Unsupervised learning and adaptation in a model of adult neurogenesis

Adult neurogenesis has long been documented in the vertebrate brain and rec ently even in humans. Although it has been conjectured for many years that its functional role is related to the renewing of memories, no clear mechan ism as to how this can be achieved has been proposed. Using the mammalian o lfactory bulb as a paradigm, we present a scheme in which incorporation of new neurons proceeds at a constant rate, while their survival is activity-d ependent and thus contingent on new neurons establishing suitable connectio ns. We show that a simple mathematical model following these rules organize s its activity so as to maximize the difference between its responses and c an adapt to changing environmental conditions in unsupervised fashion, in a greement with current neurophysiological data.