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Abstract

Artificial neuronal networks provide attractive models for cortical function, in particular, if “cognitive” properties emerge from their structure. Unfortunately, it turns out difficult to set up classical models which are comparable to the biological system on the level of single neurons. We look at artificial neuronal networks from a fresh perspective, which has the potential to extend their merits to a detailed and quantitative description of physiological phenomena in nerve nets of spiking neurons. In fact, the framework of stochastic point processes provides the tools for the construction of mathematically consistent models, which allow for a direct comparison with electrophysiological recordings on the level of individual nerve cells, in particular, if these are part of a large network. Moreover, the estimation of model parameters from experiments becomes feasible, so that a quantitative theoretical treatment as well as computer simulations of large networks under realistic conditions can be undertaken.