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 powerful than these other neural network ... 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 functions in ... 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 >>>