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We propose a new adaptive algorithm to separate a linear mixture of sources using an extended anti-Hebbian rule. This solution can be viewed as a stochastic gradient way to minimize certain output high order statistics. The system is modular: it is decomposed into parallel and independent subsystems. Each one is capable of extracting one source with negative kurtosis out of the mixture, provided the number of observations is greater or equal to the number of sources and provided it is appropriately initialized.