Untangling dendrites with quantitative models.
By Idan Segev, Michael London. Science, 2000
321 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/40b0e812db252a4fc42a0c2645e04d1556e4891e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Christoph Kayser
- Joachim Gross
- Y. Dan
- Hermann Cuntz
- Alexander Borst
- Friedrich Forstner
- Michael Häusser
- Mu-Ming Poo
In Electrical and Electronic Engineering
- Alexander Borst
- Hermann Cuntz
- Friedrich Forstner
- Michael Häusser
- Y. Dan
- Mu-Ming Poo
- Michael London
- Christoph Kayser
In Statistical and Nonlinear Physics
- Michael Häusser
- Hermann Cuntz
- Alexander Borst
- Friedrich Forstner
- Jakob H. Macke
- Stefano Panzeri
- Joachim Gross
Citations in the corpus
- Neural population coding: combining insights from microscopic and mass signals (2015, background): “A sparse population code may be advantageous for metabolic efficiency and may facilitate dendritic computations requiring the separation of individual synaptic afferents [27,28].”
- Emerging rules for the distributions of active dendritic conductances (2002, background): “Limitations of passive dendritic trees Segev and Londo”
- Spike timing-dependent plasticity: from synapse to perception. (2006, background): “Although spatial inhomogeneity in the electrical properties and nonlinearity in dendritic integration are thought to endow individual pyramidal neurons with enhanced processing capacity (44, 94), the computational power can only be harvested if the inputs carrying different signals are segregated in”
- One Rule to Grow Them All: A General Theory of Neuronal Branching and Its Practical Application (2010, methodology): “…techniques based on the theories of Wilfrid Rall have highlighted the importance of a neuron’s precise branching morphology for its electrophysiological properties [10], and have shown that dendrites can play an important role in the computations performed on the inputs to the cell [11,12].”
- Pyramidal neuron as two-layer neural network. (2003, unknown): “(Häusser et al., 2000; Segev and London, 2000; Stuart Panayiota Poirazi,1,* Terrence Brannon,2 and Bartlett W. Mel3,* et al., 1999) led us to argue previously for a two-layer model of synaptic integration in pyramidal cells (Mel et Institute of Molecular Biology and Biotechnology Foundation for Rese”
- Dendritic computation. (2005, background): “…past decade a rapidly increasing number of studies investigating signaling mechanisms in dendrites have appeared, and several recent reviews have discussed these findings in depth (Euler & Denk 2001, Häusser et al. 2000, Koch & Segev 2000, Magee 2000, Segev & London 2000, Williams & Stuart 2003).”