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Neural representations of sensory, cognitive, and motor states and processes, and associated computations over these representations, may link physical and mental phenomena. Neural representations and computations can be studied at the level of single neurons or at the level of neural networks. Both levels of analysis employ the same logic to link physical and mental phenomena, using correlation, causation, and modeling to understand representations and computational mechanisms. By examining and comparing these approaches in depth, I argue that the explanatory objects used by the neural network framework are better suited to studying most cognitive states and processes. Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of neural networks can solve problems that cannot be addressed by analyzing neurons independently. In this opinion article, I argue that while both frameworks employ the same general logic to link physical and mental phenomena, in many cases the neural network framework provides better explanatory objects to understand representations and computations related to mental phenomena. I discuss what constitutes a mechanistic explanation in neural systems, provide examples, and conclude by highlighting a number of the challenges and considerations associated with the use of analyses of neural networks to study brain function. [ABSTRACT FROM AUTHOR]