Modeling Maintenance of Long-Term Potentiation in Clustered Synapses, Long-Term Memory Without Bistability

TL;DR

This paper presents a model demonstrating how clustered synapses can maintain long-term memories with unimodal strength distributions, challenging bistability models and aligning with recent empirical data.

Contribution

The model introduces a novel mechanism where resource competition within synaptic clusters sustains strong synapses over years, supporting the clustered plasticity hypothesis.

Findings

Unimodal synaptic weight distribution can persist for over a year.

Resource competition within clusters stabilizes synaptic groups.

Model aligns with recent experimental observations.

Abstract

Memories are stored, at least partly, as patterns of strong synapses. Given molecular turnover, how can synapses maintain strong for the years that memories can persist? Some models postulate that biochemical bistability maintains strong synapses. However, bistability should give a bimodal distribution of synaptic strength or weight, whereas current data show unimodal distributions for weights and for a correlated variable, dendritic spine volume. Bistability of single synapses has also never been empirically demonstrated. Thus it is important for models to simulate both unimodal distributions and long-term memory persistence. Here a model is developed that connects ongoing, competing processes of synaptic growth and weakening to stochastic processes of receptor insertion and removal in dendritic spines. The model simulates long-term (in excess of 1 yr) persistence of groups of strong synapses. A unimodal weight distribution results. For stability of this distribution it proved essential to incorporate resource competition between synapses organized into small clusters. With competition, these clusters are stable for years. These simulations concur with recent data to support the clustered plasticity hypothesis, which suggests clusters, rather than single synaptic contacts, may be a fundamental unit for storage of long-term memory. The model makes empirical predictions, and may provide a framework to investigate mechanisms maintaining the balance between synaptic plasticity and stability of memory.