Neuroscience Can't Explain How a Microprocessor Works (economist.com)

mspohr writes: The Economist has an interesting story about two neuroscientists/engineers -- Eric Jonas of the University of California, Berkeley, and Konrad Kording of Northwestern University, in Chicago -- who decided to test the methods of neuroscience using a 6502 processor. Their results are published in the PLOS Computational Biology journal. Neuroscientists explore how the brain works by looking at damaged brains and monitoring inputs and outputs to try to infer intermediate processing. They did the same with the 6502 processor which was used in early Atari, Apple and Commodore computers. What they discovered was that these methods were sorely lacking in that they often pointed in the wrong direction and missed important processing steps.

Comments

Modern (pseudo)-"Science"

[Anonymous Coward]

In order to understand the DNA of an Orange, we "scientists" dissected an alarm clock. This _proved_ that our methods of studing oranges, and fruit in general, have been wrong for centuries.

Intelligent design

[glitch!]

I know I'll catch hell for my religious beliefs, but...

I think that the 6502 was not the result of evolution, but rather it had a Creator and was the product of Intelligent Design. There are just so many subtle clues that suggest features that were deliberately put in there. Could natural selection really explain how it had two different indirect access modes, one that selects a direct index from an offset, and the other adds the offset to the index?

These researchers may be trying to apply the wrong methods to a device that is almost certainly the product of a higher power.

Re:Intelligent design

[Anonymous Coward]

And the great and powerful Woz spake thusly: Let the Intel become the brain of my new creation! But lo! The Book of Jobs decreed the creation be cost effective and priced by the Number of the Beast. And so out of the land of Silicon came Forth the 6502 to eat from the Apple tree. Eight shall be the number of bits, no more and no less.

Re:Intelligent design

[Anonymous Coward]

All jokes asside, I think the point here was that both devices (6502 or fatty-thinkmeats) were modeled as a black box. I'd be willing to be that a significant fraction of the neuroscientist population would argue for a 6502 being the simpler system, so the blackbox approach should (one would hope) be able to model that device more easily. If they find that their blackbox approach to understanding a 6502 leads to incorrect results, then it raises questions as to the effectiveness of the approach on the thinkmeats.

Re:Massive failure from all involved

[im_thatoneguy]

This isn't so much about modeling thought processes as it is about illustrating how even in a simplified model one of our debugging approaches fails.

The logic that they're arguing appears to be:

"If we can't even properly reverse engineer an extremely simple deterministic computer chip using fault modeling, it's extremely unlikely that we can infer the mechanisms of an extremely complex non-deterministic processor like the brain."

There are some interesting ramifications.

[mmell]

We once had machinery that did computations (example: adding machines). It seemed natural to try to model the brain as a complex machine then.

We once had electronic circuits designed to perform calculations (example: Enigma). It seemed natural to try to model the brain as a complex electronic device.

We now routinely use silicon integrated circuits to perform calculations (example: the IBM PC-XT). It seems natural now to try to model the brain as a complex general computing device.

The take-away point I get from this is that we may need another revolutionary technology or two (fully three-dimensional integrated circuits? IC's based on carbon instead of silicon?) before we can model the sentient mind as similar to an artificially created device. Such advances may also be required before we can create (invent?) a true "artificial intelligence".

Re:Massive failure from all involved

[ShanghaiBill]

"If we can't even properly reverse engineer an extremely simple deterministic computer chip using fault modeling, it's extremely unlikely that we can infer the mechanisms of an extremely complex non-deterministic processor like the brain."

But that logic only makes sense if microprocessors and brains were similar enough that comparable methods could be used to attempt to understand them. But that isn't true. That is like saying you can't understand how to plow a field with a horse if you don't understand how a tractor engine works. Although horses and tractors have some similarities, understanding how one works doesn't really help you with the other.

Re:Massive failure from all involved

[John Allsup]

The point of the argument is to challenge the implicit assumption that current neuroscience methods work as well as people think they do. If you just assume your research methods work, you are resting on blind faith in your methods. One step in showing the need to challenge those foundational assumptions is to use this example to //illustrate// how then can fail. Using microprocessors allows is the luxury of total knowledge as to what we are investigating, at the expense of being quite different to the brain. The quoted bit needs fixing:

"If we can't even properly reverse engineer an extremely simple deterministic computer chip using fault modeling, it's extremely unlikely that the same fault modelling will work reliably with something extremely complex like the brain."

It does not show whether or not 'fault modelling' works or not for the brain, but gives good justification for the claim that we cannot take the efficacy of 'fault modelling' for granted when studying the brain.