  Neural network models comprising elements which have exclusively excitatory
or inhibitory synapses are capable of a wide range of dynamic behavior,
including chaos. In this paper, a simple excitatory-inhibitory neural pair,
which forms the building block of larger networks, is subjected to external
stimulation. The response shows transition between various types of dynamics,
depending upon the magnitude of the stimulus. Coupling such pairs over a local
neighborhood in a two-dimensional plane, the resultant network can achieve a
satisfactory segmentation of an image into ``object'' and ``background''.
Results for synthetic and and ``real-life'' images are given.
