A framework for generalized group testing with inhibitors and its potential application in neuroscience
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The main goal of group testing with inhibitors (GTI) is to identify a small number of defective items and inhibitor items in a large set of items. A test on a subset of items is positive if it satisfies some specific property. Inhibitor items cancel the effects of positive items, which often make the outcome of a test containing defective items negative. Different GTI models can be formulated by considering how specific properties have different cancellation effects. This work introduces generalized GTI (GGTI) in which a new type of items is added, i.e., hybrid items. A hybrid item plays the roles of both defectives items and inhibitor items. Since the number of GGTI models is large (at least 96), we introduce a framework for classifying all types of items non-adaptively, i.e., all tests are designed in advanced. We then explain how GGTI can be used to classify neurons in neuroscience. Finally, we optimize the construction of disjunct matrices, which are an important tool in GTI.
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