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What we need is a Newtonian model of the brain. A model that is incomplete and "wrong", but useful and generative. While Newtonian physics may be "wrong", is mu

• Waterluvian 7y ago
  Don't we have that in the model that describes what the parts of the brain do? 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.
  • jagger27 7y ago
    I would equate our current understanding of the brain to the common knowledge that apples fall to the ground from trees; where else would they go? People knew that long before Newton.
    • im3w1l 7y ago
      Almost 2000 years before Newton, Archimedes wrote "Any object, totally or partially immersed in a fluid or liquid, is buoyed up by a force equal to the weight of the fluid displaced by the object." The ancients knew a fair bit.
  • dr_dshiv 7y ago
    I would argue not since it doesn't do much for explaining how it works. We know how computers process information. We don't really have a good story for how the brain does it. We need a story that explains how the brain uses rhythms (oscillations) for computation.
  • numpad0 7y ago
    We know how to wire FETs, we know this is an i5 and is fast, we also know how to solder thick wires or how to loosen large screws, but no one knows how to build even a Z80 out of some germanium is how neuroscience is understood in my understanding
  • conistonwater 7y ago
    Those models and explanations are not nearly as helpful as one might think. A lot of "neuroscience" explanations of everyday behaviour are mostly nonsense that sounds plausible and appealing (https://www.nature.com/articles/nrn3817 https://www.nature.com/articles/nrn3817, https://www.mitpressjournals.org/doi/abs/10.1162/jocn_a_00750 https://www.mitpressjournals.org/doi/abs/10.1162/jocn_a_0075...). In this context plausible doesn't even mean plausible to a neuroscientist, it means more like consistent with the nonsense a layperson has heard before. 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?
    • JackFr 7y ago
      In general I'm very skeptical of much neuroscience. (If an article ever says 'emerging neuroscience shows...' you can be assured what follows is almost certainly BS.) Yet I found the senior researcher in this article quite refreshing about the limitations of their research.
• protonfish 7y ago
  That's a question I asked myself about 10 years ago, so I made one. Is it perfect? Probably not, but I have strived to make it as rational and rigorous as I could. http://behaviorallogic.com/foundations/ http://behaviorallogic.com/foundations/
• mcilai 7y ago
  Deep learning?
• JoeSmithson 7y ago
  There are several of these (cognitive architectures) e.g. the ACT-R model[0]. [0] https://en.m.wikipedia.org/wiki/ACT-R https://en.m.wikipedia.org/wiki/ACT-R
  • The_rationalist 7y ago
    Which ones aren't BS?
    • JoeSmithson 7y ago
      I'm not an expert at all. I read the book How Can The Human Mind Occur In The Physical Universe? a few years ago and was basically left with the impression that actually cognitive science is way more advanced than I had realised. I was very struck with the amount of definite results and falsifiable experiments (i.e. "actual science"). I think it's one of those things like global poverty where if you are 30 or older, what you learnt at school, and your whole rule of thumb intuition, is totally wrong. https://www.oxfordscholarship.com/mobile/view/10.1093/acprof:oso/9780195324259.001.0001/acprof-9780195324259 https://www.oxfordscholarship.com/mobile/view/10.1093/acprof...
    • abrax3141 7y ago
      None of them as BS, or, put more positively, none of these are complete, but each offers different complexly interlocking partial models, at different levels of description. This is how we understand any complex system. Indeed, this is how we understand anything. Indeed, this is what it means to “understand”, that is, to use models to reason about something. Each of these, and all brain and cognitive science, provides different complexly inTeracting models that we use to reason about the system.
  • starpilot 7y ago
    I've noticed a trend of software engineers proposing that other fields create some abstraction that already exists. Last week someone avowed that a citizen regulatory body for aviation be created. Of course, that is the FAA.
  • dr_dshiv 7y ago
• rusk 7y ago
  You mean like “neuroscience”?
• koroe020 7y ago
  Translation: your level of familiarity with the material only allows you to “see” it as the separate details. 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.
• mcilai 7y ago
  Deep learning is as close to being a "newtonian theory" of the brain as it gets -- deep learning abstracts away a lot of the complexity of neural systems (e.g., a simple artificial neuron vs a highly complex biological one) while maintaining a number of essential characteristics: massively parallel computation, error tolerance, graceful degradation, distributed representations, information is stored in slowly-changing synapses, and, most importantly: a simple, local, and powerful biologically-plausible-if-you-squint-hard-enough learning rule. 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-advanced-mathematics-equations/ 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 https://arxiv.org/pdf/1904.11955.pdf or https://papers.nips.cc/paper/9030-which-algorithmic-choices-matter-at-which-batch-sizes-insights-from-a-noisy-quadratic-model.pdf 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 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.
  • friendlybus 7y ago
• deleted 7y ago
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• ImaTigger 7y ago
  What you're looking for is Elman et al's theory in Rethinking Innateness (https://mitpress.mit.edu/books/rethinking-innateness https://mitpress.mit.edu/books/rethinking-innateness) It's more like Darwin than Newton, and is (to the point of another post off this thread) an early deep-learning-like theory of how the brain (or at least the cortex) becomes organized.
  • dr_dshiv 7y ago
    Have you read smolensky's harmonium paper? It's the first restricted Boltzmann Machine -- and I believe elman and smolensky were colleagues with hinton back at UCSD (with rumelhart and Don Norman, et al). 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!
    • ImaTigger 7y ago
      I know about Smolensky's theories (have probably read that paper, but don't remember it exactly; have def. read others by PS); PS and JE are definitely contemporaries, and work/have worked in similar areas. However, these two theories operate at different levels and time scales. The oscillatory coupling theories of PS et al are related to real time computations carried out by neural networks, whereas the trophic wave theories of JE et al. relate to how these networks come to be organized as they are. As per other posts, both are useful, and probably both true to some extent. Neither is directly applicable yet in a way that makes contact with the cognitive level.
      • dr_dshiv 7y ago
        One thing that shocked me was that no one ever tried to connect "cognitive dissonance", one of the most successful social psychology theories of all time, to actual dissonance in neural oscillations. 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.
• SquishyPanda23 7y ago
  > What we need is a Newtonian model of the brain. 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.
  • dr_dshiv 7y ago
    It's a great point. And I agree that precision in stars or mechanics seems much simpler than neuroscience -- but I do actually think that there will be some laws akin to f=ma. 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.