A unified computational model for cortical post-synaptic plasticity

Abstract

AbstractCortical synapses possess a machinery of signalling pathways that leads to various modes of post-synaptic plasticity. Such pathways have been examined to a great detail separately in many types of experimental studies. However, a unified picture on how multiple biochemical pathways collectively shape the observed synaptic plasticity in the neocortex is missing. Here, we built a biochemically detailed model of post-synaptic plasticity that includes the major signalling cascades, namely, CaMKII, PKA, and PKC pathways which, upon activation by Ca2+, lead to synaptic potentiation or depression. We adjusted model components from existing models of intracellular signalling into a single-compartment simulation framework. Furthermore, we propose a statistical model for the prevalence of different types of membrane-bound AMPA-receptor tetramers consisting of GluR1 and GluR2 subunits in proportions suggested by the biochemical signalling model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance. We show that our model can reproduce neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex. Moreover, we demonstrate that our model can be fit to data from many cortical areas and that the resulting model parameters reflect the involvement of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity.Significance statementNeocortical synaptic plasticity has been studied experimentally in a number of cortical areas, showing how interactions between neuromodulators and post-synaptic proteins shape the outcome of the plasticity. On the other hand, non-detailed computational models of long-term plasticity, such as Hebbian rules of synaptic potentiation and depression, have been widely used in modelling of neocortical circuits. In this work, we bridge the gap between these two branches of neuroscience by building a detailed model of post-synaptic plasticity that can reproduce observations on cortical plasticity and provide biochemical meaning to the simple rules of plasticity. Our model can be used for predicting the effects of chemical or genetic manipulations of various intracellular signalling proteins on induction of plasticity in health and disease.