State Based Model of Long-Term Potentiation and Synaptic Tagging and Capture

Recent data indicate that plasticity protocols have not only synapse-specific but also more widespread effects. In particular, in synaptic tagging and capture (STC), tagged synapses can capture plasticity-related proteins, synthesized in response to strong stimulation of other synapses. This leads to long-lasting modification of only weakly stimulated synapses. Here we present a biophysical model of synaptic plasticity in the hippocampus that incorporates several key results from experiments on STC. The model specifies a set of physical states in which a synapse can exist, together with transition rates that are affected by high- and low-frequency stimulation protocols. In contrast to most standard plasticity models, the model exhibits both early- and late-phase LTP/D, de-potentiation, and STC. As such, it provides a useful starting point for further theoretical work on the role of STC in learning and memory. Author Summary: It is thought that the main biological mechanism of memory corresponds to long-lasting changes in the strengths, or weights, of synapses between neurons. The phenomenon of long-term synaptic weight change has been particularly well documented in the hippocampus, a crucial brain region for the induction of episodic memory. One important result that has emerged is that the duration of synaptic weight change depends on the stimulus used to induce it. In particular, a certain weak stimulus induces a change that lasts for around three hours, whilst stronger stimuli induce changes that last longer, in some cases as long as several months. Interestingly, if separate weak and strong stimuli are given in reasonably quick succession to different synapses of the same neuron, both synapses exhibit long-lasting change. Here we construct a model of synapses in the hippocampus that reproduces various data associated with this phenomenon. The model specifies a set of abstract physical states in which a synapse can exist as well as probabilities for making transitions between these states. This paper provides a basis for further studies into the function of the described phenomena.