Generic origins of irregular spiking in neocortical networks.

We identify generic sources of complex and irregular spiking in biological neural networks. For the network description, we operate on a mathematically exact mesoscopic approach. Starting from experimental data, we determine exact properties of noise-driven, binary neuron interaction and extrapolate from there to properties of more complex types of interaction. Our approach fills a gap between approaches that start from detailed biophysically motivated simulations but fail to make mathematically exact global predictions, and approaches that are able to make exact statements but only on levels of description that are remote from biology. As a consequence of the approach, a novel coding scheme emerges, shedding new light on local information processing in biological neural networks.