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In a range of domains, in particular higher level brain areas, DL models trained on imagine are already the best predictive models of brain function. If they ar
by neuronerd 7y ago
In a range of domains, in particular higher level brain areas, DL models trained on imagine are already the best predictive models of brain function. If they are better than all other models at describing the data, why would we say they have nothing to do with neuroscience?
- mistrial9 7y agobecause any set of math functions might do really well at predicting within a certain domain, and then produce noise or worse with new cases outside of the trained area.. perhaps more importantly, from a psychological point of view, a substitution error by humans, of replacing one not-understood system (mind) with another (black box training via NNs) is common and may be incentivized, too
- briga 7y agoAs far as I know there is no evidence that the brain has any analogue to the back-propagation used to train pretty much all modern neural networks. Back-propagation is a good way to optimize neural networks, but it doesn't seem to be the way brains optimize neural networks.
- canjobear 7y agoDL models are also the best way to predict the behavior of three-body systems in physics. Would you say DL models tell us something about physics?
- etbebl 7y agoYou're talking about the output of a deep network predicting the solution to a problem it was trained on. They're talking about something completely different: the properties of the whole network (opening up the "black box") correlating with/predicting properties of brain regions while they perform similar tasks.