A computationally efficient method for modeling neural spiking activity with point processes nonparametrically


Point process models have been shown to be useful in characterizing neural spiking activity (NSA) as a function of extrinsic and intrinsic factors. Most point process models of NSA are parametric as they are often efficiently computable. However, if the actual point process does not lie in the assumed parametric class of functions, misleading inferences can… (More)
DOI: 10.1109/CDC.2007.4434240


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