From the actual study's abstract: > Unique to Neuroscience, deep learning models can be used not only as a tool but interpreted as models of the brain. The cen

From the actual study's abstract: > Unique to Neuroscience, deep learning models can be used not only as a tool but interpreted as models of the brain. The central claims of recent deep learning-based models of brain circuits are that they make novel predictions about neural phenomena or shed light on the fundamental functions being optimized... Using large-scale hyperparameter sweeps and theory-driven experimentation, we demonstrate that the results of such models may be more strongly driven by particular, non-fundamental, and post-hoc implementation choices than fundamental truths about neural circuits or the loss function(s) they might optimize. Finally, we discuss why these models cannot be expected to produce accurate models of the brain without the addition of substantial amounts of inductive bias, an informal No Free Lunch result for Neuroscience. In conclusion, caution and consideration, together with biological knowledge, are warranted in building and interpreting deep learning models in Neuroscience. And IMO a succinct description of the problematic assumption being cautioned against in the study's introduction section: > Broadly, the essential claims of DL-based models of the brain are that 1) Because the models are trained on a specific optimization problem, if the resulting representations match what has been observed in the brain, then they reveal the optimization problem of the brain, or 2) That these models, when trained on sensibly motivated optimization problems, should make novel predictions about the brain’s representations and emergent behavior. --- I think to most, the problem with claim number 2 directly above is obvious, but it's important to also look at claim 1.