POSTER PRESENTATION Open Access Taming the model zoo: a unified view on correlations in recurrent networks

The meaning of correlated neural activity for the proces-sing and representation of information in cortical net-works is still not understood, but evidence for a pivotal role of correlations increases [1]. Recent theoretical work has shown [2-4] that balanced recurrent networks of bin-ary model neurons [3] and spiking integrate-and-fire mod-els [2-4] are able to produce weak correlations despite common input to pairs of cells. For binary model neurons, the theory of correlations in recurrent networks is well established [5]. Investigating learning in recurrent net-works with spike-timing dependent plasticity requires spiking neuron models. Theoretical work often employs linear stochastic point process models [6] for their analytic tractability [7]. The diversity of neuron models used in contemporary theoretical neuroscience brings up th