This posting was edited to include only the reports which are likely to be of
interest to readers of comp-neuro.
The European Community ESPRIT Working Group in Neural and Computational
Learning Theory (NeuroCOLT) has produced a set of new Technical Reports
available from the remote ftp site described below. They cover topics in
real valued complexity theory, computational learning theory, and analysis
of the computational power of continuous neural networks. Abstracts are
included for the titles.
*** Please note that the location of the files was changed at the beginning of
** the year, so that any copies you have of the previous instructions should be
* discarded. The new location and instructions are given at the end of the list.
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NeuroCOLT Technical Report NC-TR-96-030:
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Exponentially many local minima for single neurons
by Peter Auer, University of California, Santa Cruz, USA,
Mark Herbster, University of California, Santa Cruz, USA,
Manfred K. Warmuth, University of California, Santa Cruz, USA
Abstract:
We show that for a single neuron with the logistic function as the
transfer function the number of local minima of the error function
based on the square loss can grow exponentially in the dimension.
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NeuroCOLT Technical Report NC-TR-96-031:
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An Efficient Implementation of Sigmoidal Neural Nets in Temporal
Coding with Noisy Spiking Neurons
by Wolfgang Maass, Institute for Theoretical Computer Science,
Technische Universitaet Graz, Austria
Abstract:
We show that networks of rather realistic models for biological neurons
can in principle simulate arbitrary feedforward sigmoidal neural nets
in a way which has previously not been considered. This new approach
is based on temporal coding by single spikes (respectively by the
timing of synchronous firing in pools of neurons), rather than on the
traditional interpretation of analog variables in terms of firing
rates. The resulting new simulation is substantially faster and
apparently more consistent with experimental results about fast
information processing in cortical neural systems.
As a consequence we can show that networks of noisy spiking neurons are
"universal approximators" in the sense that they can approximate with
regard to temporal coding {\it any} given continuous function of
several variables.
Our new proposal for the possible organization of computations in
biological neural systems has some interesting consequences for the
type of learning rules that would be needed to explain the
self-organization of such neural circuits.
Finally, our fast and noise-robust implementation of sigmoidal neural
nets via temporal coding points to possible new ways of implementing
sigmoidal neural nets with pulse stream VLSI.
***************** ACCESS INSTRUCTIONS ******************
The Report NC-TR-96-001 can be accessed and printed as follows
% ftp ftp.dcs.rhbnc.ac.uk (134.219.96.1)
Name: anonymous
password: your full email address
ftp> cd pub/neurocolt/tech_reports
ftp> binary
ftp> get nc-tr-96-001.ps.Z
ftp> bye
% zcat nc-tr-96-001.ps.Z | lpr -l
Similarly for the other technical reports.
Uncompressed versions of the postscript files have also been
left for anyone not having an uncompress facility.
In some cases there are two files available, for example,
nc-tr-96-002-title.ps.Z
nc-tr-96-002-body.ps.Z
The first contains the title page while the second contains the body
of the report. The single command,
ftp> mget nc-tr-96-002*
will prompt you for the files you require.
A full list of the currently available Technical Reports in the
Series is held in a file `abstracts' in the same directory.
The files may also be accessed via WWW starting from the NeuroCOLT
homepage (note that this is undergoing some corrections and may be
temporarily inaccessible):
http://www.dcs.rhbnc.ac.uk/neural/neurocolt.html
Best wishes
John Shawe-Taylor