The paper, at least as shown here, [1] is vague about which results came from implanted electrodes and which came from functional MRI data. Functional MRI data is showing blood flow. It's like looking at an IC with a thermal imager and trying to figure out what it is doing.
Just to clarify, the paper [0] does use both implanted electrodes and fMRI data, but it is actually quite transparent about which data came from which source. The authors worked with two datasets: the B2G dataset, which includes multi-unit activity from implanted Utah arrays in macaques, and the Shen-19 dataset, which uses noninvasive fMRI from human participants.
You’re right that fMRI measures blood flow rather than direct neural activity, and the authors acknowledge that limitation. But the study doesn’t treat it as a direct window into brain function. Instead, it proposes a predictive attention mechanism (PAM) that learns to selectively weigh signals from different brain areas, depending on the task of reconstructing perceived images from those signals.
The “thermal imager” analogy might make sense in a different context, but in this case, the model is explicitly designed to deal with those signal differences and works across both modalities. If you’re curious, the paper is available here:
That could be an interesting project in itself, take a simple 8 but microcontroller, a thermal camera, and some code that does different kinds of operations, see if you can train a classification model at least, or even generate the code running via an image to text llm.
I want to see a cats POV when its startled by a cucumber (Youtube has lots of examples). A theory is that part of the brain mistook it for a snake. Also research on "constant bearing, decreasing range (CBDR)" where drivers may not notice another car/cycle in a perfectly clear crossroads till its too late.'
For something like these kinds of reflexes, my understanding is that the response comes from the central nervous system, even before the brain has had the chance to fully process the input. This shortcut makes one avoid, say, burns or snakes, quicker than if it required the brain. Still, I agree with you that seeing what a cat sees (here or anywhere) would be awesome.
Maybe I missed this, but isn't the underlying concept here big news?
Am I understanding this right? It seems that by reading areas of the brain, a machine can effectively act as a rendering engine with knowledge on colour, brightness etc per pixel based on an image the person is seeing? And AI is being used to help because this method is lossy?
This seems huge, is there other terminology around this I can kagi to understand more?
>And AI is being used to help because this method is lossy?
AI is the method. They put somebody in a brain scanner and flash images on a screen in front of them. Then they train a neural network on the correlations between their brain activity and the known images.
To test it, you display unknown images on the screen and have the neural network predict the image from the brain activity.
Yeah, it's pretty crazy. This seems like it's inputting an image to the Monkeys eyes and then figuring out how that particular input maps to brain activity. Someone would have to fight me here, but with enough input, we should be able to mostly figure out how things map. As in, we can perfect this ...
This is a big jump ethically, but technically it feels like it's a hop away.
If we can do this for visual images, we could use the same strategy on patterns of thought - especially if the person is a skilled at visualisation.
I think it would be interesting to know if the viewer's familiarity with the object informs how accurate the reconstruction is. This shows presumably lab-raised macaques looking at boats and tarantulas and goldfish -- and that's cool. But presumably a macaque especially whose life has been indoors in confinement has no mental concepts for these things, so they're basically seeing still images of unfamiliar objects. If the animal has e.g. some favorite toys, or has eaten a range of foods, do they perceive these things with a higher detail and fidelity?
Using these techniques, never. The electrode methods can only see a tiny section of processing and are missing all the information elsewhere. fMRI is very low resolution. Because of this they are all very overfitted- they cue off very particular subject-specific quirks that will not generalize well.
More importantly, these techniques operate on the V1, V4 and inferior temporal cortex areas of the brain. These areas will fire in response to retina stimulation regardless of what's happening in the rest of your brain. V1 in particular is connected directly to your retinas. While deeper areas may be sympathetically activated by hallucinations etc, they aren't really related to your conception of things. In general if you want to read someone's thoughts you would look elsewhere in the brain.
neonate|10 months ago
Animats|9 months ago
[1] https://archive.is/650Az
vo2maxer|9 months ago
You’re right that fMRI measures blood flow rather than direct neural activity, and the authors acknowledge that limitation. But the study doesn’t treat it as a direct window into brain function. Instead, it proposes a predictive attention mechanism (PAM) that learns to selectively weigh signals from different brain areas, depending on the task of reconstructing perceived images from those signals.
The “thermal imager” analogy might make sense in a different context, but in this case, the model is explicitly designed to deal with those signal differences and works across both modalities. If you’re curious, the paper is available here:
[0] https://www.biorxiv.org/content/10.1101/2024.06.04.596589v2....
buildbot|9 months ago
aitchnyu|9 months ago
explodes|9 months ago
averageRoyalty|10 months ago
Am I understanding this right? It seems that by reading areas of the brain, a machine can effectively act as a rendering engine with knowledge on colour, brightness etc per pixel based on an image the person is seeing? And AI is being used to help because this method is lossy?
This seems huge, is there other terminology around this I can kagi to understand more?
Legend2440|9 months ago
AI is the method. They put somebody in a brain scanner and flash images on a screen in front of them. Then they train a neural network on the correlations between their brain activity and the known images.
To test it, you display unknown images on the screen and have the neural network predict the image from the brain activity.
walterbell|10 months ago
There are startups working on less intrusive (e.g. headset) brain-computer interfaces (BCI).
ivape|9 months ago
ianarchy|9 months ago
EasyMarion|9 months ago
w_for_wumbo|9 months ago
Hoasi|9 months ago
abeppu|9 months ago
smusamashah|9 months ago
https://www.bbc.co.uk/news/science-environment-40131242
https://www.cell.com/cell/fulltext/S0092-8674(17)30538-X
cheschire|10 months ago
hwillis|9 months ago
More importantly, these techniques operate on the V1, V4 and inferior temporal cortex areas of the brain. These areas will fire in response to retina stimulation regardless of what's happening in the rest of your brain. V1 in particular is connected directly to your retinas. While deeper areas may be sympathetically activated by hallucinations etc, they aren't really related to your conception of things. In general if you want to read someone's thoughts you would look elsewhere in the brain.
StefanBatory|9 months ago
gitroom|9 months ago
g0db1t|9 months ago
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