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- W3019473200 abstract "Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and utilizing form-function principles in realistic neuronal reconstructions." @default.
- W3019473200 created "2020-05-01" @default.
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- W3019473200 date "2020-04-29" @default.
- W3019473200 modified "2023-10-17" @default.
- W3019473200 title "Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification" @default.
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- W3019473200 doi "https://doi.org/10.1007/s12021-020-09466-8" @default.
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