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Much of the controversy evoked by the use of deep neural networks (DNNs) as models of biological neural systems amount to debates over what constitutes scientific progress in neuroscience. In order to discuss what constitutes scientific progress, one must have a goal in mind (progress towards what?). One such long term goal is to produce scientific explanations of intelligent capacities (e.g. object recognition, relational reasoning). I argue that the most pressing philosophical questions at the intersection of neuroscience and artificial intelligence are ultimately concerned with defining the phenomena to be explained and with what constitute valid explanations of such phenomena. As such, I propose that a foundation in the philosophy of scientific explanation and understanding can scaffold future discussions about how an integrated science of intelligence might progress. Towards this vision, I review several of the most relevant theories of scientific explanation and begin to outline candidate forms of explanation for neural and cognitive phenomena.

Original publication




Journal article


Center for Open Science

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