Dendritic computation.

By Michael London, Michael Häusser. Annual Review of Neuroscience, 2005

1,093 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/81cf8182d5725d5fdd9a33b65843f2f9fdb6a9f6

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Canonical microcircuits for predictive coding. (2012, background): “Some authors emphasize that even within a dendrite, there are all the necessary biophysical mechanisms for performing surprisingly advanced computations, such as direction selectivity, coincidence detection, or temporal integration (Häusser and Mel, 2003; London and Häusser, 2005).”
Dendritic excitability and synaptic plasticity. (2008, neutral): “In the past few decades, however, many neuroscientists have come to appreciate the importance of the vast dendritic arborizations with which most neurons are endowed (445).”
Simultaneous multi-plane imaging of neural circuits (2016, positive): “The extended axial range of our method would allow exploring L5 soma and their apical tufts simultaneously, and may give direct insight into the role of dendritic spikes and computation in neuronal output (London and Häusser, 2005; Shai et al., 2015).”
Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture (2011, background): “Changes in the dendrite branch weight are analogous to the amplifications of a signal along the dendrite branch (see London and Häusser, 2005), whereas changes in the axon branch (or axo-synaptic) weight are analogous to changes at the presynaptic level and postsynaptic level (at synapse).”
Pattern separation in the hippocampus through the eyes of computational modeling (2017, background): “While dendrites have been suggested to serve as powerful computational units of pyramidal neurons across various brain areas (London & Häusser, 2005; Papoutsi, Kastellakis, Psarrou, Anastasakis, & Poirazi, 2014; Poirazi, Brannon, & Mel, 2003; Sidiropoulou, Pissadaki, & Poirazi, 2006; Silver, 2010;…”
Dynamic causal modelling: A critical review of the biophysical and statistical foundations (2011, neutral): “Dynamic Causal Modelling: criticisms.”
Engineering a Less Artificial Intelligence. (2019, background): “Real neurons are vastly more complex machines, with nonlinear interactions within and between different branches of their dendrites (London and H € ausser, 2005).”
Episodic CAMN: Contextual Attention-Based Memory Networks with Iterative Feedback for Scene Labeling (2017, background): “Biologically, it is inspired by discoveries in neuroscience that neuron adaptation actually happens across multiple time-scales through the feedback connections between the dendrites and the axon [39, 42]; feedback of ionic currents are flowing back from the dendrites to the axon to allow refining i”
Could a Neuroscientist Understand a Microprocessor? (2016, positive): “For example, there are known effects based on the 416 extrasynaptic diffusion of neurotransmitters [61] and it is believed that active 417 conductances in dendrites may be crucial to computation [62].”
In search of intelligence: evolving a developmental neuron capable of learning (2014, background): “Changes in the dendrite branch weight are analogous to the ampliﬁcations of a signal along the dendrite branch (see London & Hüsser, 2005), whereas changes in the axon branch (or axo-synaptic) weight are analogous to changes at the pre-synaptic level and postsynaptic level (at synapse).”
Using Grid Cells for Navigation (2015, background): “In the case of the rate-coded vector cell model, those synaptic connections must also be multiplicative, which lacks biological plausibility (but see Mel, 1993), although the same functionality could be achieved by the integration of inputs on distinct dendritic branches (London and Ha¨usser, 2005).”
Physics for neuromorphic computing (2020, background): “Frequently ignored brain components play an important role and might be important to emulate: dendrites seem to be able to deploy extremely complex computations 40,41 , while astrocytes (Figure 3a) seem to play a key role in neuron regulation 42 .”