Asymmetric voltage attenuation in dendrites can enable hierarchical heterosynaptic plasticity.

Long-term synaptic plasticity is mediated via cytosolic calcium concentrations ([Ca 2+ ]). Using a synaptic model which implements calcium-based long-term plasticity via two sources of Ca 2+ , NMDA receptors and voltage-gated calcium channels (VGCCs), we show in dendritic cable simulations that the interplay between these two calcium sources can result in a diverse array of heterosynaptic effects. When spatially clustered synaptic input produces a local NMDA spike, the resulting dendritic depolarization can activate VGCCs at non-activated spines, resulting in heterosynaptic plasticity. NMDA spike activation at a given dendritic location will tend to depolarize dendritic regions that are located distally to the input site more than dendritic sites that are proximal to it. This asymmetry can produce a hierarchical effect in branching dendrites, where an NMDA spike at a proximal branch can induce heterosynaptic plasticity primarily at branches that are distal to it. We also explored how simultaneously activated synaptic clusters located at different dendritic locations synergistically affect the plasticity at the active synapses, as well as the heterosynaptic plasticity of an inactive synapse "sandwiched" between them. We conclude that the inherent electrical asymmetry of dendritic trees enables sophisticated schemes for spatially targeted supervision of heterosynaptic plasticity. Significance Statement Our simulations suggest a novel framework for understanding synaptic plasticity. As opposed to plasticity being controlled only locally at the target synapse (as with frequency-dependent protocols) or globally via a backpropagating action potential (as with spike timing-dependent plasticity, STDP), our results indicate that plasticity can be controlled in a sophisticated, hierarchical and branch-dependent manner. Our work makes experimentally verifiable predictions for experimentalists studying plasticity and also provides a basis for further theoretical research about dendritic computation and learning.