Prof. Russell Hanson on How To Make a Digital Copy of Your Brain - Biohacker's Podcast

Welcome to the Biohackers Podcast. My name is Teemu Aarina, I'm your host, and today's guest is Professor Russell Hanson from Mount Sinai, and he's the CEO of Brain Backup, and he's been looking, looking at how to map the brain and how can we make a digital copy almost of the human brain. And last year at the Biohacker Summit, we had Max Moore, and he's been working on cryogenics, so putting people into cryopreservation with the idea of treating people with terminal— kind of that once you have a terminal illness, the Cryopreservation is a treatment for the terminal condition. Basically, if today's modern medicine can't save you, we can freeze you up and maybe return you back to life later in life. Now, making a copy of your connectome, which Professor Russell Hanson will be discussing in a moment, is another way to preserve what's in here. And we are going to be diving deep into the brain and if it's possible to make that copy and if it's possible maybe in the future to restore that, let's say in a digital format in a physical body and what that would entail.

So welcome to the show, Professor.

Yeah, thank you for having me. It's really nice to chat about the connectome with the biohacker audience. I guess I consider myself a biohacker and I used to be a computer hacker going to like DEF CON and the Chaos Computer Convention in Germany and other hacker conferences. So it's nice that you've provided this biohacker summit.

Right on.

What I find interesting is that Aubrey de Grey, who is basically at the SENS Foundation looking at human longevity. He thinks that aging is a disease that can be cured. And his background is also in computer science. So what's going on here? Why do people with computer science background, why are they interested in life extension and longevity?

Well, I think everybody has their own reason. I think for me personally, I started off as a physicist actually, so I trained as a physicist And in the early 2000s, there was a huge interest in gene sequencing. And so I got into biology through the Human Genome Project and went to graduate school doing gene sequencing. And I'm still doing gene sequencing now in the genetics department at Mount Sinai. So I think that basically, at least for me, I just, I didn't really see what the application was in physics to really continue. Whereas, you know, every day we, it seems there's a new amazing discovery or therapy in biology. And it just seemed so much more interesting to me.

Tell me one thing. So when looking at, when looking at the genome and looking at the brain, right now the sort of dominating metaphor for the brain is the computer metaphor, so processing information, storing information, input, process, output, etc. Is that like exactly accurate? Can we describe the human brain as we are describing a computer?

I think that it sort of depends who you're talking to. I think that there are so many different ways that computer scientists and mathematicians can model systems that it would be— I don't think it's accurate to say that there's no way that a computer scientist or an applied mathematician cannot model the brain. So I would say it's accurate. I think that there's sort of a data gap right now between what would be needed to make an accurate model of the brain and the current data that's available. And so that's what me and many other people are working on is generating the data, making technologies to make the data full enough that we can make an accurate model of the brain at the biological and cellular and kind of information flow level.

What I find interesting is that in the past, during Descartes' time, we were describing, or he was describing the brain as kind of like it's operating through hydraulic pumps. And that was the metaphor during that time. And later on in the Industrial Revolution, there was the ideas that in the human body there are kind of like cogs or gears and machinery going on.

And now we have the computation paradigm that we use to describe what's going on.

And kind of like, I really liked the book from Jaron Lanier, who was one of the pioneers of virtual reality and augmented reality. And he recently wrote a book called I'm Not a Gadget, where he's making the case that biological systems and analog systems when you make a digital representation of them, you are losing something. So can we accurately transcode what is going on in a biological process and make a replica of that in a current information domain?

I believe so. I haven't read the book that you're referring to. There are many people who have many different opinions on this, and I think that their opinions usually are just based on what is it that they believe is the most fundamental piece of information. So for me and my colleagues, what we believe is the most fundamental piece of information is the chemical information and to some extent the electrical information or the electron flow. So the way that we break it up is we break it up into the chemical information being things like proteins and ions and neurotransmitters and the electrical information or the current flow or the ion flow through axons that makes what we call electrophysiology. So the way that neurons spike much. So there are two types of cells that spike. Heart cells have these electrical spikes and neurons have these electrical spikes. And you've certainly seen the way that the heart pumps blood pumps and the heartbeat is an electrical spiking process. Right. So what we believe is that the foundational information is the chemical information. And so the people that I work with and the people who, in my lab, we're all experts in chemicals and measuring chemicals in interesting ways. So the way we measure those chemicals is by bouncing other particles off of them, almost the way that a particle physicist would hit two particles together to see what the results of the particle collision is. What we do is we bounce particles off neurons and proteins and we see where they are and what their structure is. So I think that one of the analogies that I found very useful in kind of understanding the connectome and deciphering the brain is that if you put someone under a deep sedation for surgery, most of the electrical activity in their brain stops. So, you know, the electrical activity stops, but when they wake up, they're still certainly themselves. So they've lost maybe 15 to 20 minutes of their short-term memory, which is what's encoded in all these electrical impulses that are shooting around your brain, but they still have their long-term memories. So the long-term memories are encoded in these relatively slowly changing proteins. Usually they're proteins at the synapse. And so to say that we can't image the brain because it's always in a state of flux is true, but it doesn't take into account that we can actually separate the very fast processes, these electrical processes, from these very slow processes, the chemicals such as the proteins and the cells themselves. So, you know, I'm okay losing 15 to 20 minutes because I, you know, the long-term stuff is what everyone really cares about anyway.

Another question that I want to pose is: when you sleep, what you have learned throughout the day is encoded into long-term memory in the REM sleep phase. So is that basically where you are taking the electric activity and there is some physical changes in the connectome?

To be clear, we're not really interested in measuring the electrical information as much as we are the chemical information. So to us, the connectome is not electrical information, it's chemical long-term information that is, you know, more or less static over a period of 30 to 60 minutes. I think another interesting area for your listeners would actually be this next generation neural interface. So neural interfaces or brain-machine interfaces are these ways of interacting usually with the brain through the electrical impulses. And so the DARPA in the United States, which has funded the creation of the internet back in the late '70s, early '80s, is starting a new project right now that we're involved with called NESDID, or Neural Information Systems Design. And what they're doing is we're— they're putting out about a $60 million grant to do what we've called a neural modem. And so what it is is it simulates 100,000 neurons and reads from a million neurons. And current technology is to read from neurons on the electrical scale are, let's say, from 50 to a couple hundred electrical signals. So to go from 100 to a million is really a huge advance. And the goal of this DARPA program is to do this project in the next 3 to 4 years and have an implant that's available in humans that can interface with motor cortex, visual cortex, auditory cortex with extremely high resolution. And that could almost allow, for lack of a better term, we could call it telepathic communication. So basically very, very high-resolution interfacing directly with the cortex.

Elon Musk is working on the neural lace. Is it related to the same kind of domain.

Could you remind me what the Neural Lace is that you've heard of?

The Neural Lace is some kind of computational implant that is physically attached to the brain that can interface with the internet, for example, so that when you're referring to telepathic communication, The basic idea would be that in long term, I'm just thinking of something and it could be transmitted and I could also receive information that is generated through this neural lace in terms of electric activity in the brain. This is at least how I understand it.

Yeah, I think I know what you mean. I'm not aware of any actual product that has been proposed for the neural lace. There are many, maybe you've heard of the company called Kernel, which was started recently. Have you heard of Kernel?

No, I haven't. Please give us a short summary.

Okay, so in the last couple of weeks, there was a very, to us, innovative company called Kernel that was funded to the tune of $100 million to make an implantable CPU in the human brain. And this is profoundly forward-thinking because there's currently very little that is possible to do in terms of implanting a CPU in the brain of even a mouse or a rat or a monkey. So to say that you are doing a company that makes neural implants that implant CPUs in the human brain is It's a little bit forward-thinking, but at the same time, it's really fascinating. So they're using the same paradigm that we're using, but you know, 15 years ago, it was unthinkable that we could sequence the genome for $1,000. You know, everybody thought it would take $1 billion and years of people's time to do the genome sequencing. And in the end, it really was a rather small team that was able to put together the technologies to do the genome sequencing.

The genome sequencing.

So much like the genome sequence has become something that, you know, on the floor of the building I'm in now, we sequence hundreds of people a week. In the future, you know, using whatever people come up with, it'll be possible to sequence connectomes for hopefully a similar price, you know, maybe something like $1,000. There are many technical challenges to be solved, but there were many technical challenges to be solved with the genome as well.

Right, right. So this kind of accelerating technological change that Ray Kurzweil refers to, also technological singularity or just exponential technologies. Peter Diamond is one of his co-founders at Singularity University, was in Finland recently, and I had a I had the opportunity to have a chat with him and he's working with Greg Venter, who, if I remember correctly, was the first one to sequence the human genome. And he has this company, Human Longevity Inc. And what they're planning to do is to kind of take your genome, take your metabolomics, take your MRI, et cetera, and all these different domains, and then input those in the machinery and to be processed through machine learning. And they aim to figure out like how to expand human lifespans. And he calls this Moonshot Project, one of his Moonshot Projects. So how would you kind of describe where we are right now, you have now $1,000 to sequence a genome. I've done that with 23andMe. I know that it's not the full genome. It's a fraction of that, but it's available. I could even just buy that for a few, a couple of thousand right now and get that genome. Now I can also analyze my microbiome. That was not possible for consumers until recently. And not long time ago, there was only like one person in the world who had his microbiome fully analyzed. So it seems to me that we are going extremely fast to the future and new opportunities are opening up. And you are now talking about Connectome. That we can also map the neural connections in the brain. So when you think of putting all these pieces together, the electric activity in the human nervous system, looking at the genetics, looking at the, the genetic code in the bacteria that lives in our bodies, the microbiome, the skin biome, etc. And all these interactive relations that they have together. So how is that going to solve the problems that we have today in healthcare or things related to human longevity? Or is it really just to augment human capabilities to even take them beyond what it means to be human?

Yeah, it's a great question. It's a question that everybody, that many people are fascinated with, including myself and perhaps you. I think that what we're seeing with this kind of data revolution in the microbiome and the connectome and the genome is really, It's both technical and scientific and it's also sort of humanistic. You know, people want to understand themselves better and it's really amazing that so many methods have been developed that allows people to sequence their, to get their SNPs of their genomes from 23andMe for a few hundred bucks. I also got my 23andMe 23andMe is only maybe 600,000 probes out of the 3 billion in the human genome. So it's a much reduced set of coverage. I'm not an expert in longevity or anti-aging, so I don't think that I'm really qualified to comment on all the things that are happening in that field. I would say that from my perspective as someone who works in genetics, The more we know, the more we will be able to address the problems that arise as people age. And I think that, and that it's certainly accelerating and it's becoming a very technical field of sort of understanding all these changes that occur and doing something about them. I mean, that's kind of what precision medicine is about. One could say that precision medicine or personalized medicine are actually, you know, really, the same thing as longevity, or, you know, by addressing the personal issues that are happening in people's aging or sickness, we're actually working to make them live longer. So it's, you know, I think that humans are destined to find tools to make them live better and healthier, and I don't see any problem with that.

All right, so you are working on brain backups, and right now, if I want to map out what's going on in my brain. If I understood correctly, most of the techniques that we've used in the past are destructive in nature. So I need to slice up the brain and look at each slice and in that way map out what's going on there. Now we can use brain imaging technologies and you may want to refer where we are in terms of that technology to map that out in a non-destructive way. Can you explain to us what's the state of the art? Where are we in terms of brain imaging and mapping out the connectome in a non-invasive way?

Yeah, that's a great question. Thanks for asking. That is indeed the whole impetus behind Brain Backups, was that we were very frustrated with the fact that in order to get the kind of resolution that's needed for a full connectome, you have to use dead tissue. You know, it's a destructive process. So what you do is you take a dead brain, you treat it with some chemicals that make it more amenable to imaging. One particular chemical that we use is called osmium tetroxide, which is an electron microscope staining agent. And then you slice the brain very thin and you image it on an optical microscope or an electron microscope. So, you know, I felt like this was very disappointing that, you know, in order to image the thing that you want to image, you have to destroy it. You know, why would you want to destroy the thing you want to image? It seems inherently wasteful and destructive. So So if you're using electron microscopes or optical microscopes, you will destroy your sample. And most likely, if the organism isn't already dead, you will kill it. And of course, with people, that's not going to happen. It's unethical and it simply will never happen. So then when you move from these destructive methods Over to non-destructive methods, the sets of tools that we have right now are things like MRI, which use big magnets. Another imaging method is called PET, or positron emission tomography, where you can see, you inject this contrast agent, and the contrast agent will show you things like where dopamine is or other different types of neurotransmitters. And then the third kind of non-destructive technique that's commonly used is called computed tomography, which is X-rays. And yet a fourth might be near-infrared. So between magnets, positrons, and different wavelengths of photons, you're left— you have a huge variety of ways that you can image non-destructively and non-invasively through the skull. So the skull and the water are really the things that get in the way. If you try to transmit radio waves from a submarine, the water stops many of the radio waves because of just the nature of water. It just absorbs radio waves really well. Sorry if this is getting a little bit technical, but all these different imaging methods are really important. I think the fifth one is actually pressure waves. Kind of like sonar. So right now there's many people who believe that pressure waves or sonar actually have the needed resolution to image the brain at the submillimeter scale. So— The fire alarm has gone off in my building. I don't know if you can hear it. Yes, I can.

I hope it's not a drill.

Hopefully. All right, it already stopped. [Speaker] Right, so the challenge then is to get submillimeter. So with MRI right now, you can image a millimeter, a couple of millimeters, but that's just not good enough for detailed connectivity of the neurons. So our goal is to image at the micron scale in vivo. And so that's 3 orders of magnitude, 1,000 times higher resolution. And we have some CT machines, or these X-ray machines right now that can image at about 300 nanometers, so sub-half a micron. That is to say, you know, basically the scale we want. The trouble with X-rays is that they actually cause ionizing radiation, which can cause your, um, you know, the water— the waters break apart, though they cause DNA damage. If there's damage on both strands of the the DNA, then DNA repair can't function, and then you have a mutation that is persistent. And that's a big problem because it causes cancer. So the struggle right now with non-destructive, non-invasive imaging is kind of balancing all of these different factors together, from the radiation or the ionizing radiation of X-rays to making a sonar, sensor or transducer that's sensitive enough. Maybe you need to actually do some kind of surgery and open the skull and put a transducer underneath the skull because the skull blocks many of the pressure waves. Maybe you need to put a near-infrared, which is a, you know, infrared actually can pass through up to 10 centimeters of tissue and also skull. But the trouble is, is that it's actually very, The resolution isn't very good. It's like looking through a very, very dense fog. You can see kind of— you've certainly looked through the fog.

Like trying to look through the wall.

Yeah.

Gotcha. So basically putting something in the brain that helps the imaging process.

Yes. And so what we are working on is in this— for CT imaging, we're interested in contrast agents. So for contrast agents allow you to image more of the subtypes of neurons, the physiology of the neuron, the biophysiology, say the neurochemistry of the neuron. So basically by putting a piece of a chemical in the brain that responds to X-rays, you can actually see more than you could without it. Because just like when you get an X-ray of your arm, you mostly see the bone because the bone has calcium and the X-rays bounce off of the calcium. So you can use calcium as a contrast agent. So what we do is we, we put things like gold or iron or calcium or potassium, which are these contrast agents that the X-rays bounce off of, and then we can more precisely image what, what we need to.

Gotcha. So I'm gonna have a golden brain in the future. So, so what's the implication of this? So, so I will be then able to— that, or we could be able to get a more clear picture with less fog on what's going on in your brain in terms of a connectome. Now let's assume we can make that copy and we have that stored somewhere. What can we do with it?

Yeah, it's a great question. For fear of going back to the genome analogy, right now we can get these full genomes of people, but it's the analysis that's really troubling. And so what people do, in these genome studies is they take 2,000 people who have a disease and 2,000 people, see what's different in the sick people, whether they have prostate cancer or brain cancer or lung cancer. And so I think that what's going to happen and what is already happening is that you'll get these case control studies of connectomes of brains. It's already happening with MRI. So in MRI, of course, there are many, many studies of what section of the brain lights up when you do arithmetic or when you do language processing. And as new and better methods to image the brain, the scientists will do their analysis of what part of the brain is involved and what types of connectivities are involved in dementia or Alzheimer's or other schizophrenia, Basically studying of diseases and how—

Right, so mapping different types of brains, one with Alzheimer's disease and the damage that is involved, one of a developing brain of a young child, one of an adult, one of an elderly person, and then mapping out what aging does, what different disease does, and basically running a simulation and analysis on computers then with the copies that we have. Do we have a connectome already? So is it still like a holy grail that is being searched for? Has it been mapped? So where are we?

Yeah, there are a couple of connectomes that have been published. The most commonly cited one is the smallest one and the most complete connectome, which was of the flatworm, the C. elegans worm. So the flatworm, the C. elegans worm, has 302 neurons and, you know, Anyone can download it from the internet right now. It looks like an Excel spreadsheet where every neuron is on every line and it's connected to, neuron 1 is connected to neuron 2 and neuron 1 is an acetylcholine receptor or whatever receiving neuron. And as you move up in complexity through different brain sizes, you get to the fruit fly. So the fruit fly is another very commonly studied organism in biology. And there are some partial connectomes of the fruit fly. And then as you go up from the mouse to the fly to the— sorry, from the worm to the fly, then you get to the mouse and the rat. And so we mostly use mice and rats as a small, simple mammalian brain. And there are sections of the brain for which the connectomes have been somewhat mapped in small— and all right, I forgot one. So another big one is the octopus. So the octopus has a brain that has very huge neurons. And so mapping neurons in an octopus is actually relatively easy. So in fish and octopuses, It's not that hard because the neurons are just huge in many cases.

So basically because of the resolution that we don't have yet, we have been successful in mapping the brains that have larger neurons that you can actually see more clearly. Is that right?

Yes, precisely.

And when we go to mammalians, I mean, we have partial. So does it mean that we don't have a scanner yet that can go through the whole thing, or it's just so difficult right now to get the resolution?

The way that people like to do connectomics in mammalian brains is using electron microscopes, and these electron microscopes are very expensive to run and very slow. And so they're maybe a cubic millimeter of brain. The number's always changing, but we could say, let's say a cubic centimeter of human brain has been mapped at the EM level. There are groups at the ETH Zurich and at Harvard University where very, very small volumes of human brain have been mapped. And what you have to do is you do these slicing images using an electron microscope, and then you stack up the slices again, and then you use a computer program to trace out all the different cell bodies in there. And so very, very small volumes, you know, on the order of a cubic centimeter have been mapped with, I would say, every cell in that volume. And so many groups have been looking at ways to automate the process of doing EM or electron microscope imaging at just much faster and more cheaply. And so they make robots that take those slices of brain and move them over to the microscope and image them and basically just have a robot do the whole process. And so the goal of any of some of these robots is that sometime in the future you could have a warehouse full of brains of people who have died of natural causes and you just let the robots do the whole scanning process and you let the computers do the reconstruction of all the cells and then you have, you move the, the brains from a physical warehouse to a digital warehouse. And it turns out that the slice of a human brain is about the same size as a semiconductor wafer, you know, about this big. And so you can use the same machines that are used in semiconductor industry to kind of move these slices around in a very parallel fashion. The trouble is, right now it's just so, so expensive, you know. Some friends of mine have said they could do a whole human brain at cellular resolution, but it would cost $2 million just in the blades to slice it because the blades would wear out and you have to keep replacing the blades. So, you know, it's really a lot of engineering and kind of figuring out ways to make it cost-effective to do this kind of thing. You know, most people can't afford the $2 million to just by blades. Right on. Right on.

So now if we in the future would be able to map out and I would be able to download my connectome. So once again, like, what can I do with it? Like, okay, we could map it. The scientists could use that to study disease. But do you foresee if we go a little bit further from that? Just to figure out how it works. Is there some kind of like application that we can use? Just like right now when I do map my 23andMe, I can see my disease risk. That could mean that I could use that information to adjust my lifestyle so that I would maybe reduce a risk for a specific thing to occur., or I could use the genetics together with blood work to verify if I have perhaps vitamin D, uh, absorption problems or iron levels, uh, ferritin levels could be higher in my case. So I could be verifying that and, and that could actually mean like practical changes in the way how I live. So if I had that picture of my brain, um, what can I do with it?

Yeah, again, it's a great question. Um, I, when we, when we discuss brain backups with people, oftentimes the question is, you know, what would I do with this, this image of my brain? And, um, and one of the answers I give is, you know, it's, it's your brain, you can do whatever you like with it. And I think that it's, it's a personal question. It's like, it's, you have your body, what do you do with your body? No one's going to tell you what to do with it. So I guess one of the things that I personally first thought that was very interesting or useful was education. So you could image someone's connectome or brain before they went to college or graduate school and after, and then you could see, you know, what is it that they learned in chemical engineering or biological engineering or philosophy. And then you could maybe give someone else that kind of education just by kind of merging the two connectomes. So I think education could be one. You know, the question, what happens when you merge two different connectomes that have different experiences and memories is an open question. No one's tried. It's really pretty out there scientifically or technologically. I think another, someone here in New York told me that maybe a big application for business would be advertising. So maybe instead of going to scientists, right? So neuromarketing, you know, instead of, if you had a neuromodem that was able to, imprint on somebody's brain that they wanted to buy Coca-Cola. You know, that might be something that Coca-Cola really cares about.

So you could basically optimize for— you could optimize your tools so that the brain would take what you want it to take, basically. So let's say the image of Coca-Cola and the affection for it. So what we are talking about here is the time time dimension, so seeing changes in the brain over time. So let's say I could imagine that if I have a scan and then I change drastically the way how I learn, so let's say from reading books to trying to more like orate the topics or practically or physically experiencing them, and then like mapping my brain afterwards, I could maybe make a comparison if that is more effective on the connectome level to induce change. Another idea that comes to my mind is things related to neuroplasticity. So I could be looking at if I'm inducing a change where I would have more resources, specific parts of the brain taking over, Let's say if I'm becoming partly blind or whatever, I might be using different kind of feedback loops to enable my body to experience vision again through different sensory inputs. I could optimize for that. So there's probably many applications that we can then get into once we have the time dimension and you could have like several snapshots of your brain. Not to even mention like a real-time picture over time, which is, I mean, healthcare right now is snapshots. You're born, we take a snapshot of you, you get a disease, we take a snapshot of you. And what really matters is the change that we are inducing and then mapping again and see if we made a change. Now, if we had a real-time picture of what is going on, then that would enable a feedback loop through which we could optimize a hell of a lot of different processes in the way how the organism is interacting with its environment.

Is that right? Yeah, indeed. I think that one of the first things that people think of when they think about optimizing the brain is become smarter, get a better memory. And so indeed, that's what Kernel, the startup I mentioned that's doing the the neural implant is interested in is they want to augment memory and the CPU optimizes how people do what they do and allows this faster iteration loop. Instead of taking written tests with a pen and paper, maybe you could just, you read someone's brain and you see whether they learned it already. Yeah, I guess one other business application that I think has been somewhat interesting is kind of the personal assistant thing. So, you know, if you've ever had an employee or a coworker, sort of the knowledge transfer that allows them to do their job is something that's often very time-consuming. And so if you could use the connectome or the brain image to kind of allow this training process to occur faster, either of a virtual assistant or of a person, then this would be something that would really be advantageous for, um, for a personal assistant, or you might call them like a minion or something. You can have a robot that, you know, instead of— if you could train them much more quickly by just, you know, instantly training them as to what they're supposed to do in their, their task.

Well, I think we have to close soon, but I want to ask one more question. And that would be, now, if we have a copy of the connectome, does it have all the information that we could theoretically boot up the brain in a digital simulation? And I could run a replica of myself in the digital world. And I mean, would it be conscious, etc.? So I guess we have to simulate a lot of other things. Things on top of Connectome to enable an avatar to emerge in a digital realm?

I believe that consciousness is kind of a scare word. It's used to kind of represent, you know, all kinds of things that people interpret as, you know, another human around them. If you break down consciousness into other things like being able to make decisions, being able to recognize patterns and all kinds of things. It seems like consciousness is actually broken relatively— it can be broken down into these different parts that when analyzed separately are more accessible. So I think that the computation that's required to simulate the whole human brain is immense. It's certainly more than any computer on the planet right now can accomplish, but that doesn't mean that it won't be possible in the future. The basic equations of neuron firing are well known. It's just, I think it's joining it with the new data that's coming from the Connectome projects or the Brain Initiative or other companies that will make it more useful.

Right. Are we living in a simulation? I mean, there is now a huge, like, kind of trend in Silicon Valley among the technology elite with the idea that because of laws of nature are following so detailed kind of algorithms, that it could be as well possible that we are kind of being simulated and all of these different connections and everything that we can map out. It's just like someone programmed this and that we, as we map out these things and create a digital replica, and we are working on virtual reality, augmented reality, mixed reality interfaces with artificial intelligence that we ourselves could give birth to a simulation. And so do you believe in that kind of stuff?

I guess as a scientist and a physicist, I don't— I wouldn't believe in something unless I had proof or evidence that that was indeed the case. You know, to say that because we can't find evidence that we're in a simulation, means that we could just be in a really good simulation is nonsensical to me. I mean, if someone could point to some piece of evidence that says, you know, because of this or that, we're in a simulation, sure, you know, I would take that for what it is as a piece of evidence. I think also as a scientist, you know, I think personally it seemed reasonable to me to think that if you keep measuring the world around you in new and different ways and and it's self-consistent in all these different ways that you measure it, that the probability of it being a simulation, when you test it over and over again in all these different ways, is minuscule. So no, I do not believe we're living in a simulation. I have no evidence for that.

Right on. Yeah, I mean, just to note that some of these very wealthy people are now funding scientists to break us out from simulation. We'll see if they will succeed. But at this stage, we are still mapping out, figuring out things. What intrigues me is also in physics, there is the observer effect and the way how we are actually making things appear once we observe them, once we measure them, and how we are also with our instruments and ways of looking at things, we are also influencing than what we are observing. So it's kind of quantum dimension to these things. It's also extremely fascinating topic and we'll see where we're getting, but definitely I see that there's going to be just with like with artificial intelligence, probably broad AI is still far away, but the narrow applications will be phenomenal. And probably the narrow applications of connectome will also be phenomenal for mapping out and understanding disease at the resolution that was not possible before and would probably also invalidate a lot of science that has been done about the brain. I remember reading that in the '80s, et cetera, when the imaging technologies were taking leaps, many of the, a lot of the research theories we had of the brain changed drastically, that we had to have a completely new view of what's going on. And do you foresee that this technology would develop a full understanding of the connectome, we'll be able to mapping that out, that there would be a, like a paradigm shift, almost like a leap in our understanding of the brain that seems to be a black box as we speak?

I don't think it will be a paradigm shift. I think it will be a very slow and gradual process that will, you know, little pieces will crack away one by one. And I think that it will be a big business as well. I think that, you know, many companies have, you know, trillions. There was a study that showed that, you know, a trillion dollars had been pumped into the economy through the human brain. Through the Human Genome Project, you know, so many, and health has improved so dramatically as well. So, you know, there's money to be made in improving people's health, and I think that, you know, mapping the connectome will be a huge area for business as well as science and for people's day-to-day lives.

I mean.

Totally.

Yeah, totally. I mean, thank you very much, Professor. Professor Russell Hanson will be speaking at the Biohacker Summit in Helsinki on 18th of November, and we will be diving deeper into the brain and connectome. And there's also Dr. Julia Shaw coming along who has been looking at memory and how it functions and how it changes as we as we retrieve memories and how it might be an illusion. So if you're listening to this, you might want to check it out. But for now, thank you so much, Professor, and looking forward to a breakthrough in the work that you do and also seeing you in Finland soon.

Yeah, thank you. It's been great to chat and we're really looking forward to coming to Finland.

Finally, if people want to learn more about what you're doing and want to read more, educate themselves more, where should they go? What should they look for? What books, websites, etc., they should go after?

There are so many. I mean, you might look up the Allen Brain Institute has a very immense website that has this, for instance, the map of the The 30-year-old woman where you can zoom in on every single cell in an adult human brain is pretty fascinating. If you look for the Brain Initiative as well, the NIH here in the US has many, many resources. There are so many, it's hard to say.

Awesome. Thank you so much. And, uh, Biocris Podcast. My name is Dave Marna, over and out, and see you next time. Bye-bye.