[skohan]

I also tend to be skeptical that ANNs are very useful as a model for brain function. In vivo neural networks are so complex and so dynamic when compared ANNs.

In my opinion, the fact that even such a massively simplified model of one specific subtype neural processing has been able to give as powerful results as we have seen from Deep Learning should give us an appreciation for how much there still is for us to learn about this staggeringly complex system.

I would guess that the next great advancements will come from using better understandings of the brain to build better ANNs, not the other way around.

[iciac]

Learning's likely to be bidirectional. ANN (as a mathematical analogue) is independent to the biological function (the original and key inspiration). Advances in network architecture (e.g. the recent trend towards skip connections and parallel processes) is likely to give insight to how an underlying, more complex system is likely to operate. In particular, systematic errors made by ANNs under given frameworks have a tendency for existing in some form in psychology and biology. Since conceptual thinking from both domains can directly feed towards each other, it's a rare bootstrap moment with the potential for rapid advances in both directions.

[skohan]

> Advances in network architecture (e.g. the recent trend towards skip connections and parallel processes) is likely to give insight to how an underlying, more complex system is likely to operate.

Maybe. The thing about these advances in ANNs is, so we have any reason to believe they have anything to do with the way biological neural networks work? It might be the case that these kinds of advances correlate to a more accurate understanding of how our brains process information, or it might also be the case that these are just optimizations on a mathematical model which is fundamentally different to biological intelligence.

To me advances in the other direction are much more compelling. We actually know quite a lot about how biological neural networks work. The way that electrical and chemical signals are transmitted is quite well understood, and can be accurately modeled through mathematical models derived from physics and physical chemistry. At the moment, the problem seems more to be more about how to accurately model this system at scale which we already have tons of data on.

It's not that I think these innovations in ANNS have no value, it's just that it seems that ANNs are quite tangential to neuroscience.

[crawfordcomeaux]

From studying ANNs, I've reconceived of how I view myself from a programmatic perspective. I have used the resulting models to change myself in useful ways. If they're not perfectly accurate, they may still be accurate enough to be useful.

The trick, to me, is to avoid falling into the trap of thinking imperfect models aren't useful. Then the accuracy matters less.

An example of a useful intuition was realizing choosing to believe something is a skill and I can choose to believe the opposite of anxious thoughts to safely defuse anxiety as long as I'm meeting my needs.

I know people who've been in therapy for a long time before learning that one, so I'm gonna keep using ANNs as a guide for self-hacking. It's way too useful to me.

[skohan]

It's fine and good that ANNs can serve as a metaphor for your own mind. That's something very different than saying they're going to be useful in unraveling the scientific mysteries of the brain.