People sometimes ask whether deep learning is a good model of the brain. I think that’s the wrong question. The brain isn’t trying to be efficient in the way a neural network is. It’s doing something stranger — running on noisy hardware, under metabolic constraints, across decades of experience. The gap between the two is where the interesting questions live. I’ve stopped being bothered by the fact that my models don’t map cleanly onto biology. The mismatch is data.
A lot of fMRI analysis is deciding what to ignore. The signal you care about is small. The noise is everywhere. The pipeline is a series of choices about what counts as real. I think about that outside the lab too. Most of what happens in a day is noise. The hard part isn’t collecting more information — it’s figuring out what to stop attending to. I’m still not very good at this. But at least I have a framework for feeling bad about it.
The short answer is: I wanted to understand why people suffer in the ways they do. The longer answer involves an undergrad psychology class, a summer working in a memory clinic, and a slow realisation that the questions I kept coming back to were empirical, not philosophical. Not what is consciousness but what goes wrong in depression, and why does it go wrong differently for different people. Neuroscience doesn’t have clean answers to that yet. That’s why I’m still here.