Spike‐timing dependent inhibitory plasticity to learn a selective gating of backpropagating action potentials

Inhibition is known to influence the forward‐directed flow of information within neurons. However, also regulation of backward‐directed signals, such as backpropagating action potentials (bAPs), can enrich the functional repertoire of local circuits. Inhibitory control of bAP spread, for example, can provide a switch for the plasticity of excitatory synapses. Although such a mechanism is possible, it requires a precise timing of inhibition to annihilate bAPs without impairment of forward‐directed excitatory information flow. Here, we propose a specific learning rule for inhibitory synapses to automatically generate the correct timing to gate bAPs in pyramidal cells when embedded in a local circuit of feedforward inhibition. Based on computational modeling of multi‐compartmental neurons with physiological properties, we demonstrate that a learning rule with anti‐Hebbian shape can establish the required temporal precision. In contrast to classical spike‐timing dependent plasticity of excitatory synapses, the proposed inhibitory learning mechanism does not necessarily require the definition of an upper bound of synaptic weights because of its tendency to self‐terminate once annihilation of bAPs has been reached. Our study provides a functional context in which one of the many time‐dependent learning rules that have been observed experimentally – specifically, a learning rule with anti‐Hebbian shape – is assigned a relevant role for inhibitory synapses. Moreover, the described mechanism is compatible with an upregulation of excitatory plasticity by disinhibition.