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.S ynaptic plasticity as a physiological basis for learning and memory storage has been extensively investigated. Induction of bidirectional synaptic plasticity has been shown to depend on calcium influx into the postsynaptic cell (1, 2). In a previous paper, we proposed a model of bidirectional activity-dependent synaptic plasticity that depends on the calcium currents mediated by N-methyl-D-aspartate receptors (NMDARs) (3). In this model, which we henceforth denote calcium-dependent plasticity (CaDP), the direction and magnitude of synaptic changes are determined by a function of the intracellular calcium concentration: basal levels of calcium generate no plasticity, moderate ones induce depression, and higher elevations lead to potentiation (4). At a synapse, the amount of neurotransmitter bound to NMDARs provides information on the local, presynaptic activities, whereas back-propagating action potentials signal the global, postsynaptic activities. This association between pre-and postsynaptic activities thus forms the basis for Hebbian learning. Analysis and simulations have shown that this model can explain the rate-, voltage-, and spike timing-dependent plasticity as consequences of, respectively, the temporal integration of calcium transients, the voltage-dependence of NMDAR conductances, and the coincidence-detection property of these receptors (3, 5). In addition, numerous experimental results support the idea that NMDARs play key roles in activity-dependent development and refinement of synapses because of their permeability to calcium ions (6-11).However, typical of associative forms of plasticity rules, CaDP is not completely stable. Excessive neural excitation generates high levels of depolarization, favoring calcium entry into the dendrites and thus promoting synaptic potentiation. Such potentiation further enhances the excitability of the...

Although spike-timing-dependent plasticity (STDP) is well characterized when pre- and postsynaptic spikes are paired with a given time lag, how this generalizes for more complex spike-trains is unclear. Recent experiments demonstrate that contributions to synaptic plasticity from different spike pairs within a spike train do not add linearly. In the visual cortex conditioning with spike triplets shows that the effect of the first spike pair dominates over the second. Using a previously proposed calcium-dependent plasticity model, we show that short-term synaptic dynamics and interaction between successive back-propagating action potentials (BPAP) may jointly account for the nonlinearities observed. Paired-pulse depression and attenuation of BPAPs are incorporated into the model through the use-dependent depletion of pre- and postsynaptic resources, respectively. Simulations suggest that these processes may play critical roles in determining how STDP operates in the context of natural spike-trains.

LC, Shouval LN. Effect of stochastic synaptic and dendritic dynamics on synaptic plasticity in visual cortex and hippocampus. J Neurophysiol 97: 375-386, 2007. First published October 11, 2006 doi:10.1152/jn.00895.2006. Various forms of synaptic plasticity, including spike timing-dependent plasticity, can be accounted for by calcium-dependent models of synaptic plasticity. However, recent results in which synaptic plasticity is induced by multi-spike protocols cannot simply be accounted for by linear superposition of plasticity due to spike pairs or by existing calcium-dependent models. In this paper, we show that multi-spike protocols can be accounted for if, in addition to the dynamics of back-propagating action potentials, stochastic synaptic dynamics are taken into account. We show that a stochastic implementation can account for the data better than a deterministic implementation and is also more robust. Our results demonstrate that differences between experimental results obtained in hippocampus and visual cortex can be accounted for by the different synaptic and dendritic dynamics in these two systems.