Abstract
Chaos generated by the internal dynamics of a large neural network
can be correlated over large spatial scales. Modulating the spatial
coherence of the chaotic fluctuations by the spatial pattern of the
external input provides a robust mechanism for feature segmentation
and binding; which cannot be accomplished by networks of oscillators
with local noise. This is demonstrated by an investigation of synchronized
chaos in a network model of bursting neurons responding to an inhomogeneous
stimulus.
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