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Comment by hprotagonist | Hacker News Reader

A classic.

its spiritual sequel: “Can a neuroscientist understand a microprocessor?” https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...

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I'm a trained physicist (undergrad) in biology (phd). I've always wanted to give a retort "could a physicist understand a microprocessor?". the answer is no, no they couldn't, unless they were trained in computer science. The abstraction of 'state evolving in time according to analytical equation' really breaks down when looking at computational systems, such as a microprocessor, as well as a biological cell.

Interesting. My intuition is the opposite [1]: computers are highly optimized to decouple the different functions and subsystems. That is, even when the state space is huge, only a very narrow set of state transitions is possible. This constraint is what makes experimentation tractable, in contrast to biological systems, where state will bleed across the whole system because, at some point, that was a fit adaptation.

I think that view is historically validated in that, in wartime, one side would steal the other's encryption hardware (e.g. the Enigma machine) and be able to piece together a meaningful description of what it's doing, without access to the original designers, and even get to the point where they could pose what encryption function they're trying to reverse.

Of course, there's a key distinction there -- those were largely mechanical systems, while the question here is about tiny microprocessors. So I'd agree there's a sort of pre-requisite that physicists be aware of the existence of some key primitives that the system operates on (here, current, voltage, electricity, etc. along with the ability to see small enough parts). If that's what you meant by being "trained in computer science" then I'd agree, though that's a non-standard use of the term.[2]

In any case, there's more to the physicist's toolbox than closed-form analytical equations.

[1] My earlier comment: https://news.ycombinator.com/item?id=16818220

[2] Per the Dijstrka quote ("Computer science is no more about computers than astronomy is about telescopes"), compsci is about the limits of computational processes, not electronic hardware per se.

> Per the Dijstrka quote ("Computer science is no more about computers than astronomy is about telescopes")

Off topic, as the origin and precision of the quote doesn't affect your point, but: AFAICT the closest Dijkstra actually said to this was in https://dl.acm.org/doi/10.5555/25596.25598 :

> As a result, primarily in the U.S., the topic became prematurely known as "computer science"---which actually is like referring to surgery as "knife science"---and it was firmly implanted in people's minds that computing science is about machines and their peripheral equipment.

I'm a physicist. When I was in school, a year of electronics was typically required for an undergrad physics degree. The capstone project at my college was building a 4-bit minicomputer from gates.

There were two reasons for this tradition. First, physicists are often called upon to act as multi-disciplinary problem solvers. Second, experimental physics is almost exclusively electronic and computerized. Things like data acquisition boards for personal computers were only beginning to be commercialized, and a lot of us rolled our own gear.

I always thought the spirit of the paper was "we have a set of techniques to probe the brain and try to understand functionality (e.g. calcium/voltage indicators, optogenetics, laser ablation); could we have understood how a black-box microprocessor works by using EE equivalents of these techniques?" It's less about applying equations and more about, say, activating one transistor at a time and seeing what happens.

that is the spirit of the paper, and the answer is a resounding “no, not even close!”

This paper is so frustrating! They chose a microprocessor-based system, where a single set of components does all sorts of things, switching function millions times per second. Of course you cannot analyze it using biological methods, no organisms work like that!

A much better thing to analyze would be something FPGA based, or even old CPU-less games like PONG. Look at the labeled schematics -- 6th image at [0]. Each element has a 2-letter label like G2 or B3, those are actual chip position.. so if you are looking at ball/paddle collision circuit, it is B2 or G3. So for example damaging 2nd chip in 2nd row (B2) will make ball/paddle collision detection not work, 5th chip in first row (A5) will prevent vertical ball movement and so on..

This would give a totally different conclusion for the paper, which I think is much more realistic.

[0] https://www.aussiearcade.com/topic/81725-atari-pong-pcb-repr...

> where a single set of components does all sorts of things, switching function millions times per second. Of course you cannot analyze it using biological methods, no organisms work like that!

uh, what do you think the brain does ..? does it have a magic special place cell for every place you’ve ever remembered being? (no)

The brain has specific area for each function - one part of the brain is responsible for sense of smell, another for vision, etc.. See Wikipedia [0] for basic introduction to this.

Compare this to CPU, where a single register bank, ALU, memory controller are used for everything - player movement, score keeping, sound, disk access, keyboard input handling.

[0] https://en.m.wikipedia.org/wiki/Brodmann_area

it gets substantially more complicated than this very rapidly, i’m sorry to say.

And the debate between anatomists and physiologists rages as fiercely as ever.

I don’t deny the existence of anatomically distinct brain regions — my research area is in the auditory cortex — but i promise you it’s not nearly as simple as “one area one function”

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