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Electronic Colloquium on Computational Complexity

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Reports tagged with sigmoidal gates:
TR96-031 | 30th April 1996

Networks of Spiking Neurons: The Third Generation of Neural Network Models

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 >>>

TR99-005 | 21st December 1998
Michael Schmitt

On the Sample Complexity for Nonoverlapping Neural Networks

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 >>>

TR00-030 | 31st May 2000

A Simple Model for Neural Computation with Firing Rates and Firing Correlations

A simple extension of standard neural network models is introduced that
provides a model for neural computations that involve both firing rates and
firing correlations. Such extension appears to be useful since it has been
shown that firing correlations play a significant computational role in
many biological neural systems. Standard ... more >>>

ISSN 1433-8092 | Imprint