Abstract
Computational models are important tools for determining dendritic properties and for understanding their functional roles. However, these models are limited by simulation time and storage requirements, particularly when modeling neuronal networks. We review reduced models of the neuron that accurately report the transmembrane potential at a few specified locations while retaining dendritic properties, including the spatial distribution of synaptic inputs throughout the dendritic tree. These models are rooted in two classes of methods from linear algebra: methods based on the singular value decomposition and moment-matching methods. The reduced models can be used to further elucidate dendritic function as they greatly reduce the computational cost associated with simulating networks of morphologically accurate neurons. We demonstrate this capability by simulating a network of hippocampal pyramidal cells and interneurons coupled through chemical synapses and electrical gap junctions.
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Acknowledgements
This work is supported by NSF grant DMS-1122455 and by a training fellowship from the Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIBIB Grant No. 1T32EB006350-01A1).
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Hedrick, K.R., Cox, S.J. (2014). Morphological Reduction of Dendritic Neurons. In: Cuntz, H., Remme, M., Torben-Nielsen, B. (eds) The Computing Dendrite. Springer Series in Computational Neuroscience, vol 11. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8094-5_29
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DOI: https://doi.org/10.1007/978-1-4614-8094-5_29
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