Paper dynamic linking, paper active vision

[Bert Kappen]

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Dynamic linking in Stochastic Networks
Hilbert J. Kappen, Marcel J. Nijman
To be presented at the International Conference on Brain Processes,
Theories and Models to take place in Las Palmas de Gran
Canaria, Spain, Nov 12-17, 1995. 
FTP-host: ftp.mbfys.kun.nl
FTP-file: /snn/pub/reports/Kappen.Dyn.Link.ps.Z

Abstract:
It is well established that cortical neurons display synchronous
firing for some stimuli and not for others. The resulting synchronous
subpopulation of neurons is thought to form the basis of object
perception.  In this paper this 'binding'
problem is formulated for Boltzmann Machines. Feed-forward
connections implement feature detectors and lateral connections
implement memory traces or cell assemblies.
We show, that dynamic linking can be solved in the
Ising model where sensory input provides local evidence.
The lateral connections in the
hidden layer provide global correlations between features that belong
to the same stimulus and no correlations between features from different
stimuli.

Learning Active Vision
Hilbert J. Kappen, Marcel J. Nijman, Tonnie van Moorsel
To be presented at ICANN'95, October 1995, Paris
FTP-host: ftp.mbfys.kun.nl
FTP-file: snn/pub/reports/Kappen.Active.Vision.ps.Z

Abstract:
In this paper we introduce a new type of problem which we call
active decision. It consists of finding the optimal subsequent action,
based both on partial observation and on previously learned knowledge.
We propose a method for solution, based on Boltzmann Machine learning
of joint input-output probabilities
and on an entropy minimization criterion.
We show how the method provides a basic mechanism for active vision
tasks such as saccadic eye movements.