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The following preprint is available. Requests can be sent to
HORN at TAUNIVM.BITNET :
 
 
Segmentation, Binding and Illusory Conjunctions
 
           D. HORN, D. SAGI and M. USHER
 
                  Abstract
 
We investigate binding within the framework of a model of excitatory
and inhibitory cell assemblies which form an oscillating neural
network. Our model is composed of two such networks which are
connected through their inhibitory neurons. The excitatory cell
assemblies represent memory patterns. The latter have different
meanings in the two networks, representing two different attributes
of an object, such as shape and color.
The networks segment an input which contains mixtures of
such pairs into staggered oscillations of the relevant activities.
Moreover, the phases of the oscillating activities representing the
two attributes in each pair lock with each other to demonstrate binding.
The system works very well for two inputs, but displays faulty
correlations when the number of objects is larger than two.
In other words, the network conjoins attributes of different objects,
thus showing the phenomenon of "illusory conjunctions", like in
human vision.