title: Modeling maintenance of long-term potentiation in clustered synapses: long-term memory without bistability.
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. 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 (>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.
authors.affiliation: Laboratory of Origin, Department of Neurobiology and Anatomy, W. M. Keck Center for the Neurobiology of Learning and Memory, The University of Texas Medical School at Houston, Houston, TX 77030, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Smolen
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D049229:Dendritic Spines / Q000502:physiology*; D006801:Humans; D017774:Long-Term Potentiation; D057567:Memory, Long-Term / Q000502:physiology*; D008959:Models, Neurological; D013269:Stochastic Processes; D013569:Synapses / Q000502:physiology*