  Providing the neurobiological basis of information processing in higher
animals, spiking neural networks must be able to learn a variety of complicated
computations, including the generation of appropriate, possibly delayed
reactions to inputs and the self-sustained generation of complex activity
patterns, e.g.~for locomotion. Many such computations require previous building
of intrinsic world models. Here we show how spiking neural networks may solve
these different tasks. Firstly, we derive constraints under which classes of
spiking neural networks lend themselves to substrates of powerful general
purpose computing. The networks contain dendritic or synaptic nonlinearities
and have a constrained connectivity. We then combine such networks with
learning rules for outputs or recurrent connections. We show that this allows
to learn even difficult benchmark tasks such as the self-sustained generation
of desired low-dimensional chaotic dynamics or memory-dependent computations.
Furthermore, we show how spiking networks can build models of external world
systems and use the acquired knowledge to control them.
