How much can we trust neural simulation strategies Computational neuroscience provides a growing number of tools which, together with the steady improvement of computational hardware, allow to simulate neural systems with increasing complexity ranging from detailed single cells to large scale neural networks. However, despite these advances, the results and, hence, qualitative interpretation of numerical simulations are still tightly bound to the simulation strategy used, and may vary across available simulation tools or for different settings within the same simulator. Specifically for networks of integrateandfire (IF) neurons, crucial differences in the occurrence of synchronous activity patterns were observed, depending on the temporal resolution of the neural simulator or the integration method used. Recently, a new eventdriven simulation approach was proposed, which complements traditional clockdriven strategies. In contrast to the latter, where spiking and synaptic release events are bound to a temporal grid of finite resolution, eventbased methods keep the precise timing of events. Focusing on most commonly used clockdriven and eventdriven approaches, in this contribution we evaluate to which extend the temporal precision of spiking events impacts on neuronal dynamics of single as well as small networks of IF neurons with plastic synapses. We found that the used strategy can severely alter simulated neural dynamics and, therefore, turn out to be crucial for the interpretation of the result of numerical imulations. Drastic differences were observed in models with spike timing dependent plasticity, arguing that the speed of neuronal simulations should not be the sole criteria for evaluation of the efficiency of simulation tools, but must complement an evaluation of their exactness, possibly in disfavour of their speed.

A calciuminfluxdependent plasticity model exhibiting multiple STDP curves Hebbian plasticity means that if the firing of two neurons is correlated, then their connection is strengthened. Conversely, uncorrelated firing causes a decrease in synaptic strength. Spiketimingdependent plasticity (STDP) represents one instantiation of Hebbian plasticity. Under STDP, synaptic changes depend on the relative timing of the pre and postsynaptic firing. By inducing pre and postsynaptic firing at different relative times the STDP curves of many neurons have been determined, and it has been found that there are different curves for different neuron types or synaptic sites. Biophysically, strengthening (longterm potentiation, LTP) or weakening (longterm depression, LTD) of glutamatergic synapses depends on the postsynaptic influx of calcium (Ca2): weak influx leads to LTD, while strong, transient influx causes LTP. The voltagedependent NMDA receptors are the main source of Ca2 influx, but they will only open if a postsynaptic depolarisation coincides with presynaptic neurotransmitter release. Here we present a computational mechanism for Ca2dependent plasticity in which the interplay between the presynaptic neurotransmitter release and the postsynaptic membrane potential leads to distinct Ca2 timecourses, which in turn lead to the change in synaptic strength. It is shown that the model complies with classic STDP results, as well as with results obtained with triplets of spikes. Furthermore, the model is capable of displaying different shapes of STDP curves, as observed in different experimental studies.