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
computationally ... more >>>

For ordinary circuits with a fixed upper bound on the maximal fanin
of gates it has been shown that logarithmic redundancy is necessary and
sufficient to overcome random hardware faults.
Here, we consider the same question for unbounded fanin circuits that
in the noiseless case can compute Boolean ... more >>>

A neural network is said to be nonoverlapping if there is at most one
edge outgoing from each node. We investigate the number of examples
that a learning algorithm needs when using nonoverlapping neural
networks as hypotheses. We derive bounds for this sample complexity
in terms of the Vapnik-Chervonenkis dimension. ... more >>>