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A biologically realizable, unsupervised learning rule is described for the online extraction of object features, suitable for solving a range of object recognition tasks. Alterations to the basic learning rule are proposed which allow the rule to better suit the parameters of a given input space. One negative consequence of such modifications is the potential for learning instability. The criteria for such instability are modeled using digital filtering techniques and predicted regions ofdoi:10.1109/tnn.2004.841795 pmid:15787138 fatcat:blqvnz3suzcfdkumvr3qjgy7gi