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We investigate generic models for cortical microcircuits, i.e. recurrent circuits of integrate-and fire neurons with dynamic synapses. These complex dynamic systems subserve the amazing information processing capabilities of the cortex, but are at the present time very little understood. We analyze the transient dynamics of models for neural microcircuits from the point of view of one or two readout neurons that collapse 2 the high dimensional transient dynamics of a neural circuit into a 1- or 2--dimensional output stream. This stream may for example represent the information that is projected from such circuit to some particular other brain area or actuators. It is shown that simple local learning rules enable a readout neuron to extract from the high dimensional transient dynamics of a recurrent neural circuit quite different low-dimensional projections, that even may contain "virtual attractors " which are not apparent in the high dimensional dynamics of the circuit itself. Furthermore it is demonstrated that the information extraction capabilities of linear readout neurons are boosted by th