Understanding the structure of real-time neural computation in
highly recurrent neural microcircuits that consist of complex
heterogeneous components has remained a serious challenge for
computational modeling. We propose here a new conceptual framework
that strongly differs from all previous approaches based on
computational models inspired by computer science or artificial
neural networks. It is based on a rigorous mathematical model, the
liquid state machine, whose computational power is analyzed in this
article, both for the case of time -- varying analog input and for
the case where the input consists of spike trains. The theoretical
analysis implies that recurrent circuits are able to carry out
complex real-time computations on such inputs, even several such
computations in parallel, provided that they are able to separate
different inputs through different activation patterns at subsequent
time points. Furthermore, biologically realistic recurrent circuits
of spiking neurons, consisting of heterogeneous neurons and synapses
with different time constants, appear to be particularly good at
this separation task. Based on this new approach one can now for the
first time employ computer models for biologically realistic neural
microcircuits as central processing units for complex computational
tasks.