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Electronic Colloquium on Computational Complexity

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Reports tagged with spiking neurons:
TR96-025 | 22nd March 1996
Berthold Ruf

The Computational Power of Spiking Neurons Depends on the Shape of the Postsynaptic Potentials

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 >>>

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 >>>

TR97-049 | 22nd October 1997
Michael Schmitt

On the Complexity of Learning for Spiking Neurons with Temporal Coding

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 >>>

TR00-038 | 24th May 2000

On Computation with Pulses

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

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

TR04-033 | 23rd January 2004
Michael Schmitt

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions

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 >>>

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