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The main goal of group testing with inhibitors (GTI) is to efficientlyidentify a small number of defective items and inhibitor items in a large setof items. A test on a subset of items is positive if the subset satisfies somespecific properties. Inhibitor items cancel the effects of defective items,which often make the outcome of a test containing defective items negative.Different GTI models can be formulated by considering how specific propertieshave different cancellation effects. This work introduces generalized GTI(GGTI) in which a new type of items is added, i.e., hybrid items. A hybrid itemplays the roles of both defectives items and inhibitor items. Since the numberof instances of GGTI is large (more than 7 million), we introduce a frameworkfor classifying all types of items non-adaptively, i.e., all tests are designedin advance. We then explain how GGTI can be used to classify neurons inneuroscience. Finally, we show how to realize our proposed scheme in practice.

A framework for generalized group testing with inhibitors and its potential application in neuroscience