It reminds of this research where faces monkey's were seeing were recreated almost identically.
AI focused on brain regions recreates what you're looking at (2024)
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Re: AI focused on brain regions recreates what you're looking at (2024)
#12I 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.
I think the distinction you're drawing between "the central nervous system" and "the brain" is mistaken here -- the brain is part of the CNS. This kind of reflex basically has to involve the brain b/c it involves both the visual system and the motor system i.e. there's not a fast path from the retina to moving your appendages etc that doesn't include the brain.
The "fully process" part is part of the story though -- e.g. perhaps some reactions use the dorsal stream based on peripheral vision while ventral stream is still waiting on a saccade and focus to get higher resolution foveal signals. But though these different pathways in the brain operate at different speeds, they're both still very much in the brain.
Re: AI focused on brain regions recreates what you're looking at (2024)
#13I 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?
Re: AI focused on brain regions recreates what you're looking at (2024)
#14The 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.
Re: AI focused on brain regions recreates what you're looking at (2024)
#15Big jump when we go from decoding what you’re seeing to what you’re thinking.
Re: AI focused on brain regions recreates what you're looking at (2024)
#16Maybe 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 und…
>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.
> Then they train a neural network on the correlations between their brain activity and the known images.
Not onto known images, onto latent spaces of existing image networks. The recognition network is getting a very approximate representation which it is then mapping onto latent spaces (which may or may not be equivalent) and then the image network is filling in the blanks.
When you're using single-subject, well-framed images like this they're obviously very predictable. If you showed something unexpected, like a teddy bear with blue skin, the network probably would just show you a normal-ish teddy bear. It's also screwy if it doesn't have a well-correlated input, which is how you get those weird distortions. It will also be very off for things that require precision like seeing the actual outlines of an object, because the network is creating all that detail from nothing.
At least the stuff using a Utah array (a square implanted electrode array) is not transferrable between subjects, and the fmri stuff also might not be transferrable. These models are not able to see enough detail to know what is happening- they only see glimpses of a small section of the process (Utah array) or very vague indirect processes (fmri). They're all very overfitted.
Re: AI focused on brain regions recreates what you're looking at (2024)
#17Earlier quoted context omitted.
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.
I think the distinction you're drawing between "the central nervous system" and "the brain" is mistaken here -- the brain is part of the CNS. This kind of reflex basically has to involve the brain b/c it involves both the visual system and the motor system i.e. there's not a fast path from the retina to moving your appendages etc that doesn't include the brain. The "fully process" part is part of the story though --…
Some touch-based reflexes might avoid the higher parts of the brain though no?
Re: AI focused on brain regions recreates what you're looking at (2024)
#18The 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. [1] https://archive.is/650Az
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.
Re: AI focused on brain regions recreates what you're looking at (2024)
#19I hope one day we can turn this on for coma patients and see if they're dreaming or otherwise processing the world.
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.
Re: AI focused on brain regions recreates what you're looking at (2024)
#20The 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. [1] https://archive.is/650Az
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.
Ah yes, yet another attack vector chip manufacturers will have to protect against now.