309 research outputs found
Modeling Maintenance of Long-Term Potentiation in Clustered Synapses, Long-Term Memory Without Bistability
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.Comment: 17 pages, 5 figure
Interlinked dual-time feedback loops can enhance robustness to stochasticity and persistence of memory.
Multiple interlinked positive feedback loops shape the stimulus responses of various biochemical systems, such as the cell cycle or intracellular Ca2+ release. Recent studies with simplified models have identified two advantages of coupling fast and slow feedback loops. This dual-time structure enables a fast response while enhancing resistances of responses and bistability to stimulus noise. We now find that (1) the dual-time structure similarly confers resistance to internal noise due to molecule number fluctuations, and (2) model variants with altered coupling, which better represent some specific biochemical systems, share all the above advantages. We also develop a similar bistable model with coupling of a fast autoactivation loop to a slow loop. This model\u27s topology was suggested by positive feedback proposed to play a role in long-term synaptic potentiation (LTP). The advantages of fast response and noise resistance are also present in this autoactivation model. Empirically, LTP develops resistance to reversal over approximately 1h . The model suggests this resistance may result from increased amounts of synaptic kinases involved in positive feedback
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