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Comment by dschuetz | Hacker News Reader

"... Our analogies often look to artificial neural networks: for neural networks compute, and they are made of up neuron-like things; and so, therefore, should brains compute. But if we think the brain is a computer, ..." OK, enough of this. Neurons are not computers. There is nothing what can be compared to actual neurons. "Artificial" neurons are just reduced models of the real ones, so that only the "compute" parts are used to calculate input vectors. It's only a fraction of what the real neurons actually do.

While I appreciate the article trying to actually understand what's really going on in neural networks, let's not make unnecessary dumbed-down assumptions. At least the subtitle of the article is actually correct. The main title is sensationalist "...17 billion computers!!".

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It's funny, I thought the opposite. I was happy to read an explanation by an eminent and respected systems neuroscientist on hierarchy of computation, rather than the musing of an undergrad computer scientist on their first encounter with nature neuroscience.

An ANN is just calculus with matrices that does not lend itself, by the mathematics alone, to any "neuronal" description which is ad-hoc and imposed from the outside. You can draw many computations in "neuronal" form, eg., a logistic regression. It's really just a way of diagramming math.

"Computer" is an observer-relative term. There is no physical property of a system which makes it a computer. A "digital computer" is just a tool made of silicon which we use to aid computation (a goal we have). There are many tools (from an abacus to a waterfall) that we can use to aid in computation.

"Computation" isn't anything other than a goal we have. To interpret the brain as engaged in it carries no information and says nothing explanatory. The sense in which a brain is a computer is the same sense in which everything is: a physical system whose state evolution can be used to aid in computation (but isnt: no one uses brains to compute).

Let's not go too far. When we talk about the brain being a computer, we mean it in a sense in which some μC/μP and related paraphernalias forming a computer inside your phone, or any other electronical device - that is, a component which takes all or most of the inputs, drives the device, and which if you rip out, you're left with a useless and dead machine.

A rock and a waterfall perform computations in a theoretical sense if you look at them right, but there's also a different sense in which a bunch of op-amps in a circuit are a computer, a bunch of hydraulic logic gates are a computer, and a Raspberry Pi is a computer, but a rock isn't a computer. In that sense, human and animal brains are computers too.

(I'm before my morning coffee so I apologize for not being able to name "that sense" properly.)

I'd be very interested in knowing what this "sense" is -- I don't think there is one.

I chose waterfall by symmetry with a hydraulic computer. A waterfall is just what we call a hydraulic computer when we're not using it for computation.

I still only see an observer-relative distinction (ie., in how it is used). One can "juggle rocks" to compute.

You've said something about "inputs" and "useless when removed". Both of these are again observer-relative. Useless gives it away.

An animal isn't "useless" when its brain is removed, because it isn't our use of it which makes it alive.

I don't disagree, but you've just described the Motte and this time omitted the Bailey. I'd like the Bailey folks to at least take pause.

I don’t think it’s helpful to eliminate teleological accounts categorically. This writing is clearly for public consumption and benefits from simplying descriptions of what’s happening, so that the layman reader might have the impression of having understood something.

Computational neuroscience could be written off by your statement that the brain is not a computer, but perhaps soften your stance and accept that that allows for applying tools from computer science to ask questions, just as physicists do.

Well my stance is hard for the sake of ruling out computer science as an explanatory framework for neuroscience.

Computational metaphors arent explanatory, they're illusory sorts of explanations (like narrative) which "satisfy" without providing a causal model (ie., a scientific explanation).

I'm not convinced they have been helpful, and mostly end up giving deeply mistaken impressions about the nature of digital computers -- rather than helpful impressions about the nature of brains.

Computational metaphors about the brain usually acknowledge explicitly the nonlinearity of information transformation, and use the word computation under the assumption the transformation is doing something useful. This hardly seems controversial so I find the statements you’re making a bit allergic.

I only agree that it's good to have articles using layman's terms. But a problem arises when false/simplified claims are made from which the uneducated reader will make false or exaggerated assumptions! For example, an article says "Quantum Physics is so weird that it's almost magical!" The uneducated reader only can make the assumption "Quantum Physics is magical, because it's weird!" due to lack of background knowledge and understanding of terms. Same goes for any other popular science article/journal.

The key is to explain using layman's terms without making false claims while trying to simplify the subject.

I agree but it is rare to find a layman’s explanation that is faithful in way all the experts can agree on.

Well, when I had tried to say the same thing her HN awhile ago I was heavily downvoted. So you agree with Searle then? (I almost, but I leave a possibility he is wrong).

Your argument is solely based on the rather odd semantics you ascribe to the term "computer". In common contemporary parlance, hardly anyone would call an abacus or a waterfall a 'computer' just because you can use such things. Conversely, I think you would not cease to call a desktop PC a computer if it is solely used for playing games and never used for 'making computations'.

When people call a thing a 'computer' they mean that it can run (potentially complex) algorithms to produce outputs based on inputs, in many cases it would also be assumed that it has memory, and it would not have much in the way of direct physical interactions with the world (although it might be part of systems that have such interactions). Based on these notions, it seems very valid to interpret brains as some kind of computer, made out of cells instead of metal and silicon, and formed by evolutionary pressure instead of human engineering.

Yes, and I'm calling your view here incoherent or, at least, mistaken.

It isnt valid to interpret brains this way. An "algorithm" isnt something a physical system can "run". These are observer-relative properties a system instantiates because of our use of it.

A brain is a physical system and our model of it should be a causal model in terms of its intrinsic properties that do not "disappear" when not in use.

A digital computer is an oscillating electrical field across a piece of silicon whose oscillations "correspond" to steps in an "algorithm" but only to an observer who imposes those correspondances. The program "1 + 1" on a digital computer is some state transitions of the cpu's electrical state -- it is an algorithm only when we impose an meaning on those oscillations that they do not themselves have.

There is no physical distinction in the "waterfall computer" and "digital computer" that bares on them being computers. They are both computers in exactly the same way. We havent gone to the length of a water-to-lcd display, but there's no reason we couldnt.

Water falling over a series of moveable rocks can (easily) be arranged to display the result of "1 + 1" on an LCD (eg., bucket/pixel, with water level = intensity, pipes to handle "graphics").

It is your view that is incoherent. A brain and and a PC are physical systems that work based on causal physical mechanisms. Both can run algorithms, e.g., my brain and a computer can both instantiate the same algorithm, albeit in very different ways. Your argument actually seems to be based on some notion of function, or extrinsic utility, or teleology of some sort, i.e., computers are artifacts created for a specific use, while animals and their brains just exist in themselves, or something to that effect?

All models are wrong, some are useful.

Not all models are wrong, since some may be modelling exactly the aspects of importance and not modelling exactly those which are incidental and of no consequence.

Then they are still wrong. It's just that they are wrong in the part that you don't care about.

Is it wrong, or is it simply an instance of the fact that a model is not the thing it models?

The use of the word wrong sounds wrong to me here. The wrongness should be about whether the model accurately, or just approximately, or not at all, shares with the modelled object that part of the behaviour we are interested in.

The point is that no model of a complex real world phenomenon is ever perfect, so, you shouldn't try to create a perfect model. But not being perfect doesn't mean that a model can't be useful; among all the possible "wrong" models, you need to identify the one(s) which is useful, ie, which models well the aspects of importance to you.

I realize all that. I'm questioning the wisdom of the standard manoeuvre of preemptively calling all models imperfect or wrong, though.

Not everybody realises all that, so I suppose the OP cited that quote to reinforce the point that ANN are a very useful, but very imperfect model of the brain. I cite it often enough, but usually it's in person, where I can see the reaction of the person I'm talking with, and explain if necessary.

All models are wrong. The map is not the territory. We are arguing semantics now. The point you're trying to argue is that a model is "right" in the sense that it models whatever we wanted it to model. We argue that a model is "right" if it captures the "true" nature, which it will never be able to do (because of its virtue of being a model) and hence all models are wrong.

Semantics. Unless you disagree of course.

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