Editing Dendrite (section)

==Plasticity ==
Dendrites themselves appear to be capable of [[synaptic plasticity|plastic changes]] during the adult life of animals, including invertebrates.<ref> Michmizos D, Koutsouraki E, Asprodini E, Baloyannis S. 2011. Synaptic Plasticity: A Unified Model to Address Some Persisting Questions. ''International Journal of Neuroscience'', 121(6): 289-304. https://www.tandfonline.com/doi/abs/10.3109/00207454.2011.556283</ref> Neuronal dendrites have various compartments known as functional units that are able to compute incoming stimuli. These functional units are involved in processing input and are composed of the subdomains of dendrites such as spines, branches, or groupings of branches. Therefore, plasticity that leads to changes in the dendrite structure will affect communication and processing in the cell. During development, dendrite morphology is shaped by intrinsic programs within the cell's genome and extrinsic factors such as signals from other cells. But in adult life, extrinsic signals become more influential and cause more significant changes in dendrite structure compared to intrinsic signals during development. In females, the dendritic structure can change as a result of physiological conditions induced by hormones during periods such as pregnancy, lactation, and following the estrous cycle. This is particularly visible in pyramidal cells of the CA1 region of the hippocampus, where the density of dendrites can vary up to 30%.<ref name=Tavosanis />

Recent experimental observations suggest that adaptation is performed in the neuronal dendritic trees, where the timescale of adaptation was observed to be as low as several seconds.<ref>{{cite journal | vauthors = Hodassman S, Vardi R, Tugendhaft Y, Goldental A, Kanter I | title = Efficient dendritic learning as an alternative to synaptic plasticity hypothesis | journal = Scientific Reports | volume = 12 | issue = 1 | pages = 6571 | date = April 2022 | pmid = 35484180 | pmc = 9051213 | doi = 10.1038/s41598-022-10466-8 | bibcode = 2022NatSR..12.6571H }}</ref><ref>{{cite journal | vauthors = Sardi S, Vardi R, Goldental A, Sheinin A, Uzan H, Kanter I | title = Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links | journal = Scientific Reports | volume = 8 | issue = 1 | pages = 5100 | date = March 2018 | pmid = 29572466 | pmc = 5865176 | doi = 10.1038/s41598-018-23471-7 | bibcode = 2018NatSR...8.5100S }}</ref> Certain machine learning architectures based on dendritic trees have been shown to simplify the learning algorithm without affecting performance.<ref>{{cite journal | vauthors = Meir Y, Ben-Noam I, Tzach Y, Hodassman S, Kanter I | title = Learning on tree architectures outperforms a convolutional feedforward network | journal = Scientific Reports | volume = 13 | issue = 1 | pages = 962 | date = January 2023 | pmid = 36717568 | pmc = 9886946 | doi = 10.1038/s41598-023-27986-6 | bibcode = 2023NatSR..13..962M }}</ref>