A model synapse that incorporates the properties of short- and long-term synaptic plasticity (original) (raw)
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Converging evidence for a simplified biophysical model of synaptic plasticity
Biological Cybernetics, 2002
Different mechanisms that could form the molecular basis for bi-directional synaptic plasticity have been identified experimentally and corresponding biophysical models can be constructed. However, such models are complex and therefore it is hard to deduce their consequences to compare them to existing abstract models of synaptic plasticity. In this paper we examine two such models: a phenomenological one inspired by the phenomena of AMPA receptor insertion, and a more complex biophysical model based on the phenomena of AMPA receptor phosphorylation. We show that under certain approximations both these models can be mapped on to an equivalent, calcium-dependent, differential equation. Intracellular calcium concentration varies locally in each postsynaptic compartment, thus the plasticity rule we extract is a single-synapse rule. We convert this single synapse plasticity equation to a multi-synapse rule by incorporating a model of the NMDA receptor. Finally we suggest a mathematical embodiment of metaplasticity, which is consistent with observations on NMDA receptor properties and dependence on cellular activity. These results, in combination with some of our previous results, produce converging evidence for the calcium control hypothesis including a dependence of synaptic plasticity on the level of intercellular calcium as well as on the temporal pattern of calcium transients.
Synaptic plasticity with discrete state synapses
Physical Review E, 2005
Experimental observations on synaptic plasticity at individual glutamatergic synapses from the CA3 Shaffer collateral pathway onto CA1 pyramidal cells in the hippocampus suggest that the transitions in synaptic strength occur among discrete levels at individual synapses ( [21] and S. S.-H. Wang, unpublished data used with the authors' permission). This happens for both long term potentiation (LTP) and long term depression (LTD) induction protocols. O'Connor, Wittenberg, and Wang have argued that three states would account for their observations on individual synapses in the CA3-CA1 pathway. We develop a quantitative model of this three state system with transitions among the states determined by a competition between kinases and phosphatases shown by O'Connor et al. to be determinant of LTP and LTD, respectively. Specific predictions for various plasticity protocols are given by coupling this description of discrete synaptic AMPA conductance changes to a model of postsynaptic membrane potential and associated intracellular calcium fluxes to yield the transition rates among the states. We then present various LTP and LTD induction protocols to the model system and report the resulting whole cell changes in AMPA conductance. We also examine the effect of our discrete state synaptic plasticity model on the synchronization of realistic oscillating neurons. We show that one-to-one synchronization is enhanced by the plasticity we discuss here and the presynaptic and postsynaptic oscillations are in phase. Synaptic strength saturates naturally in this model and does not require artificial upper or lower cutoffs, in contrast to earlier models of plasticity.
Expressive Models for Synaptic Plasticity
2007
We explore some presynaptic mechanisms of the calyx of Held synapse through a stochastic model. The model, drawn from a kinetic approach developed in literature, exploits process calculi as formal grounds, enjoys nice compositional properties, has a direct computational implementation that supports simulation trials, and, to our knowledge, represents the first process calculi based model of a presynaptic terminal. Simulation results have shown coherence with experimental data and robustness against sensitivity analysis. The core model has been extended in order to address some issues related to open problems: we discuss hypotheses on short-term synaptic enhancement (facilitation) and depression, i.e. plasticity mechanism that are related to memory and learning. The two aims of our work, i.e. addressing neural mechanisms and validating and possibly improving, process calculi based modeling techniques are discussed throughout the paper, together with the results of experiments.
Biophysical model of long-term potentiation and synaptic tagging and capture
2009
Recent data indicate that plasticity protocols have not only synapse-specific, but also more widespread effects. In particular, in synaptic tagging and capture (STC), tagging describes a synapse-specific process, while capture describes how stimulation of one synapse can affect another, recently tagged, synapse. Here we present a biophysical model of synaptic plasticity in the hippocampus which incorporates several key results from experiments on STC. The model specifies a set of physical states in which a synapse can exist, together with transition rates that are affected by high-and low-frequency stimulation protocols. In contrast to most standard plasticity models, the model exhibits both early-and late-phase LTP/D, depotentiation and STC. As such it provides a useful starting point for further theoretical work on the rôle of STC in learning and memory. In addition, the model makes testable predictions about the variability of the field EPSP following a potentiation or depression protocol.
Neuropharmacology, 1998
Recent experimental evidence indicates that in the neocortex, the manner in which each synapse releases neurotransmitter in response to trains of presynaptic action potentials is potentially unique. These unique transmission characteristics arise because of a large heterogeneity in various synaptic properties that determine frequency dependence of transmission such as those governing the rates of synaptic depression and facilitation. A theoretical analysis was therefore undertaken to explore the phenomenologies of changes in the values of these synaptic parameters. The results illustrate how the change in any one of several synaptic parameters produces a distinctive effect on synaptic transmission and how these distinctive effects can point to the most likely biophysical mechanisms. These results could therefore be useful in studies of synaptic plasticity in order to obtain a full characterization of the phenomenologies of synaptic modifications and to isolate potential biophysical mechanisms. Based on this theoretical analysis and experimental data, it is proposed that there exists multiple mechanisms, phenomena and algorithms for synaptic plasticity at single synapses. Finally, it is shown that the impact of changing the values of synaptic parameters depends on the values of the other parameters. This may indicate that the various mechanisms, phenomena and algorithms are interlinked in a 'synaptic plasticity code'.
2006
It has been considered that an amount of calcium elevation in a synaptic spine determines whether the synapse is potentiated or depressed. However, it has been pointed out that simple application of the principle can not reproduce properties of spike-timing-dependent plasticity (STDP). To solve the problem, we present a possible mechanism using dynamically sliding threshold as the linear summation of calcium elevations induced by single pre-synaptic and post-synaptic spikes. We demonstrate that the model can reproduce the timing dependence of biological STDP. In addition, we find that the model can reproduce the dependence of biological STDP on the initial synaptic strength, which is found to be asymmetric for synaptic potentiation and depression, whereas no explicit initial-strength dependence nor asymmetric mechanism are incorporated into the model.
Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation
Neural Computation, 2000
We present a model for spike-driven dynamics of a plastic synapse, suited for aVLSI implementation. The synaptic device behaves as a capacitor on short time scales, and preserves the memory of two stable states e cacies on long time scales. The transitions LTP LTD are stochastic because both the number and the distribution of neural spikes in any nite stimulation interval uctuate, even at xed pre-and post-synaptic spike rates. The dynamics of the single synapse is studied analytically by extending the solution to a classic problem in queuing theory Tak acs process. The model of the synapse is implemented in aVLSI, and consists of only 18 transistors. It is also directly simulated. The simulations indicate that LTP LTD probabilities vs rates are robust to uctuations of the electronic parameters in a wide range of rates. The solutions for these probabilities, are in very good agreement both with the simulations and with measurements. Moreover, the probabilities are readily manipulable by variation of the chip's parameters, even in ranges where they are very small. The tests of the electronic device cover the range from spontaneous activity 3 4 Hz, to stimulus driven rates 50 Hz. Low transition probabilities can be maintained in all ranges, even though the intrinsic time constants of the device are short 100 ms.
Synaptic Plasticity: A Unifying Model to Address Some Persisting Questions
International …, 2011
Since it was first observed, synaptic plasticity has been considered as the experimental paradigm most likely to provide us with an understanding of how information is stored in the vertebrate brain. Various types have been demonstrated over these past 45 years, most notably long-term potentiation and long-term depression, and their established characteristics as well as their induction and consolidation requirements are highly indicative of this plasticity being the substrate for skills acquisition and mnemonic engraving. The molecular, biochemical, and structural models that have been proposed in the past, although most accommodate some aspect of synaptic plasticity observations, admittedly cannot offer a universally functional connection between all the phenomena that surround and result in the different modifications of synaptic efficacy. As a result, there are a number of persisting questions. In an attempt toward synthesis, we reviewed the most important studies in the field and believe that we can now propose a unifying Model for synaptic plasticity that can accommodate the experimental evidence and reconcile most of the contradictions. Moreover, from this model emerge potential answers to several unyielding questions, namely, accounting for the induction and expression of long-term depression, identifying the plasticity switch, offering a possible explanation for the sliding modification threshold, and proposing a new mechanism for synaptic tagging.
Synaptic Homeostasis and Input Selectivity Follow From a Calcium-Dependent Plasticity Model
Proceedings of The National Academy of Sciences, 2004
Modifications in the strengths of synapses are thought to underlie memory, learning, and development of cortical circuits. Many cellular mechanisms of synaptic plasticity have been investigated in which differential elevations of postsynaptic calcium concentrations play a key role in determining the direction and magnitude of synaptic changes. We have previously described a model of plasticity that uses calcium currents mediated by N-methyl-Daspartate receptors as the associative signal for Hebbian learning. However, this model is not completely stable. Here, we propose a mechanism of stabilization through homeostatic regulation of intracellular calcium levels. With this model, synapses are stable and exhibit properties such as those observed in metaplasticity and synaptic scaling. In addition, the model displays synaptic competition, allowing structures to emerge in the synaptic space that reflect the statistical properties of the inputs. Therefore, the combination of a fast calcium-dependent learning and a slow stabilization mechanism can account for both the formation of selective receptive fields and the maintenance of neural circuits in a state of equilibrium.