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It's an interesting idea here. The paper is arguing that current brain analysis methods don't work well in an alternative environment with a lot of data, so may
by chrisfosterelli 9y ago
It's an interesting idea here. The paper is arguing that current brain analysis methods don't work well in an alternative environment with a lot of data, so maybe the methods are the problem instead of our lack of data in neuroscience.
However, I think this misses part of the point. We use these methods because we have very little data available. There are tons of interesting new ways to analyze brain data that I think computational neuroscientists are dying to explore, but don't have enough data to do so. If we had a lot more data, we might not be using these approaches.
- bordercases 9y agoThis explains the diversity of methods and their emphasis on statistical inference. But it isn't necessarily true, that the current best choice for methods implies that the outcomes from using these methods are good. In this case, if we expect that these methods are underpowered in the ecological context, then we should expect them to perform better in a more controlled one, or else they would be useless. But given the author is correct... then, well, there's obviously room for more thought.
- neltnerb 9y agoI think a big part of the point was that by demonstrating that the analysis software works on toy systems, you can get a big jump on your validation of those analysis methods. Why wait until you have the best data if there seems to be a lot of low hanging fruit you can get now? It could be wasted effort, but if it does work wouldn't you be more confident on real data? Even if it doesn't prove to be relevant, this type of statistics seems very interesting and widely applicable. Obviously reverse engineering, probably identifying alien life, characterizing group behavior on the internet... anywhere where you're still discovering structure and don't know what to expect.