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A synaptic learning rule for exploiting nonlinear dendritic computation

Bicknell, BA; Häusser, M; (2021) A synaptic learning rule for exploiting nonlinear dendritic computation. Neuron 10.1016/j.neuron.2021.09.044. (In press). Green open access

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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.

Type: Article
Title: A synaptic learning rule for exploiting nonlinear dendritic computation
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neuron.2021.09.044
Publisher version: https://doi.org/10.1016/j.neuron.2021.09.044
Language: English
Additional information: © 2021 The Author(s). Published by Elsevier Inc. 1 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: NMDA receptors, biophysical model, cable theory, dendritic computation, feature-binding problem, learning rule, morphology, pyramidal neuron, synaptic plasticity
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Wolfson Inst for Biomedical Research
URI: https://discovery.ucl.ac.uk/id/eprint/10137669
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