  The reshaping and decorrelation of similar activity patterns by neuronal
networks can enhance their discriminability, storage, and retrieval. How can
such networks learn to decorrelate new complex patterns, as they arise in the
olfactory system? Using a computational network model for the dominant neural
populations of the olfactory bulb we show that fundamental aspects of the adult
neurogenesis observed in the olfactory bulb -- the persistent addition of new
inhibitory granule cells to the network, their activity-dependent survival, and
the reciprocal character of their synapses with the principal mitral cells --
are sufficient to restructure the network and to alter its encoding of odor
stimuli adaptively so as to reduce the correlations between the bulbar
representations of similar stimuli. The decorrelation is quite robust with
respect to various types of perturbations of the reciprocity. The model
parsimoniously captures the experimentally observed role of neurogenesis in
perceptual learning and the enhanced response of young granule cells to novel
stimuli. Moreover, it makes specific predictions for the type of odor
enrichment that should be effective in enhancing the ability of animals to
discriminate similar odor mixtures.
