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Comment by shahar2k | Hacker News Reader

So if it were possible to read a single neuron in the brain, and optimize input through its' signal would it be similarly possible to extract what triggers that neuron from such a setup?

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Yes, there's a technique called single-unit recording:

https://en.wikipedia.org/wiki/Single-unit_recording

I think it's pretty rare to see an very selective response though, unless you're looking close to sensory neurons.

There's definitely selective responses in single unit recordings, even upstream of sensory neurons in "higher" areas; look for articles on the "Jennifer Aniston cell" and on so-called mirror neurons. The question is, how come you can fool a DNN into classifying noise as something with high probability (as they explain in the article), but you can't fool the brain?

I think it's actually quite common that you can; see for example Pareidolia: https://en.wikipedia.org/wiki/Pareidolia)

This page links to some YouTube examples: http://theness.com/roguesgallery/index.php/skepticism/audio-...

> but you can't fool the brain?

How do you know you can't? No brain of any kind has been scanned and emulated to the point where you could try such a gradient-ascent method.

Have you ever mistaken "color TV static" for a king penguin? If not, then your built in DNN does a good job of discriminating between them. There are optical illusions that mess with our visual system, of course. You could I guess do something like raise a brain in one environment with statistics different from the natural world and then ask how that affects discrimination. Is that what you're getting are with "a gradient ascent method"? Because AFAIK we also don't have any proof the brain uses a gradient ascent algorithm so I'm not sure why you'd ask an in silico brain to carry one out

Gradient ascent is what you use to find the image that tricks the DNN. If you could run repeated experiments on a brain in exactly the same state over and over then you could perform gradient ascent on a brain as well. Whether the result of that hypothetical would be static that tricks the brain is unknown, but I don't see any reason to assume one way or the other. An easier experiment to help the discussion would be to calculate the probability that a random image of static can fool a DNN, rather than a special designed image that appears like noise. If the probability is not vanishingly small then there is indeed something fundamentally different at a functional level between brains and DNN. If not then we have to work harder to answer that question.

Yes, aka the "grandmother cell" hypothesis. I think those studies are interesting, but pretty limited given the number of neurons in a brain and how many you can (ethically) measure...

http://www.nature.com/nature/journal/v435/n7045/abs/nature03...

edit: woops. I mean to respond to GP.

Here is the non-paywalled copy:

http://suns.mit.edu/articles/Nature.pdf

can this be done in a non-invasive way?

There are some non-invasive things like fMRI, fNIRS, and EEG that can measure activity to some extent, but those are all at far coarser resolution than single units.

I wonder if there's a sort of JTAG port into the neural network to do direct scans of the individual neurons.

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