5 ms·

The location of the electrodes aren't arbitrary. The region of implantation is likely the hand-motor cortex (which has thickness on the order of mm), the area a

The location of the electrodes aren't arbitrary. The region of implantation is likely the hand-motor cortex (which has thickness on the order of mm), the area associated with arm movements (check out somatotopy), so the signals acquired are actually targeted for the demo task. This relationship has been known in neuroscience for almost a century and has been validated in decades of brain-machine interface experiments. They are also recording from up to 1000 channels, which is probably overkill for mind pong, tbh. But you'll need that many implanted electrodes to study long term electrode biocompatibility.

mattkrause 6y ago
Even outside of primary motor/somatosensory areas, 2D cursor control is a pretty standard BMI paradigm. Not exactly “Hello, World” but maybe “ToDo List App”.
- caddemon 6y ago
  Yeah their implant hardware is really impressive but "mind pong" is a pretty common starter project for at home EEG hacking, let alone legit BCI research. The internet is littered with examples of mind pong for the Muse like this one: https://medium.com/@nayvelt.lina/playing-ping-pong-with-my-brain-49025f044b9f https://medium.com/@nayvelt.lina/playing-ping-pong-with-my-b... Granted it moves slower than actual Atari Pong, and takes a few minutes to get the hang of controlling the paddle. But IME it isn't much harder to control the up/down of the paddle with the EEG than it is to play a game like flappy bird, and that's using a couple random scalp electrodes from a consumer device. So yeah thanks for bringing this up, because I feel some of the comments here are acting like this is much closer to "mind reading" than it actually is. Not that it isn't cool, but the overhyping kinda kills it for me lol. Is this how robotics people feel when Boston Dynamics releases a new video?
- swordsmith 6y ago
  I'd argue it is the "Hello, World" in any BMI lab. Assuming existing implants and electronics, and non-naive monkey, advanced undergrads should get it in a semester.
  - mattkrause 5y ago
    That's why I went with To Do list :-) It's not totally trivial, but a reasonably skilled person should be able to get a not-too-janky version working with a bit of effort. (Also, where are you that undergrads get to interact with monkeys?!)
jonnycomputer 6y ago
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 5y 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.

mattkrause 6y ago

Even outside of primary motor/somatosensory areas, 2D cursor control is a pretty standard BMI paradigm. Not exactly “Hello, World” but maybe “ToDo List App”.

caddemon 6y ago
Yeah their implant hardware is really impressive but "mind pong" is a pretty common starter project for at home EEG hacking, let alone legit BCI research. The internet is littered with examples of mind pong for the Muse like this one: https://medium.com/@nayvelt.lina/playing-ping-pong-with-my-brain-49025f044b9f https://medium.com/@nayvelt.lina/playing-ping-pong-with-my-b... Granted it moves slower than actual Atari Pong, and takes a few minutes to get the hang of controlling the paddle. But IME it isn't much harder to control the up/down of the paddle with the EEG than it is to play a game like flappy bird, and that's using a couple random scalp electrodes from a consumer device. So yeah thanks for bringing this up, because I feel some of the comments here are acting like this is much closer to "mind reading" than it actually is. Not that it isn't cool, but the overhyping kinda kills it for me lol. Is this how robotics people feel when Boston Dynamics releases a new video?
swordsmith 6y ago
I'd argue it is the "Hello, World" in any BMI lab. Assuming existing implants and electronics, and non-naive monkey, advanced undergrads should get it in a semester.
- mattkrause 5y ago
  That's why I went with To Do list :-) It's not totally trivial, but a reasonably skilled person should be able to get a not-too-janky version working with a bit of effort. (Also, where are you that undergrads get to interact with monkeys?!)

jonnycomputer 6y ago

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 5y 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.