**W. Maass, T. Natschlaeger, and H. Markram**

A key challenge for neural modeling is to explain how a continuous stream of
multi-modal input from a rapidly changing environment can be processed by
stereotypical recurrent circuits of integrate-and-fire neurons in real-time.
We propose a new computational model that does not require a task-dependent
construction of neural circuits. Instead it is based on principles of high
dimensional dynamical systems in combination with statistical learning
theory, and can be implemented on generic evolved or found recurrent
circuitry.