Metaplasticity of synaptic tagging and capture and its implications for maintaining long-term memory in normal and diseased neural networks

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Synaptic tagging and capture (STC) is one of the prominent models of associative memory formation at the cellular level. In line with this STC hypothesis, weak memory formation creates time-dependent synaptic tags/marks that might capture memory related proteins available due to a parallel strong memory input in a nearby synaptic network. This process results in the consolidation of the respective weak memory. One of us was involved in the identification of some of the key mechanisms and molecules in STC (Sajikumar et al., 2005b; Sajikumar et al., 2007). However, it is still unclear how memory consolidated through STC erases with time. We propose a new mechanism for the erasure of memory in neural networks: synaptic competition / distribution phase of memory. According to our hypothesis, a neuronal population will compete for memory proteins if the availability of these proteins is scarce, and the competing partners exhibit nearly similar plasticity thresholds. In consequence, consolidated memories are erased over the time. On the other hand, our model predicts that a synaptic population, which is able to survive synaptic competition, will have a better chance to code long-term memory without disruption. Understanding the cellular mechanisms of synaptic competition is important for finding appropriate therapeutic agents for preventing memory loss. In our studies, we will use ryanodine receptor (RYR) or metabotropic receptor mediated metaplasticity and analyze the role of metaplasticity induced protein PKMzeta for preventing synaptic competition. Understanding the phase and properties of competition of memory will provide new insight into the onset of age related dementia and the puzzling question of why some people have sharp memory irrespective of age and why others have not leading to Alzheimers disease (AD). In addition, we will explore the possibility of using metaplasticity as a therapeutic strategy for improving memory by targeting the neural network level in Alzheimers disease.