Maximization of the connectivity repertoire as a statistical principle governing the shapes of dendritic arbors. Export

@article{citeulike:5297762, abstract = {The shapes of dendritic arbors are fascinating and important, yet the principles underlying these complex and diverse structures remain unclear. Here, we analyzed basal dendritic arbors of 2,171 pyramidal neurons sampled from mammalian brains and discovered 3 statistical properties: the dendritic arbor size scales with the total dendritic length, the spatial correlation of dendritic branches within an arbor has a universal functional form, and small parts of an arbor are self-similar. We proposed that these properties result from maximizing the repertoire of possible connectivity patterns between dendrites and surrounding axons while keeping the cost of dendrites low. We solved this optimization problem by drawing an analogy with maximization of the entropy for a given energy in statistical physics. The solution is consistent with the above observations and predicts scaling relations that can be tested experimentally. In addition, our theory explains why dendritic branches of pyramidal cells are distributed more sparsely than those of Purkinje cells. Our results represent a step toward a unifying view of the relationship between neuronal morphology and function.}, author = {Wen, Quan and Stepanyants, Armen and Elston, Guy N. and Grosberg, Alexander Y. and Chklovskii, Dmitri B.}, citeulike-article-id = {5297762}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0901530106}, citeulike-linkout-1 = {http://www.pnas.org/content/106/30/12536.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/106/30/12536.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19622738}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19622738}, day = {28}, doi = {10.1073/pnas.0901530106}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {dendritic, wiring, wiring\_economy}, month = {July}, number = {30}, pages = {12536--12541}, posted-at = {2009-10-09 11:51:16}, priority = {0}, title = {Maximization of the connectivity repertoire as a statistical principle governing the shapes of dendritic arbors.}, url = {http://dx.doi.org/10.1073/pnas.0901530106}, volume = {106}, year = {2009} }

TY - JOUR ID - citeulike:5297762 L3 - citeulike-article-id:5297762 N2 - The shapes of dendritic arbors are fascinating and important, yet the principles underlying these complex and diverse structures remain unclear. Here, we analyzed basal dendritic arbors of 2,171 pyramidal neurons sampled from mammalian brains and discovered 3 statistical properties: the dendritic arbor size scales with the total dendritic length, the spatial correlation of dendritic branches within an arbor has a universal functional form, and small parts of an arbor are self-similar. We proposed that these properties result from maximizing the repertoire of possible connectivity patterns between dendrites and surrounding axons while keeping the cost of dendrites low. We solved this optimization problem by drawing an analogy with maximization of the entropy for a given energy in statistical physics. The solution is consistent with the above observations and predicts scaling relations that can be tested experimentally. In addition, our theory explains why dendritic branches of pyramidal cells are distributed more sparsely than those of Purkinje cells. Our results represent a step toward a unifying view of the relationship between neuronal morphology and function. IS - 30 JF - Proceedings of the National Academy of Sciences of the United States of America SN - 1091-6490 EP - 12541 TI - Maximization of the connectivity repertoire as a statistical principle governing the shapes of dendritic arbors. VL - 106 SP - 12536 KW - dendritic KW - wiring KW - wiring_economy AU - Wen, Quan AU - Stepanyants, Armen AU - Elston, Guy AU - Grosberg, Alexander AU - Chklovskii, Dmitri PY - 2009/07/28/ UR - http://dx.doi.org/10.1073/pnas.0901530106 ER -