"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. ..."
Model Type: Neuron or other electrically excitable cell
Model Concept(s): Synaptic Plasticity; Dendritic Action Potentials; Detailed Neuronal Models; Action Potentials; Learning; Active Dendrites; STDP
Simulation Environment: NEURON
Implementer(s): Wilmes, Katharina A. [katharina.wilmes at googlemail.com]
References:
Wilmes KA, Schleimer JH, Schreiber S. (2017). Spike-timing dependent inhibitory plasticity to learn a selective gating of backpropagating action potentials. The European journal of neuroscience. 45 [PubMed]