ArticleFrontiers in neuroanatomy2014
Context-aware modeling of neuronal morphologies.
Article in Frontiers in neuroanatomy, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Article
- Efficient simulation of neural development using shared memory parallelization.Frontiers in neuroinformatics · 2023Article
- Mathematical models of neuronal growth.Biomechanics and modeling in mechanobiology · 2022Review
- Digital Reconstruction of the Neuro-Glia-Vascular Architecture.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction.eLife · 2020Article
- A generative growth model for thalamocortical axonal branching in primary visual cortex.PLoS computational biology · 2020Article
- Generation of Granule Cell Dendritic Morphologies by Estimating the Spatial Heterogeneity of Dendritic Branching.Frontiers in computational neuroscience · 2020Article
- A stochastic framework to model axon interactions within growing neuronal populations.PLoS computational biology · 2018Article
- Theoretical Models of Neural Development.iScience · 2018Review
- Spatial registration of neuron morphologies based on maximization of volume overlap.BMC bioinformatics · 2018Article
- Design and implementation of multi-signal and time-varying neural reconstructions.Scientific data · 2018Article
- Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.Cerebral cortex (New York, N.Y. : 1991) · 2016Article
- Reconstructing the brain: from image stacks to neuron synthesis.Brain informatics · 2016Article
- Is realistic neuronal modeling realistic?Journal of neurophysiology · 2016Review
- New paradigm to assess brain cell morphology by diffusion-weighted MR spectroscopy in vivo.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- ViSimpl: Multi-View Visual Analysis of Brain Simulation Data.Frontiers in neuroinformatics · 2016Article
- Topological characterization of neuronal arbor morphology via sequence representation: I--motif analysis.BMC bioinformatics · 2015Article
- REMOD: A Tool for Analyzing and Remodeling the Dendritic Architecture of Neural Cells.Frontiers in neuroanatomy · 2015Article
- Editorial: Quantitative Analysis of Neuroanatomy.Frontiers in neuroanatomy · 2015Article
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2 authors.
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Abstract
neuronal morphologies are pivotal for brain functioningphysical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction) with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation. Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate. As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling.
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