Dynamical response of an excitatory-inhibitory neural network to external stimulation: An application to image segmentation

Neural network models comprising elements that have exclusively excitatory or inhibitory synapses are capable of a wide range of dynamical 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. The corresponding network model, obtained by coupling such pairs over a local neighborhood in a two-dimensional plane, can achieve a satisfactory segmentation of an image into “object” and “background.” Results for synthetic and “real-life” images are given.