science practices have seen a sharp increase in adoption over the last few years, supported by policy (for example, those by the European Commission or the 2022 White House Office of Science and Technology Policy (OSTP) memoAU : Pleasecheckandconfirmthattheplacementofclosingparenthesisinthesenten ) [3,4] as well as new generations of digital tools and scientists who are embedding open values in their research practices.In this faster-paced open science environment, universities are key to fostering adoption among researchers.Universities drive implementation by advancing best practices and accounting for the needs and norms of diverse departments and disciplines.Universities are positioned to catalyze adoption of open practices through their academic evaluation processes, particularly, recruitment, tenure, and promotion.The capacity of researchers and instructors to engage with data and software scholarship will shape the next generation of students and scientists, and universities will play a crucial role in nurturing those skills by rewarding such contributions and expertise among their faculty.The ways in which promotion and tenure committees operate vary significantly across universities and departments.While committees often have the capability to evaluate the rigor and quality of articles and monographs in their scientific field, assessment with respect to practices concerning research data and software is a recent development and one that can be harder to implement, as there are few guidelines to facilitate the process.More specifically, the guidelines given to tenure and promotion committees often reference data and software in general terms, with some notable exceptions such as guidelines in [5] and are almost systematically trumped by other factors such as the number and perceived impact of journal publications.The core issue is that many colleges establish a scholarship versus service dichotomy: Peer-reviewed articles or monographs published by university presses are considered

SUMMARY: Neural morphology, the branching geometry of brain cells, is an essential cellular substrate of nervous system function and pathology. Despite the accelerating production of digital reconstructions of neural morphology, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and limit synergy. We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via NeuroMorpho.Org leads to a significant increase of citations to the original article, thus directly benefiting authors. The rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative applications, which accrue on average twice the citations of re-analyses of individual datasets. We also released an open-source citation tracking web-service allowing researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and tools can facilitate the recognition of shared data reuse for merit evaluations and funding decisions. AVAILABILITY AND IMPLEMENTATION: The application is available at: http://cng-nmo-dev3.orc.gmu.edu:8181/. The source code at https://github.com/HerveEmissah/nmo-authors-app and https://github.com/HerveEmissah/nmo-bibliometric-analysis.

MOTIVATION: Large-scale neuronal morphologies are essential to neuronal typing, connectivity characterization and brain modeling. It is widely accepted that automation is critical to the production of neuronal morphology. Despite previous survey papers about neuron tracing from light microscopy data in the last decade, thanks to the rapid development of the field, there is a need to update recent progress in a review focusing on new methods and remarkable applications. RESULTS: This review outlines neuron tracing in various scenarios with the goal to help the community understand and navigate tools and resources. We describe the status, examples and accessibility of automatic neuron tracing. We survey recent advances of the increasingly popular deep-learning enhanced methods. We highlight the semi-automatic methods for single neuron tracing of mammalian whole brains as well as the resulting datasets, each containing thousands of full neuron morphologies. Finally, we exemplify the commonly used datasets and metrics for neuron tracing bench testing.

BACKGROUND: The biomedical literature is expanding at ever-increasing rates, and it has become extremely challenging for researchers to keep abreast of new data and discoveries even in their own domains of expertise. We introduce PaperBot, a configurable, modular, open-source crawler to automatically find and efficiently index peer-reviewed publications based on periodic full-text searches across publisher web portals. RESULTS: PaperBot may operate stand-alone or it can be easily integrated with other software platforms and knowledge bases. Without user interactions, PaperBot retrieves and stores the bibliographic information (full reference, corresponding email contact, and full-text keyword hits) based on pre-set search logic from a wide range of sources including Elsevier, Wiley, Springer, PubMed/PubMedCentral, Nature, and Google Scholar. Although different publishing sites require different search configurations, the common interface of PaperBot unifies the process from the user perspective. Once saved, all information becomes web accessible allowing efficient triage of articles based on their actual relevance and seamless annotation of suitable metadata content. The platform allows the agile reconfiguration of all key details, such as the selection of search portals, keywords, and metadata dimensions. The tool also provides a one-click option for adding articles manually via digital object identifier or PubMed ID. The microservice architecture of PaperBot implements these capabilities as a loosely coupled collection of distinct modules devised to work separately, as a whole, or to be integrated with or replaced by additional software. All metadata is stored in a schema-less NoSQL database designed to scale efficiently in clusters by minimizing the impedance mismatch between relational model and in-memory data structures. CONCLUSIONS: As a testbed, we deployed PaperBot to help identify and manage peer-reviewed articles pertaining to digital reconstructions of neuronal morphology in support of the NeuroMorpho.Org data repository. PaperBot enabled the custom definition of both general and neuroscience-specific metadata dimensions, such as animal species, brain region, neuron type, and digital tracing system. Since deployment, PaperBot helped NeuroMorpho.Org more than quintuple the yearly volume of processed information while maintaining a stable personnel workforce.

Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with or without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively different responses, such as spiking and bursting under different input conditions, thus forming a complex phenotype of responses. In previous work, the comprehensive knowledge base of hippocampal neuron types Hippocampome.org systematically characterized various spike pattern phenotypes experimentally identified from 120 neuron types/subtypes. In this paper, we present a complete set of simple phenomenological models that quantitatively reproduce the diverse and complex phenotypes of hippocampal neurons. In addition to point-neuron models, we created compact multi-compartment models with up to four compartments, which will allow spatial segregation of synaptic integration in network simulations. Electrotonic compartmentalization observed in our compact multi-compartment models is qualitatively consistent with experimental observations. The models were created using an automated pipeline based on evolutionary algorithms. This work maps 120 neuron types/subtypes in the rodent hippocampus to a low-dimensional model space and adds another dimension to the knowledge accumulated in Hippocampome.org. Computationally efficient representations of intrinsic dynamics, along with other pieces of knowledge available in Hippocampome.org, provide a biologically realistic platform to explore the large-scale interactions of various neuron types at the mesoscopic level.

BACKGROUND: The morphology of neurons offers many insights into developmental processes and signal processing. Numerous reports have focused on metrics at the level of individual branches or whole arbors; however, no studies have attempted to quantify repeated morphological patterns within neuronal trees. We introduce a novel sequential encoding of neurite branching suitable to explore topological patterns. RESULTS: Using all possible branching topologies for comparison we show that the relative abundance of short patterns of up to three bifurcations, together with overall tree size, effectively capture the local branching patterns of neurons. Dendrites and axons display broadly similar topological motifs (over-represented patterns) and anti-motifs (under-represented patterns), differing most in their proportions of bifurcations with one terminal branch and in select sub-sequences of three bifurcations. In addition, pyramidal apical dendrites reveal a distinct motif profile. CONCLUSIONS: The quantitative characterization of topological motifs in neuronal arbors provides a thorough description of local features and detailed boundaries for growth mechanisms and hypothesized computational functions.

BACKGROUND: The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively. We implemented a modified global alignment algorithm representing axonal and dendritic bifurcations as strings of characters. Sequence alignment quantifies neuronal similarity by identifying branch-level correspondences between trees. RESULTS: The space generated from pairwise similarities is capable of classifying neuronal arbor types as well as, or better than, traditional topological metrics. Unsupervised cluster analysis produces groups that significantly correspond with known cell classes for axons, dendrites, and pyramidal apical dendrites. Furthermore, the distinguishing consensus topology generated by multiple sequence alignment of a group of neurons reveals their shared branching blueprint. Interestingly, the axons of dendritic-targeting interneurons in the rodent cortex associates with pyramidal axons but apart from the (more topologically symmetric) axons of perisomatic-targeting interneurons. CONCLUSIONS: Global pairwise and multiple sequence alignment of neurite topologies enables detailed comparison of neurites and identification of conserved topological features in alignment-defined clusters. The methods presented also provide a framework for incorporation of additional branch-level morphological features. Moreover, comparison of multiple alignment with motif analysis shows that the two techniques provide complementary information respectively revealing global and local features.

Experiencing certain events triggers the acquisition of new memories. Although necessary, however, actual experience is not sufficient for memory formation. One-trial learning is also gated by knowledge of appropriate background information to make sense of the experienced occurrence. Strong neurobiological evidence suggests that long-term memory storage involves formation of new synapses. On the short time scale, this form of structural plasticity requires that the axon of the pre-synaptic neuron be physically proximal to the dendrite of the post-synaptic neuron. We surmise that such "axonal-dendritic overlap" (ADO) constitutes the neural correlate of background information-gated (BIG) learning. The hypothesis is based on a fundamental neuroanatomical constraint: an axon must pass close to the dendrites that are near other neurons it contacts. The topographic organization of the mammalian cortex ensures that nearby neurons encode related information. Using neural network simulations, we demonstrate that ADO is a suitable mechanism for BIG learning. We model knowledge as associations between terms, concepts or indivisible units of thought via directed graphs. The simplest instantiation encodes each concept by single neurons. Results are then generalized to cell assemblies. The proposed mechanism results in learning real associations better than spurious co-occurrences, providing definitive cognitive advantages.

We briefly identify several critical issues in current computational neuroscience, and present our opinions on potential solutions based on bioimage informatics, especially automated image computing.

The firing rate of individual neurons depends on the firing frequency of their distributed synaptic inputs, with linear and nonlinear relations subserving different computational functions. This letter explores the relationship between the degree of synchrony among excitatory synapses and the linearity of the response using detailed compartmental models of cortical pyramidal cells. Synchronous input resulted in a linear input-output relationship, while asynchronous stimulation yielded sub- and supraproportional outputs at low and high frequencies, respectively. The dependence of input-output linearity on synchrony was sigmoidal and considerably robust with respect to dendritic location, stimulus irregularity, and alteration of active and synaptic properties. Moreover, synchrony affected firing rate differently at lower and higher input frequencies. A reduced integrate-and-fire model suggested a mechanism explaining these results based on spatiotemporal integration, with fundamental implications relating synchrony to memory encoding.

Computational modeling of neuronal morphology is a powerful tool for understanding developmental processes and structure-function relationships. We present a multifaceted approach based on stochastic sampling of morphological measures from digital reconstructions of real cells. We examined how dendritic elongation, branching, and taper are controlled by three morphometric determinants: Branch Order, Radius, and Path Distance from the soma. Virtual dendrites were simulated starting from 3,715 neuronal trees reconstructed in 16 different laboratories, including morphological classes as diverse as spinal motoneurons and dentate granule cells. Several emergent morphometrics were used to compare real and virtual trees. Relating model parameters to Branch Order best constrained the number of terminations for most morphological classes, except pyramidal cell apical trees, which were better described by a dependence on Path Distance. In contrast, bifurcation asymmetry was best constrained by Radius for apical, but Path Distance for basal trees. All determinants showed similar performance in capturing total surface area, while surface area asymmetry was best determined by Path Distance. Grouping by other characteristics, such as size, asymmetry, arborizations, or animal species, showed smaller differences than observed between apical and basal, pointing to the biological importance of this separation. Hybrid models using combinations of the determinants confirmed these trends and allowed a detailed characterization of morphological relations. The differential findings between morphological groups suggest different underlying developmental mechanisms. By comparing the effects of several morphometric determinants on the simulation of different neuronal classes, this approach sheds light on possible growth mechanism variations responsible for the observed neuronal diversity.

The task of designing cognitive tests and challenges for robots and intelligent agents is vital for guidance and evaluation of the development of cognitive architectures aimed at the human level of intelligence. In addressing this task, the tremendous store of knowledge of experimental psychology cannot be ignored. At the same time, methods and test paradigms used for evaluation of humans and/or brain models need to be adjusted before they can be used for evaluation of artifacts. The present work is focused on this problem and its possible solutions in one general paradigm of ontogenesis in virtual environment. We propose several test scenarios and metrics that are adequate to assess cognitive features in computational agents.

According to dendritic cable theory, proximal synapses give rise to inputs with short delay, high amplitude, and short duration. In contrast, inputs from distal synapses have long delays, low amplitude, and long duration. Large scale neural networks are seldom built with realistically layered synaptic architectures and corresponding electrotonic parameters. A complete representation of electrotonic dynamics implies the computationally expensive solution of cable differential equations. Here, we use a simpler model to investigate the spike response dynamics of networks with different electrotonic structures. The networks consist of a layer of neurons receiving a sparse feedforward projection from a set of inputs, as well as sparse recurrent connections from within the layer. Firing patterns are set in the set of inputs, and recorded from the neuron (output) layer. The feedforward and recurrent synapses are independently set as proximal or distal, representing dendritic connections near or far from the soma, respectively. Analyses of firing dynamics indicate that recurrent distal synapses tend to concentrate network activity in fewer neurons, while proximal recurrent synapses result in a more homogeneous activity distribution. In addition, when the feedforward input is regular (spiking or bursting) and asynchronous, the output is regular if recurrent synapses are more distal than feedforward ones, and irregular in the opposite configuration. Finally, the amplitude of network fluctuations in response to asynchronous input is lower if feedforward and recurrent synapses are electrotonically distant from one another (in either configuration). In conclusion, electrotonic effects reflecting different dendritic positions of synaptic inputs significantly influence network dynamics.

Visual inspection of neurons suggests that dendritic orientation may be determined both by internal constraints (e.g. membrane tension) and by external vector fields (e.g. neurotrophic gradients). For example, basal dendrites of pyramidal cells appear nicely fan-out. This regular orientation is hard to justify completely with a general tendency to grow straight, given the zigzags observed experimentally. Instead, dendrites could (A) favor a fixed (“external”) direction, or (B) repel from their own soma. To investigate these possibilities quantitatively, reconstructed hippocampal cells were subjected to Bayesian analysis. The statistical model combined linearly factors A and B, as well as the tendency to grow straight. For all morphological classes, B was found to be significantly positive and consistently greater than A. In addition, when dendrites were artificially re-oriented according to this model, the resulting structures closely resembled real morphologies. These results suggest that somatodendritic repulsion may play a role in determining dendritic orientation. Since hippocampal cells are very densely packed and their dendritic trees highly overlap, the repulsion must be cell- specific. We discuss possible mechanisms underlying such specificity. 1 Int r oduc t i on The study of brain dynamics and development at the cellular level would greatly benefit from a standardized, accurate and yet succinct statistical model characterizing the morphology of major neuronal classes. Such model could also provide a basis for simulation of anatomically realistic virtual neurons [1]. The model should accurately distinguish among different neuronal classes: a morphological difference between classes would be captured by a difference in model parameters and reproduced in generated virtual neurons. In addition, the model should be self-consistent: there should be no statistical difference in model parameters measured from real neurons of a given class and from virtual neurons of the same class. The assumption that a simple statistical model of this sort exists relies on the similarity of average environmental and homeostatic conditions encountered by individual neurons during development and on the limited amount of genetic information that underlies differentiation of neuronal classes. Previous research in computational neuroanatomy has mainly focused on the topology and internal geometry of dendrites (i.e., the properties described in “dendrograms”) [2,3]. Recently, we attempted to include spatial orientation in the models, thus generating 2 virtual neurons in 3D [4]. Dendritic growth was assumed to deviate from the straight direction both randomly and based on a constant bias in a given direction, or “tropism”. Different models of tropism (e.g. along a fixed axis, towards a plane, or away from the soma) had dramatic effects on the shape of virtual neurons [5]. Our current strategy is to split the problem of finding a statistical model describing neuronal morphology in two parts. First, we maintain that the topology and the internal geometry of a particular dendritic tree can be described independently of its 3D embedding (i.e., the set of local dendritic orientations). At the same time, one and the same internal geometry (e.g., the experimental dendrograms obtained from real neurons) may have many equally plausible 3D embeddings that are statistically consistent with the anatomical characteristics of that neuronal class. The present work aims at finding a minimal statistical model describing local dendritic orientation in experimentally reconstructed hippocampal principal cells. Hippocampal neurons have a polarized shape: their dendrites tend to grow from the soma as if enclosed in cones. In pyramidal cells, basal and apical dendrites invade opposite hemispaces (fig. 1A), while granule cell dendrites all invade the same hemispace. This behavior could be caused by a tendency to grow towards the layers of incoming fibers to establish synapses. Such tendency would correspond to a tropism in a direction roughly parallel to the cell main axis. Alternatively, dendrites could initially stem in the appropriate (possibly genetically determined) directions, and then continue to grow approximately in a radial direction from the soma. A close inspection of pyramidal (basal) trees suggests that dendrites may indeed be repelled from their soma (Fig. 1B). A typical dendrite may reorient itself (arrow) to grow nearly straight along a radius from the soma. Remarkably, this happens even after many turns, when the initial direction is lost. Such behavior may be hard to explain without tropism. If the deviations from straight growth were random, one should be able to “remodel” th e trees by measuring and reproducing the statistics of local turn angles, assuming its independence of dendritic orientation and location. Figure 1C shows the cell from 1A after such remodeling. In this case basal and apical dendrites retain only their initial (stemming) orientations from the original data. The resulting “cotton ball” s uggests that dendritic turns are not in dependent of dendritic orientation. In this paper, we use Bayesian analysis to quantify the dendritic tropism.