4 ms·

I think the source of wonder here is that neural networks are very un-CPU like, in the level of correlation between the units. I'm not sure you could probe a CP

I think the source of wonder here is that neural networks are very un-CPU like, in the level of correlation between the units. I'm not sure you could probe a CPU in a similar manner and be able to recover meaningful information. A binary 101X10 are entirely different numbers depending on what X is, for example, and you woulnd't necessarily expect X to correlate with the place values elsehwere (e.g. 1st and 5th bit). Similar arguments, apply, to probing different components, such as registers or instruction memory.

swordsmith 6y ago
Funny you should compare understanding biological neural networks to a microprocessor. Check out this paper (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268 https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...), which systematically addresses how applying traditional neuroscience methods to analyzing a microprocessor would get you.
- jonnycomputer 6y ago
  I've read that paper years ago. I'm afraid I find it unimpressive. It is not so hard, really, to fail, when you fail to try. I have a computer science degree; I've built CPUs (from all the sequential and combinational circuits on up); I also work in neuroscience, and do some of the kinds of analysis this paper tries to criticize. For example, a very basic approach is design experimental tasks that have contrasting conditions, or events to be predicted. They don't do that. Even something as simple as pressing the controller left vs right vs not moving at all in donkeykong would have been more interesting. In any case, brains and traditional computer circuits work on different principles as I noted in my OP. Furthermore, there is quite a lot more redundancy in brains--and partial-redundancy is a bit of how it works; for example, if you have a population of neurons with different tuning curves, then the output in response to a stimulus can be integrated, and a likelihood distribution obtained for the actual value of a stimulus; that's just fundamentally different than how, say, a 5-stage pipelined RISC cpu works.

swordsmith 6y ago

Funny you should compare understanding biological neural networks to a microprocessor. Check out this paper (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268 https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...), which systematically addresses how applying traditional neuroscience methods to analyzing a microprocessor would get you.

jonnycomputer 6y ago
I've read that paper years ago. I'm afraid I find it unimpressive. It is not so hard, really, to fail, when you fail to try. I have a computer science degree; I've built CPUs (from all the sequential and combinational circuits on up); I also work in neuroscience, and do some of the kinds of analysis this paper tries to criticize. For example, a very basic approach is design experimental tasks that have contrasting conditions, or events to be predicted. They don't do that. Even something as simple as pressing the controller left vs right vs not moving at all in donkeykong would have been more interesting. In any case, brains and traditional computer circuits work on different principles as I noted in my OP. Furthermore, there is quite a lot more redundancy in brains--and partial-redundancy is a bit of how it works; for example, if you have a population of neurons with different tuning curves, then the output in response to a stimulus can be integrated, and a likelihood distribution obtained for the actual value of a stimulus; that's just fundamentally different than how, say, a 5-stage pipelined RISC cpu works.