Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Reconstruction, Techniques, and Validation

  • David MayerichEmail author
  • Yoonsuck Choe
  • John Keyser
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_288-2


Reconstruction algorithms are used to build a geometric model of neurons, describing their morphology, from two-dimensional and three-dimensional images. Validation methods are used to determine the accuracy of the resulting models. Both reconstruction and validation are active areas of research, where proposed algorithms must address several complex problems, including (a) the use of a wide range of imaging modalities used to collect data, (b) the complex topological structure of interconnected branches within the neurons, and (c) automation for large data sets. Several methods have been proposed for addressing these issues; however, current reconstruction algorithms can be broadly placed into four categories: semiautomated software packages, local exploration, global processing, and crowdsourcing-based approaches.

Detailed Description

Neuronal reconstructions provide geometric representations of cell and network morphology that can be used to perform quantitative analysis...


Seed Point Electron Microscopy Data Multicompartmental Model Supervise Learning Technique Graph Isomorphism Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Department of Computer Science and EngineeringTexas A&M UniversityCollege StationUSA