Recently one has started to investigate the
computational power of spiking neurons (also called ``integrate and
fire neurons''). These are neuron models that are substantially
more realistic from the biological point of view than the
ones which are traditionally employed in artificial neural nets.
It has turned out that the ... more >>>

The computational power of formal models for
networks of spiking neurons is compared with
that of other neural network models based on
McCulloch Pitts neurons (i.e. threshold gates)
respectively sigmoidal gates. In particular it
is shown that networks of spiking neurons are
... more >>>

Spiking neurons are models for the computational units in
biological neural systems where information is considered to be encoded
mainly in the temporal pattern of their activity. In a network of
spiking neurons a new set of parameters becomes relevant which has no
counterpart in traditional ... more >>>

We explore the computational power of formal models for computation
with pulses. Such models are motivated by realistic models for
biological neurons, and by related new types of VLSI (``pulse stream
VLSI'').

In preceding work it was shown that the computational power of formal
models for computation with pulses is ... more >>>

We study networks of spiking neurons that use the timing of pulses
to encode information. Nonlinear interactions model the spatial
groupings of synapses on the dendrites and describe the computations
performed at local branches. We analyze the question of how many
examples these networks must ... more >>>