Neuroscience usually focuses on precision details, but doesn't aim to tell big picture stories. There are a few exceptions, however, like Karl Friston's free energy reduction model.
This seems like an odd take on Newton to me. What made his contributions important is that they were correct up to the precision we could measure for centuries.
We are nowhere near that for a subject like neuroscience.
There have been models adopted by scientests who at the time knew they were wrong and incomplete. For example ancient astronomy or medicine or logic. but their adherents tended to hold back science when new discoveries were made. So they are double edged swords.
Ok, maybe not quite to that level. But consider this paper, by great neuroscientists and cited over 1000 times, that models brain wave bands as harmonics, with band widths as the golden mean. [1, see figure 4]
My money is on some synthesis of the many theories in oscillatory neurodynamics. Neural resonance, dissonance, harmonics and entrainment... So many of the theories* have borne out empirically, but there has hardly been an attempt at synthesis.
* Theories like "Communication through coherence", "binding by synchrony", "phase amplitude coupling" + "working memory", etc etc etc.
[1] Klimesch, W. (2012). Alpha-band oscillations, attention, and controlled access to stored information. Trends in cognitive sciences, 16(12), 606-617.
https://aaai.org/ojs/index.php/aimagazine/article/view/2744
"A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics"
https://www.oxfordscholarship.com/mobile/view/10.1093/acprof...
Proven to be wrong when you watch people with serious brain injuries re learn skills, but shown to have value by how it can predict what tumors or injuries will do to someone's ability.
What would be really helpful is a model that can add to things we already know. Saying "studying for an exam engages the X part of the brain and uses the Y neurotransmitter" adds literally nothing to your understanding of studying (you can find out much more about studying by talking to people that are good at it and who have done a lot of studying themselves), it's just taking an everyday activity and identifying the small but still vastly complex portion of the brain that is activated more than others. Imagine being told that a particular bug in a 1-billion-lines-of-code codebase is due to some code within a 10-million-lines-of-code portion of it: that's great but how helpful is it really?
We need a story that explains how the brain uses rhythms (oscillations) for computation.
The ancients knew a fair bit.
The approach was focused on presymbolic processing -- and tried to optimize harmony. Harmony was, interestingly, the first mathematical model of the mind (by Pythagoreans/platonists in ancient Greece). It has a lot going for it these days, too, to understand oscillatory coupling in neural circuits. I learned recently that brain waves are harmonics (frequency doublings), which somehow I missed before!
Consonance results in greater periodicity, meaning that the action potentials are more likely to line up, whereas dissonance has less periodicity, so action potentials don't line up. It feels better (there is pleasure) when the action potentials align because of hebbian reinforcement (synchronous firing). This assumes that reinforcement would be pleasurable, but pleasure is the main reinforcer at a cognitive level.
The important question to ask is: is the deep learning abstraction any good?
There's a very strong case to be made that the answer is yes: deep learning systems can perform many (of course, not all, at least not yet) tasks that involve perception (computer vision/speech recognition), motor control (the recent openai robot), language understanding (machine translation/BERT/GPT), planning (alphago/dota/the deepmind protein folding), and even some symbolic reasoning (the recent work from facebook on symbolic integration https://ai.facebook.com/blog/using-neural-networks-to-solve-...). Some of these tasks are performed at such a high level that they become commercially useful, and in some cases, surpass "human level".
So here we have a "model family" -- deep learning -- with a set of principles so simple that it can be studied with intense mathematical rigor (for example, https://arxiv.org/pdf/1904.11955.pdf or https://papers.nips.cc/paper/9030-which-algorithmic-choices-...), and that produces many of the behaviors we want out of brains (and not just behavioral: see, e.g., https://arxiv.org/abs/1805.10734: " Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition tasks can serve as strikingly good models for predicting the responses of neurons in visual cortex to visual stimuli, suggesting that analogies between artificial and biological neural networks may be more than superficial." -- this is just one of many papers that show that even under the hood, trained deep learning systems exhibit many properties of biological neural networks).
These reasons strongly suggest (imho) that deep learning is in fact the newtonian theory of neuroscience. More strongly, no other theory comes remotely close in its simplicity and explanatory power.
Self driving cars can't leave an enclosed environment and might never do so safely.
Richard Dawkins spoke very highly of the brains ability to do some kind of natural calculus for the sake of tracking a ball in flight, but most animals run on simple tricks and reference points.
Deep learning might be the "good think" for the next ten years, some of us are not going to let go of the transcendent truth that the brain is not defined by what we think it is. I see limited reason to see deep learning as more likely than some emergent behaviour from a vast number of simple rules. Like animals flocking together in a boid sim.
Is this the same mistake as in "The Relativity of Wrong" [1]?
> people have thought they understood the Universe at last, and in every century they were proven to be wrong. It follows that the one thing we can say about out modern "knowledge" is that it is wrong.
> [...]
> My answer to him was, "John, when people thought the Earth was flat, they were wrong. When people thought the Earth was spherical, they were wrong. But if you think that thinking the Earth is spherical is just as wrong as thinking the Earth is flat, then your view is wronger than both of them put together."
Modelling the brain as a bunch of pistons or as a complicated machine or clockwork thing is a lot better than as a magical clay golem or opaque soul. Modelling it as a computer is even better than that. Not a computer in the sense of an x86 desktop exactly, of course, but the concept of computation is clearly fundamental to understanding the system. Similarly, the brain is not ResNet but concepts like backpropagation are probably useful.
So, sure, maybe people have been using the latest fad to explain the brain forever. But that's only bad to the extent that the latest fad is getting further away instead of closer.
1: http://hermiene.net/essays-trans/relativity_of_wrong.html
Ancients used to think that thinking happened in the gut and recently the microbiome pathway for describing thought has re-emerged. Both the gut pathway and the computational stream could be wrong.
The outcome of seeing the brain as a computational device will run out of juice like revelation has.
The problem is for all this power people still play chess,go and starcraft and we don't know how their brain works.
Daniel Coleman’s “Emotional Intelligence” offers a useful daily model for reasoning around it.
They exist but realize they’re going to “feel” different than physics models. With general physics knowledge one can literally implement tests and build on that independently.
With NS, one can read a model but without imaging machines and chemical testing... shrug... it’s harder to build a muscle memory.
Which neuroscience research argues is super important to connecting details into a composite one can intuit around competently.
Reading AND writing are important to learning English. Same with everything else.
TLDR the practical value of NS is already known: practice learning as we do, don’t be a dick.