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- Publisher Website: 10.1016/j.neuron.2021.09.044
- Scopus: eid_2-s2.0-85120912816
- PMID: 34715026
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Article: A synaptic learning rule for exploiting nonlinear dendritic computation
Title | A synaptic learning rule for exploiting nonlinear dendritic computation |
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Authors | |
Keywords | biophysical model cable theory dendritic computation feature-binding problem learning rule morphology NMDA receptors pyramidal neuron synaptic plasticity |
Issue Date | 2021 |
Citation | Neuron, 2021, v. 109, n. 24, p. 4001-4017.e10 How to Cite? |
Abstract | Information processing in the brain depends on the integration of synaptic input distributed throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to be equivalent to integration by a multilayer network, potentially endowing single neurons with substantial computational power. However, whether neurons can learn to harness dendritic properties to realize this potential is unknown. Here, we develop a learning rule from dendritic cable theory and use it to investigate the processing capacity of a detailed pyramidal neuron model. We show that computations using spatial or temporal features of synaptic input patterns can be learned, and even synergistically combined, to solve a canonical nonlinear feature-binding problem. The voltage dependence of the learning rule drives coactive synapses to engage dendritic nonlinearities, whereas spike-timing dependence shapes the time course of subthreshold potentials. Dendritic input-output relationships can therefore be flexibly tuned through synaptic plasticity, allowing optimal implementation of nonlinear functions by single neurons. |
Persistent Identifier | http://hdl.handle.net/10722/343519 |
ISSN | 2023 Impact Factor: 14.7 2023 SCImago Journal Rankings: 7.728 |
DC Field | Value | Language |
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dc.contributor.author | Bicknell, Brendan A. | - |
dc.contributor.author | Häusser, Michael | - |
dc.date.accessioned | 2024-05-10T09:08:45Z | - |
dc.date.available | 2024-05-10T09:08:45Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Neuron, 2021, v. 109, n. 24, p. 4001-4017.e10 | - |
dc.identifier.issn | 0896-6273 | - |
dc.identifier.uri | http://hdl.handle.net/10722/343519 | - |
dc.description.abstract | Information processing in the brain depends on the integration of synaptic input distributed throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to be equivalent to integration by a multilayer network, potentially endowing single neurons with substantial computational power. However, whether neurons can learn to harness dendritic properties to realize this potential is unknown. Here, we develop a learning rule from dendritic cable theory and use it to investigate the processing capacity of a detailed pyramidal neuron model. We show that computations using spatial or temporal features of synaptic input patterns can be learned, and even synergistically combined, to solve a canonical nonlinear feature-binding problem. The voltage dependence of the learning rule drives coactive synapses to engage dendritic nonlinearities, whereas spike-timing dependence shapes the time course of subthreshold potentials. Dendritic input-output relationships can therefore be flexibly tuned through synaptic plasticity, allowing optimal implementation of nonlinear functions by single neurons. | - |
dc.language | eng | - |
dc.relation.ispartof | Neuron | - |
dc.subject | biophysical model | - |
dc.subject | cable theory | - |
dc.subject | dendritic computation | - |
dc.subject | feature-binding problem | - |
dc.subject | learning rule | - |
dc.subject | morphology | - |
dc.subject | NMDA receptors | - |
dc.subject | pyramidal neuron | - |
dc.subject | synaptic plasticity | - |
dc.title | A synaptic learning rule for exploiting nonlinear dendritic computation | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.neuron.2021.09.044 | - |
dc.identifier.pmid | 34715026 | - |
dc.identifier.scopus | eid_2-s2.0-85120912816 | - |
dc.identifier.volume | 109 | - |
dc.identifier.issue | 24 | - |
dc.identifier.spage | 4001 | - |
dc.identifier.epage | 4017.e10 | - |
dc.identifier.eissn | 1097-4199 | - |