Role of NMDAR plasticity in a computational model of synaptic memory

A largely unexplored question in neuronal plasticity is whether synapses are capable of encoding and learning the timing of synaptic inputs. We address this question in a computational model of synaptic input time difference learning (SITDL), where N-methyl-d-aspartate receptor (NMDAR) isoform expression in silent synapses is affected by time differences between glutamate and voltage signals. We suggest that differences between NMDARs' glutamate and voltage gate conductances induce modifications of the synapse's NMDAR isoform population, consequently changing the timing of synaptic response. NMDAR expression at individual synapses can encode the precise time difference between signals. Thus, SITDL enables the learning and reconstruction of signals across multiple synapses of a single neuron. In addition to plausibly predicting the roles of NMDARs in synaptic plasticity, SITDL can be usefully applied in artificial neural network models.

Automated summary

This article discusses whether synapses are capable of encoding and learning the timing of synaptic inputs, proposing a computational model of synaptic input time difference learning. The study shows that NMDAR expression at individual synapses can encode the precise time difference between signals.