A Brain Researcher's Bid to Make Digital Twins of Humans

**Andreas Tolias** (0:00)
But now imagine that if I had a way to create a model, a functional model, a digital twin of that system, then if I have enough resources and I buy enough GPUs or enough computers, I could run what would take 10,000 years, I can run it in an hour.

**Ashlee Vance** (0:18)
Simulations.

**Andreas Tolias** (0:19)
Because yeah, exactly, I can just download the video on the Internet and then just show it to these digital tweets.

**Ashlee Vance** (0:26)
It's like a digital representation of the mouse's visual cortex that's watching the movie.

**Andreas Tolias** (0:32)
Exactly.

**Ashlee Vance** (0:47)
Hello, everyone, and welcome to another episode of Core Memory. Our guest today is Andreas Tolias, brain researcher, extraordinary. I'll give you a proper intro here in a second. I just wanted to welcome people to our new podcasting studio, which is Influx. It's going to be even more glorious and beautiful in the weeks to come. But thank you so much for joining us in person in our new digs.

**Andreas Tolias** (1:14)
Yeah, thank you. Thank you for having me. And I'm very excited to be your first podcast.

**Ashlee Vance** (1:18)
You also have the honor of being the first guest on a when I read an ad for the podcast.
We're some very kind people at E1 Ventures, a venture capital firm, have been, are now sponsoring the show. They're a local VC firm, so we're not doing, we're not doing supplements or mattresses like every other podcast. We're going pure capitalism. And there you go. Everyone can check out E1 Ventures. And now, into the show we go. Andreas, I've known you for, I guess, a year, year and a half now. You're down the road at Stanford doing brain research. Before that, I think you did your equivalent of your undergrad studies. At Cambridge, and then at least PhD stuff at MIT. And you are in this area that has always fascinated me, which is, well, you do a lot. But one part of it is this idea of actually understanding how the brain works, trying to map it. And then now, these days, is doing a bit of pairing between neuroscience and AI research, and trying to figure out where these fields meet together.

**Andreas Tolias** (2:42)
Yeah. So basically, the brain is the example of an intelligence system that we have, animals, humans. And we're primarily interested to understand how the brain works, what are the algorithms of intelligence.
And it so happens that in the last 10 years or so, we have another form of intelligence that is also performing and doing very sophisticated tasks, artificial intelligence, that got early inspiration from the brain. You know, like there was many different attempts over the years, over the decades, to build AI systems, you know, symbolic approaches, other approaches in computer science departments, but what has ultimately succeeded in what we have right now as AI, it's artificial neural networks. And what is very fascinating to us in neuroscience is that not only we can use the brain for inspiration, to keep on hopefully developing better AI, safer AI, smarter AI, but also now we can use AI to help us understand the brain. So we can use AI for science. And what is particularly exciting is that we can build models of the brain that are in the same substrate as AI. So we can compare artificial intelligence with natural intelligence. So this is like tight loop from neuroscience to AI, AI to neuroscience. And we hope this loop will accelerate and has both fields in the next decade.

**Ashlee Vance** (4:19)
Yeah, I mean, and this is why I get so excited.
We have, like you said, you have this collision almost of these two things happening, which is new tools and abilities to understand the physical brain as we're racing to make an artificial intelligence. And to your point, I mean, they called these things neural nets from the outset. I used to feel like it was loose and vague at best in terms of representing the brain or being a Kim. But as time has gone on, and we see the complexity of these neural networks, I mean, it feels more apt and realistic to me these days.

**Andreas Tolias** (5:00)
Yeah, definitely. So they are not implementing intelligence at what we call implementation level. They don't have like, you know, they're not built from, you know, the same complexity of neurons that are built in our brain or in animals' brains. They don't have ion channels. They don't have like the biological complexity. But they have, in many ways, they are built by units that essentially integrate information from other units. They have synapses that are plastic, like our brains do. And what is particularly remarkable that has come out in the last few years is this concept of universality. That if you look at artificial neural network and you take different artificial neural networks with different architectures, you train them on similar tasks. They have a lot of similarities in the way they are representing information at this sort of information processing level. So you can think of it like if you can write down, let's say, an algorithm of how to compute something, you can implement it like with biological substrate or with silicon or maybe even other kinds of stuff. But the algorithm, the way it is representing information, is processing information, you can ask the question how similar it is. So what people have found in the last few years is that there's a lot of similarities within networks that have different architectures. And also, we're finding now and others that there's similarities between biological networks, the way they present information that's about fission in particular, and these artificial neural networks. And there's some very nice work, one of the things is called the Platonic Idea. So it's basically like, it seems that the systems are becoming intelligent. They share a lot of similarities, even if it's almost like conversion evolution of some sort. But of course, there are so many differences, and that's where the excitement comes. Like, where are the similarities and where are the differences between these two systems?