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- Publisher Website: 10.1146/annurev.neuro.28.061604.135703
- Scopus: eid_2-s2.0-23244457444
- PMID: 16033324
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Article: Dendritic computation
Title | Dendritic computation |
---|---|
Authors | |
Keywords | Coding Dendrites Ion channels Spikes Synaptic integration |
Issue Date | 2005 |
Citation | Annual Review of Neuroscience, 2005, v. 28, p. 503-532 How to Cite? |
Abstract | One of the central questions in neuroscience is how particular tasks, or computations, are implemented by neural networks to generate behavior. The prevailing view has been that information processing in neural networks results primarily from the properties of synapses and the connectivity of neurons within the network, with the intrinsic excitability of single neurons playing a lesser role. As a consequence, the contribution of single neurons to computation in the brain has long been underestimated. Here we review recent work showing that neuronal dendrites exhibit a range of linear and nonlinear mechanisms that allow them to implement elementary computations. We discuss why these dendritic properties may be essential for the computations performed by the neuron and the network and provide theoretical and experimental examples to support this view. Copyright © 2005 by Annual Reviews. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/342970 |
ISSN | 2023 Impact Factor: 12.1 2023 SCImago Journal Rankings: 8.658 |
DC Field | Value | Language |
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dc.contributor.author | London, Michael | - |
dc.contributor.author | Häusser, Michael | - |
dc.date.accessioned | 2024-05-10T09:04:25Z | - |
dc.date.available | 2024-05-10T09:04:25Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | Annual Review of Neuroscience, 2005, v. 28, p. 503-532 | - |
dc.identifier.issn | 0147-006X | - |
dc.identifier.uri | http://hdl.handle.net/10722/342970 | - |
dc.description.abstract | One of the central questions in neuroscience is how particular tasks, or computations, are implemented by neural networks to generate behavior. The prevailing view has been that information processing in neural networks results primarily from the properties of synapses and the connectivity of neurons within the network, with the intrinsic excitability of single neurons playing a lesser role. As a consequence, the contribution of single neurons to computation in the brain has long been underestimated. Here we review recent work showing that neuronal dendrites exhibit a range of linear and nonlinear mechanisms that allow them to implement elementary computations. We discuss why these dendritic properties may be essential for the computations performed by the neuron and the network and provide theoretical and experimental examples to support this view. Copyright © 2005 by Annual Reviews. All rights reserved. | - |
dc.language | eng | - |
dc.relation.ispartof | Annual Review of Neuroscience | - |
dc.subject | Coding | - |
dc.subject | Dendrites | - |
dc.subject | Ion channels | - |
dc.subject | Spikes | - |
dc.subject | Synaptic integration | - |
dc.title | Dendritic computation | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1146/annurev.neuro.28.061604.135703 | - |
dc.identifier.pmid | 16033324 | - |
dc.identifier.scopus | eid_2-s2.0-23244457444 | - |
dc.identifier.volume | 28 | - |
dc.identifier.spage | 503 | - |
dc.identifier.epage | 532 | - |