Michael Levin
speaker
450 appearances
1 recordings
1 series
first heard May 2025
last heard May 2025
Michael Levin’s voice in public audio — every appearance, attributed to the second.
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Um, if if you if you have uh a the a model of a pathway or a gene regulatory network, so not the rest of the cell, no evolution, not you know, n nothing, just just a small set of a small set of nodes that are connected to turn each other on and off.
In addition to the learning that we find there,
You can also find something very interesting, which is that if you take metrics from um causal information theory that people are using to apply to brains.
So so people like Giulio Tononi and Eric Hall and others are applying these things to try to understand do I am I looking at a pile of neurons or is there somebody home in there?
You know, is there a person, is there a human patient that's in there?
Right.
So it turns out that um
Not only not only do some of these networks have significant um causal emergence, but that causal emergence goes up after you train them.
So you can calculate it.
And so what happens is for some of them, not for all of them, there are classes.
In fact, there are I I think five distinct personalities that these networks fall into, but for some of them.
If you as you start to train them, and when I say train, I mean you sit you stimulate some of the nodes.
So let's say in a con in a Pavlovian paradigm, you you know you've got your unconditioned stimulus, your condition stimulus, and then there's some other node that's your response node.
And so you start to pair the stimulations and then you see what happens to the response.
So as you do these things, uh, for some of them, um, the causal emergence goes up.
And and so you've got this really interesting loop where uh, by virtue of learning about their environment, in this case being trained, not so much learning more, being trained, uh, they become more integrated as a tiny little self.
They become, right?
They they they acquire this kind of integration.
And for some of these things, what we we looked at uh biological networks versus random networks.
And so so it's very interesting.
Showing 101–120 of 450 · page 6 of 23
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