Implementing associative memory function in biologically realistic networks raises difficulties not dealt with in previous associative memory models. In particular, during learning of overlapping input patterns, recall of previously stored patterns can interfere with the learning of new patterns. Most associative memory models avoid this difficulty by ignoring the effect of previously modified connections during learning, thereby clamping activity to the patterns to be learned. Here I propose that the effects of acetylcholine in cortical structures may provide a neuropsychological mechanism for this clamping. Recent brain slice experiments have shown that acetylcholine selectively suppresses excitatory intrinsic fiber synaptic transmission within the olfactory cortex, while leaving excitatory afferent input unaffected. In a computational model of olfactory cortex, this selective suppression, applied during learning, prevents interference from previously stored patterns during the learning of new patterns. Analysis of the model shows that the amount of suppression necessary to prevent interference depends on cortical parameters such as inhibition and the threshold of synaptic modification, as well as input parameters such as the amount of overlap between the patterns being stored.