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Publication Detail
A synaptic learning rule for exploiting nonlinear dendritic computation
  • Publication Type:
    Journal article
  • Authors:
    Bicknell BA, Häusser M
  • Publication date:
  • Journal:
  • Status:
  • Country:
    United States
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  • PII:
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  • Keywords:
    NMDA receptors, biophysical model, cable theory, dendritic computation, feature-binding problem, learning rule, morphology, pyramidal neuron, synaptic plasticity
  • Notes:
    © 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/).
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.
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