Definition
Computational neuroanatomy is a subfield of neuroscience that aims to create functionally and structurally accurate models of the nervous system. These models are based on a wide range of input data, including microscopy and electrophysiology. These data acquisition methods encompass the broad scales across which neuroanatomy plays a role: (1) nanostructure describing individual connections between cells, (2) microstructure describing neuronal shape and cell type, and (3) gross structure describing cross-brain connectivity patterns. Computing in this field draws largely from the need to synthesize multiple modalities to statistically generate accurate models of morphology. For example, properties such as morphological structure, connection topology, wiring principles, and growth rules are statistically drawn from multiple sources and combined into motifs representing large-scale connectivity. High-performance computing can then be leveraged to simulate and verify the...
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Mayerich, D., Choe, Y. (2021). Computational Neuroanatomy: Overview. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_50-2
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DOI: https://doi.org/10.1007/978-1-4614-7320-6_50-2
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Latest
Computational Neuroanatomy: Overview- Published:
- 06 December 2020
DOI: https://doi.org/10.1007/978-1-4614-7320-6_50-2
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Original
Computational Neuroanatomy: Overview- Published:
- 26 March 2014
DOI: https://doi.org/10.1007/978-1-4614-7320-6_50-1