A deep learning framework for neuroscience(nature.com) |
A deep learning framework for neuroscience(nature.com) |
The fact is, there have been neuroscientists working with neural network models with greater and lesser complexity than DNNs for decades. They've been utilized to great profit outside of neuroscience lately, but that doesn't make them not an abstraction of some aspects of cortical computation.
We don't quite understand how brains could perform or approximate backprop yet, but it's the only training algorithm that has been remotely successful at training networks deep enough to do human-like visual recognition. So many people take that as a big clue as to what we should be looking for in the brain to explain its great performance and ability to learn, rather than a reason to disqualify DNNs entirely.
There's plenty of modeling work going on with more traditional biophysical models, such as those that include spiking, interneuron compartments, attractor dynamics, etc. This is just an attempt to also come at the problem from the other direction, starting from something that we know works well (for vision) and trying to figure out how to ground it in biophysical reality.
The book On Intelligence by Jeff Hawkins was a fantastic read on HTM and similar concepts. (https://amzn.to/2JyQDF3)
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.
They are NOT proposing that neural nets architectures are analogous to brain architectures. I'm guessing their focus is on the lowest level activities in simple brains, perhaps afferent/efferent sensory perception, metabolic and physiological regulatory control -- the kind of things instrumented in worms like C. Elegans.
Looking at this article, i wonder if we'll ever be able to figure any of this out. I feel pretty hopeless about the entire situation.
I'm not saying "don't worry about it" as an uncle with two nieces I know how difficult it would be just to ignore something. You want your family and dependents to have happy full lives and every little struggle makes you worry and think. But what I am saying is, have a little hope. When you see articles like this, they are normally talking about theoretical and philosophical meanings of what intelligence and consciousness is, there is plenty of solid practical and applied science and progress that relates to your daughter's situation.
Don't get yourself worked up about the ponderings of math and computer nerds. The real life changing stuff is not being done in AI right now, its being done in universities, hospitals, and laboratories by scientists, doctors, and professors.
https://singularityhub.com/2019/10/03/deep-learning-networks...
So it sounds like they want to use this componentization of neural processing to try to understand biological neural networks better.
could be but then most of the authors of the paper work in human cognition so I'm slightly skepical that their focus is going to be at the level of cellular biology
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.
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.