Cascade Models of Synaptically Stored Memories

Fusi, Stefano; Drew, Patrick J.; Abbott, L. F.

doi:10.1016/j.neuron.2005.02.001

Cited by 485 publications

(681 citation statements)

References 51 publications

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“…The exact shape of this forgetting curve is still under debate [exponential or power law; (Wixted and Ebbesen 1991;Anderson and Tweney 1997)]. However, forgetting may occur by several mechanisms, for instance, by noise and ongoing plasticity at the synaptic level (Fusi et al 2005). Another possibility is the permanent overwriting or interference with old memories, which in turn reduces the capacity requirements of the network [see, e.g., (Mézard et al 1986;Sikström 2002) for Hopfield models] but also deletes important memories.…”

Section: Why Time Scales?mentioning

confidence: 99%

Time scales of memory, learning, and plasticity

et al. 2012

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If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis.

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Section: Why Time Scales?mentioning

confidence: 99%

Time scales of memory, learning, and plasticity

et al. 2012

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“…On the other hand, in both Aplysia and hippocampus an intermediate-term stage of plasticity has been identified that usually involves protein but not RNA synthesis and structural alterations but not synaptic growth, and therefore might form a bridge between short-and long-term plasticity (Ghirardi et al 1995;Winder et al 1998;Sutton and Carew 2000;Sutton et al 2001;Kim et al 2003;Li et al 2005Li et al , 2009Villareal et al 2007). That idea in turn suggests that aspects of the different stages of plasticity may be induced in series, similar to the states in artificial "cascade" models of memory storage (Fusi et al 2005).…”

mentioning

confidence: 99%

Whereas short-term facilitation is presynaptic, intermediate-term facilitation involves both presynaptic and postsynaptic protein kinases and protein synthesis

Jin¹,

Kandel²,

Hawkins³

2011

Learn. Mem.

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Whereas short-term plasticity involves covalent modifications that are generally restricted to either presynaptic or postsynaptic structures, long-term plasticity involves the growth of new synapses, which by its nature involves both pre-and postsynaptic alterations. In addition, an intermediate-term stage of plasticity has been identified that might form a bridge between short-and long-term plasticity. Consistent with that idea, although short-term term behavioral sensitization in Aplysia involves presynaptic mechanisms, intermediate-term sensitization involves both pre-and postsynaptic mechanisms. However, it has not been known whether that is also true of facilitation in vitro, where a more detailed analysis of the mechanisms involved in the different stages and their interrelations is feasible. To address those questions, we have examined preand postsynaptic mechanisms of short-and intermediate-term facilitation at Aplysia sensory-motor neuron synapses in isolated cell culture. Whereas short-term facilitation by 1-min 5-HT involves presynaptic PKA and CamKII, intermediate-term facilitation by 10-min 5-HT involves presynaptic PKC and postsynaptic Ca 2+ and CamKII, as well as both pre-and postsynaptic protein synthesis. These results support the idea that the intermediate-term stage is the first to involve both pre-and postsynaptic molecular mechanisms, which could in turn serve as some of the initial steps in a cascade leading to synaptic growth during long-term plasticity.

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“…Experimental evidence for binary, ternary and even larger numbers of discrete states of synaptic strength exists (Petersen et al, 1998;Montgomery & Madison, 2002, 2004O'Connor et al, 2005a,b;Bartol et al, 2015) although the interpretation of such evidence can be difficult (Elliott, 2010a), and evidence also supports the possibility that changes in synaptic strength may be discrete, jump-like, all-or-none processes (Yasuda et al, 2003;Bagal et al, 2005;Sobczyk & Svoboda, 2007). Many such memory models and analyses based on discrete synapses in feedforward or recurrent network settings now exist, using a variety of different measures to gauge memory lifetimes (see, for example, Tsodyks, 1990;Amit & Fusi, 1994, Fusi et al, 2005, Leibold & Kempter, 2006, 2008Rubin & Fusi, 2007;Barrett & van Rossum, 2008;Huang & Amit, 2010, 2011, Lahiri & Ganguli, 2013. Early models are based on "simple" synapses that lack internal states and change strength stochastically with fixed probability in response to memory storage (Tsodyks, 1990).…”

Section: Introductionmentioning

confidence: 98%

“…We have termed such a synapse a "stochastic updater". "Complex" synapses attempt to overcome the problems characteristic of simple synapses by considering internal synaptic states that govern the expression of synaptic plasticity through metaplasticity (see, for example, Fusi et al, 2005;Rubin & Fusi, 2007;Leibold & Kempter, 2008;Lahiri & Ganguli, 2013).…”

Section: Introductionmentioning

confidence: 99%

Mean First Passage Memory Lifetimes by Reducing Complex Synapses to Simple Synapses

Elliott

2017

Neural Computation

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Memory models that store new memories by forgetting old ones have memory lifetimes that are rather short and grow only logarithmically in the number of synapses. Attempts to overcome these deficits include "complex" models of synaptic plasticity in which synapses possess internal states governing the expression of synaptic plasticity. Integrate-and-express, filter-based models of synaptic plasticity propose that synapses act as low-pass filters, integrating plasticity induction signals before expressing synaptic plasticity. Such mechanisms enhance memory lifetimes, leading to an initial rise in the memory signal that is in radical contrast to other related, but non-integrative memory models. Because of the complexity of models with internal synaptic states, however, their dynamics can be more difficult to extract compared to "simple" models that lack internal states. Here, we show that by focusing only on processes that lead to changes in synaptic strength, we can integrate out internal synaptic states and effectively reduce complex synapses to simple synapses.For binary-strength synapses, these simplified dynamics then allow us to work directly in the transitions in perceptron activation induced by memory storage rather than in the underlying transitions in synaptic configurations. This permits us to write down master and Fokker-Planck equations that may be simplified under certain, well-defined approximations. These methods allow us to see that memory based on synaptic filters can be viewed as an initial transient that leads to memory signal rise, followed by the emergence of OrnsteinUhlenbeck-like dynamics that return the system to equilibrium. We may use this approach to compute mean first passage time-defined memory lifetimes for complex models of memory storage.2

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Cascade Models of Synaptically Stored Memories

Cited by 485 publications

References 51 publications

Time scales of memory, learning, and plasticity

Time scales of memory, learning, and plasticity

Whereas short-term facilitation is presynaptic, intermediate-term facilitation involves both presynaptic and postsynaptic protein kinases and protein synthesis

Mean First Passage Memory Lifetimes by Reducing Complex Synapses to Simple Synapses

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