A (3:10)

And Ben, can you. So Michael mentioned that the brain is basically an electrical. What did you call it? Electrical device.

B (3:17)

An electrical organ.

A (3:18)

Organ. So talk a little bit. We talked about this on the show a couple of times, but I'd love to hear from your perspective, talk a little bit about how the brain is electric. And is it. Is that sort of reducing the brain's capacity a little bit? For instance, like, if it's just electric, the idea would be that you could basically decode what's going on in the brain. But right now, the brain is so little understood that there's, I think, some conventional wisdom that, you know, part of it is just graduating gray matter that we'll never understand.

C (3:50)

Well, the brain is definitely not just electrical, but thinking of it as an electrical system helps us to interface with it and in some ways to. To heal the brain when it is injured. And so thinking of the brain as. As an electrical system in certain ways is a very useful, extremely useful model for understanding how the brain works, how it communicates with the body, how we can communicate with it from the outside world, and vice versa. So it's not that it's just an electrical system, but it is the electrical nature of the brain. And the electrical nature in which the brain processes information makes it special. It makes it unique kind of among biological systems. And the fact that we have a good understanding of how the brain represents the world and interact with the world through electrical signaling makes it possible to develop brain computer interfaces.

A (4:49)

Okay. And so that kind of brings us to the present where we're starting to see, I would say, rapid advancements of brain computer interfaces going into people's brain. Noland, who we've had on the show, has a brain computer interface developed by Neuralink Co. You co founded, that allows the computer to face basically read the signals in his brain. And when he thinks about moving his arm in one way, the cursor will move in that way. When he does a different thought, he can click. So talk a little bit about you're not quite doing that at Precision Neuroscience yet, but talk a little bit about why this field is advancing as quickly as it is and what this device is that you've brought here today and how it differs.

C (5:33)

Right. So I would say, first of all, one of the reasons that now is such an exciting time in the field of brain computer interfaces is that a couple of companies, Precision Neuralink among them, have advanced to clinical stage, meaning that we're well out of the realm of scientific research and, and on the path to bringing brain computer interfaces into medical reality. And that for many of us who've been interested in this field for a couple of decades, has been an incredible transformation to be a part of, because there's been this notion that what was scientifically known to be scientifically feasible for many years is now squarely on a path to becoming a medical standard of care in a way that's going to help a lot of people who have what were thought to be previously untreatable diseases like paralysis from ALS or stroke or spinal cord injury. So that's one of the reasons that there's so much excitement about the field today, is that this technology is reaching people who need it, like Noland.

B (6:40)

And, you know, when, when Precision was started, there was a dogma in the field that you needed to penetrate into the brain in order to drive powerful function. Like some of what you just mentioned, Nolan's able to do with his device. Precision was founded with a very different philosophy, which is that safety and performance are not in opposition. They are actually self reinforcing. And Precision has now done 40 implants. That's more than the rest of the industry combined in the past two years. And what we are able to achieve in those implants, which so far are temporary, so we have not permanently implanted someone, but we have implanted patients for hours at a time and enabled them to control computer cursors with their thoughts, to control robotic arms with their thoughts. So this is at the cutting edge of performance that has been demonstrated by other systems. And it's achieved without the requirement for, you know, puncturing brain neural tissue and doing some damage to neural tissue that other systems require.

A (7:44)

Okay, for listeners and viewers, I just want to say that there is going to come a moment in this conversation where we're going to take it from Today to where this goes in the future. I'm talking about treating depression, I'm talking about treating stroke and coma. I really think that is worth sticking around and, and joining us for the second half. But for those who want to understand the basics of this technology, we're going to do that now just for a bit. But this is definitely one of those episodes where it's worth staying with us and, and talking about maybe there's a way that, that can end up helping build better AI models through the brain or understanding the brain through AI. So that's all coming up. But Michael, you mentioned that this is a non invasive device. I'm going to kind of not listen to your instructions and just kind of hold it up, see it. This is, this is the device. And there's 1024 electrodes here for those who are listening. It is paper thin or less than paper thin. And the electrodes are kind of come up on a, on, on a head at the top here. Pretty amazing. And the, the thing that you mentioned about it not being invasive. So the history of this technology has basically been that if you want to get a read or on a brain signal, you need to put some like hair, like follicles or some prongs into the brain. But this lays on top of the brain.

B (9:09)

Yeah, that's right. Just to describe the device in a little bit more detail, it's actually thinner than a piece of Scotch tape. So incredibly thin and very conformal to the brain. And it's designed to sit on the surface of the brain without puncturing or damaging neural tissue. The system has 1024 tiny platinum electrodes which are basically sensors. And those, those electrodes are. We manufacture this system using photolithography. So it's the same technique as semiconductor chips. This is really the first example of cutting edge manufacturing techniques being applied to medical technology. We actually own the fab in which we do it. There is no domestic supply chain that's capable of this sort of work. But what we are able to achieve with sort of a super high resolution system that sits on the surface of the brain is really to record the awake activity of the brain at a resolution that's never before been seen and never before been achieved. And just going back to your question earlier, you know, the brain is an electrical organ, meaning that when we have thoughts, there is actually a physical manifestation of those thoughts. So we intend to move our hands or our fingers or we recall an idea. There is actually physical manifestation for that and it's electrical. And what this system is able to do is measure to record that electrical activity in a way that's never before been possible.

A (10:39)

And so Nolan's device has 1024 electrodes on it. So same as this, but you have at one time inserted as many as four of these in a brain. So that's given you much greater signal than that, just one neuralink device.

C (10:54)

So the part of the philosophy of precision was to make, is to make the technology highly scalable across a number of domains. And I think this will probably be a theme of the conversation as we take it from where the technology is today to where we might go in the future, into other disease states, or how this technology informs the development of artificial intelligence. But as you said, what you're, what you were holding in your hand just a moment ago is one module that contains 1024 electrodes. But we have the capability of deploying, because it's non damaging to the brain, multiple of these modules onto the brain simultaneously. And we've done that actually many times in quite a few patients. We've used at least two of these simultaneously, providing 2048 electrodes on the, on the surface of the human brain. And in one, in one particular case, we used 4096 electrodes for modules. And there's not really a.

A (11:52)

What does that greater number of signals give you?

C (11:54)

Well, really the important, it provides us a more detailed picture and a more complete picture of what the brain is doing at any given time. Because I think one of the important things to, to realize about the brain, at least the parts of the brain that we think of as being most relevant, is that almost all of our conscious experience, whether that is movement or sensation or vision or sort of decision making or memory, all happens basically at the surface of the brain. We call that the cortex. I think that's not an intuition that everybody has because we think of the brain as a three dimensional structure which it is living inside the head. And so people think of it as, as kind of all of the functions of the brain. People have this intuition that are, they're kind of uniformly distributed in this, in this, you know, 1500 grams of tissue. But actually they're. That all that processing is not uniformly distributed within that volume. It's almost all very, very close to the surface. That's crazy.

A (12:59)

What's deep in the brain then?

C (13:00)

Well, most of what's deep in the brain is wiring. Not, not all of it, but, but I would say most of what's deep to the surface is the wiring that connects the cortical surface to the rest of the body. And then there are areas like for example, the basal ganglia or the cerebellum or the brainstem that primarily subserve non conscious functions. So the smoothing out or the learning, unconscious learning of movements, breathing, vital, you know, vital functions that we don't think of as part of our conscious experience. So most of conscious human experience is happening very close to the surface. And that certainly includes what we're focused on now, which is movement. And most of the way we interact with the digital world and the physical world is in some way through movement or intended movement. So the fact that we're focused and the brain computer interface world today is focused on paralysis as a target disease state is not really an accident. One of the reasons for that is, first of all, that is a, is a pathway within the brain that is extremely well understood. How we, how the, how the brain goes from what's happening at the level of intention to move and active activity on the brain surface to activation of the muscles is one of the best studied and most well understood neurological systems. So interfacing with it is, is kind of a natural thing to do. But also, if you just think about the way we live our lives in, in 2025, you know, you're sitting here with a laptop in front of you, and you interact with that laptop through touching the keys and moving the cursor on the screen. And so those are volitional movements and they happen in actually a very small piece of real estate on the, on the surface of your cortex that's only a few square centimeters in size. And so to get back to your original question of what does that do for us, the ability to put a few square centimeters of high density electrodes on the brain surface. It allows us to interface with the entire extent of your desire and planned actions into the outside world. So the, the key in, in brain computer interfaces as they exist today, the key to restoring or augmenting, you know, an interface with the digital world, between the brain and the digital world is to get sensors onto the area of the brain that are responsible for those interactions with the outside world, to span the entire extent of that real estate and to do it at a level of resolution that is appropriate to the brain's intrinsic signal processing capability, and yet to do it without damaging that part of the brain at all. And that's really what we've, what we've done at precision is we've actually people, when we started the company didn't think that this was possible. As Michael mentioned, there was a dogma that said that in order to interface in a reliable way, you actually had to penetrate into the brain with these needle like electrodes. And I think part of that dogma in some ways stemmed from a mistaken intuition that, that there was, you know, a need to unlock information deep within the brain, so called deep within the brain, in order to do that. But in, in reality, enough information is represented right at the cortical surface that if you build the sensors with a high enough resolution as we have, and you span the appropriate amount of cortical real estate, you can get incredibly high fidelity function out of these, out of these interfaces. And actually we, you know, we've begun to show that, I think in ways that are surprising even to the experts. So that has been an incredibly exciting development for us over the last couple of years, especially this year.

A (16:42)

Okay, what, what surprising ways? Because you're both smiling about the surprising applications.

C (16:47)

Yeah, well, I mean, you know, you referenced cursor control and that kind of interaction with the, with the digital world as kind of a benchmark for function in brain computer interfaces, which it is. It's not actually, it's not trivial to control a cursor in multiple dimensions in real time. And yet we've begun to be able to do that in, in ways that are incredibly reliable and in ways that patients can learn to achieve cursor control actually within just a couple of minutes of, of training data.

A (17:23)

And you don't have the threat of thread retraction, which is what no one has experienced because there are no threads in the brain, correct?

B (17:29)

Yeah, exactly. And I think, you know, one other way to answer your earlier question. Why does more resolution matter? Why does a higher electrode count matter? I think one analogy that we sometimes talk about is that BCI is at core, it's a communication device. And if you think about it in that way, the bandwidth of a communication device is really what determines how it can function. So as an example, a 56k modem is capable of chat, whereas a fiber optic connection enables Netflix. It's the same thing with bci, where if you have a very low resolution system, you can do very basic things, let's say, like click on a computer mouse. Our ambition is far greater than that. So what we are going to enable in people who are right now paralyzed is seamless control of computers and smartphones and other digital devices in the way that we all frankly take for granted every day. So think about, you know, rich video games, productivity suites like Microsoft Office and Google suites, that sort of level of functionality which we think is really going to be life changing for people who today have very little therapy available to them is only possible with a system that has a high degree of bandwidth.

A (18:45)

So they'll be able to do this just entirely with their thoughts.

C (18:47)

That's right, exactly. And I would emphasize also that, you know, the component that you were holding up that's here on the desk in front of you, is only a part of the brain computer interface system. So these electrodes form the sensor that touches the brain, kind of caresses the surface of the brain. But of course, they're connected to implantable microelectronics that amplify, digitize, record, compress the data and all of that data. And this will probably take us into some of the other areas that you wanted to touch on, that you alluded to in the beginning. That data all has to be processed. So it's artificial intelligence that allows us to, in real time, decode the electrical information from the brain surface into meaningful command and control signals for a digital interface. The signals don't come off the brain in a way that tells us how to interpret them. Actually, there's a kind of translation process that needs to happen in real time.

A (19:42)

Okay, so one more question about the device itself. Then we move into the more futuristic and theoretical questions. Why haven't you left it in? Because if this is not damaging the brain, we know that the other devices have been left in. Nolan is kind of moving about the world with a neuralink in his brain right now. But this is something that has only been used in circumstances of, like, when the brain is already open to understand, to try it out or understand a little bit more about surgery. So why haven't you left it in yet?

C (20:12)

We will be leaving it in is the short answer to your question. And the reason is that we've taken a slightly different approach to development, which is to emphasize to make sure that what we've developed is safe and highly functional before beginning permanent implants. And so, for us, it's been incredibly important to ensure that the interface works and delivers a level of functionality that we think is essential to guarantee to patients before we start leaving the devices in. So we therefore pursued a strategy that was sort of a phased development approach. And so in our first 40 patients, those were temporary implants that were designed to validate that the quality of the signals and the ability to decode those signals in real time. We then, you know, we now actually are the first modern brain computer interface company to have an FDA clearance. So the version of the electrode that you were holding in your hand Actually has now FDA clearance. So among the current leading BCI companies, we're the only company that has a full clearance from the fda. And as part of leveraging that clearance, we're moving to a next phase in our clinical studies that will allow the system to be left in place for up to 30 days. And that phase will help us further validate the quality of our, of our decoding algorithms and deeply understand the nature of the signals as these devices are left in place. As you alluded to, you know, others have had some problems in their early, in their early after implantation. And what we're trying to do is to anticipate and avoid such issues as we move to permanent implants. And then it will be after that phase, after this next phase, that we, that we start leaving the devices in patients as part of a clinical trial permanently.

B (22:14)

And just, just for context, you know, I think the fact that we even have this path available to us is a testament to the underlying safety of the system. So we did our first patient implant within two years of founding. You know, it took Neuralink eight years. It's taken other competitors even longer than that. And I think that that's, you know, one a product of just the focus and the design philosophy that Ben just articulated. But it also is a feature of the fact that our system is reversible, that it doesn't damage the neural tissue, that you can remove it. And there's been no harm to patient. So I think that that just speaks to, you know, some of the underlying characteristics which we think are important today and will be very important in the future for, for clinical adoption.

A (22:59)

So there's two timelines here. One is this timeline that it's going to take you to be able to get this current device implanted full time in people's brains or on top of people's brains to be more accurate and, and then read their brain signals in the motor cortex. I'm going to be really unfair. I know that that's going to take a tremendous amount of work. And if your company does that successfully, it's probably a good, good outcome for your be unfair and ask you now where the technology can move to after that. Because Ben, you mentioned that the most of what we do, most of our, our signals of what, what happens in our brain happens at the surface. So what happens if you expand beyond the motor cortex? Where else could this technology go? I know Elon Musk and neuralink are currently working on eyesight, which even if you are born completely without eyes, could potentially take signal from the world around, and then beam it into the visual cortex so that maybe it might be one application. Where else should.

C (23:56)

It's a great question, and we definitely are thinking about this. And it actually dovetails with the prior question, which is, you know, what are we learning as we bring this technology in its current stage into the world and as we work with patients and physicians across the country, even in the early clinical studies? And part of. Part of that experience for us has been a process of discovery. You know, when you bring a technology that you've been developing into the real world and you put it into the hands of, you know, of people with lived experience and experienced, insightful practitioners, you learn all kinds of things that you might not. That you might not anticipate. And so actually, even though we're focusing on applications in the motor cortex, as part of these studies, our electrodes have been placed actually all over the brain. They've been placed in sensory cortex, in prefrontal cortex, areas which are responsible for decision making. They've been placed in the spinal cord and on the brainstem. And so I would say that at this point, it's very exciting for us because we have an incredibly large, rich data set that's probably just absolutely unique in the world now as far as regions of the brain that we've interacted with and things that we're thinking about potentially doing in the future. So just that gives you a sense of what kind of data we're starting to experience. And in practical terms, you know, we think that probably stroke represents the next expansion of the market for brain computer interfaces. So right now, the disease states that we're designing the interfaces to treat are really forms of severe paralysis, really forms primarily of quadriplegia that result from spinal cord injury in the neck or brainstem stroke, or diseases like als, neurodegenerative diseases like als. And those are very severe forms of paralysis that are either complete or near complete and leave people either mostly or completely unable to use their hands. There are other forms of paralysis which are more common, and those arise, for example, from common forms of stroke. Stroke affects almost a million patients in the United States, almost a million people in the United States per year, and about a third of those, so several hundred thousand people recover from their stroke with a persistent deficit that leaves them either having difficulty articulating speech or difficulty using their hands or difficulty walking. And those people, although they may not be completely paralyzed, live with a severe deficit or. Or disability in their ability to interact with the world just because of their. Of their paralysis. And so we believe that, that, that is a next step for brain computer interfaces in the medical world. That's what, that's what physicians with experience are asking for, and that's what patients with stroke are asking for. And it also is, we think, very medically and scientifically feasible.

A (27:12)

And when you apply it to stroke patients, is it that you are able to decode what they want to say and then help them say it, or is it if they lost some movement, actually using electrical signals to help them move again?

C (27:28)

So we're still talking, we're still talking about a function that is decoding intention and expressing that through digital means. So, for example, somebody who, you know, who has a stroke on the, you know, on one side of their brain and can't move a hand, for example, or can't move it well enough to type, you know, that that kind of deficit, which is debilitating for people who are trying to return to work, especially if it's in the dominant hand, for example, that kind of deficit could be augmented by a direct thought to digital world communication. Does that make sense? We're not talking about yet stimulating the brain in a way that restores the hand back to normal or that, you know, provides an orthosis over the hand that moves the hand again. I do think that will come, and we're already talking to partner companies to do that kind of a thing. But the therapy from brain computer interfaces is primarily something that kind of reads brain activity in real time and establishes, you know, intuitive, smooth communication with the digital world.

B (28:32)

And I think that helps hopefully give an intuition for how this industry is going to develop in coming years. So, so we're starting with very severely paralyzed people. There are about 400,000 people who have no use of their arms and hands. And so for them being able to control a computer, a smartphone, a tablet, it's going to be life changing. And we think that that's a sort of two and a half billion dollar. But as Ben mentioned, there are other issues that are much more common. Stroke being one of the principal ones, which is why the number of people who have some sort of motor deficit, maybe not complete inability to move arms and hands, but some deficit, is about 12 times that number. So, so many millions of people in the United States alone, and allowing them to interact with the digital world in a seamless way, we think is also going to be really transformative. It's going to start with the most severely impact patients and then move. But in terms of restoring movement, you know, what you really need is basically a partner device to help with either stimulating peripheral nerves or creating a prosthetic. The way that we think that brain computer interfaces are going to develop over time is that we think that the neural data is the hardest to access. It's the hardest to extract really. And as a result, we sort of think about it as like the operating system. There will be other products and devices that plug into the data that we're able to provide to enhance people's lives in different ways. Think about it like an API. But I think the companies that are able to record at high fidelity and then transmit the neural data are going to be able to create an ecosystem around them to do all sorts of things that are right now not possible.

C (30:22)

Yeah, and just to be clear, we've already had inbound from quite a few.

A (30:25)

Okay, so yeah, before we go to break, let me like throw out like one example. Like potentially you get, you could be reading signals of depression on someone's brain and then maybe some, some ancillary company can use that API data to like stimulate the part of the brain that have, that has a deficit of some electrical signals as a potential cure.

B (30:46)

That, I mean, that's, that's already starting in, in academic research where there's sort of closed loop systems that are helping people who have refractory depression that, you know, doesn't respond well to medication and which is very severe. With systems that, you know, right now we have technologies like electroconvulsive therapy, which are very coarse, which, you know, effectively, you know, incite a seizure in people. But it does have efficacy, which is why it's still used. But if you think about, imagine being.

A (31:15)

Targeted on this stuff as opposed to just using brute electricity.

B (31:17)

Exactly. And I think we're headed to that.

C (31:19)

You know, you raise a good, a good point and an application space that we've thought a lot about and that many people think about. And I think kind of what you're alluding to is this concept of a digital biomarker. And it's something that we can do with the precision system that's very difficult to do with a system of penetrating type electrodes for, for a number of reasons.

A (31:38)

But what's a digital biomarker?

C (31:40)

A digital, digital biomarker is kind of like, it's kind of like a, a signal, but it's instead of, instead of a molecular readout that like you get with a, a blood test, it's a, it's a digital signal that you get by electrically reading the brain. So think about, if you, if you were to place the, you Know the electric precision electrode array over a relevant region of the brain that's relevant to depression. And some of those are already known and well established. So in a patient who's prone to depression, you might see, and this, there's already very good preliminary data to suggest that this is the case. You might see that when there is beginning to enter a relapse of their depression, there are particular digital signatures that occur in that area of the brain and that are predictive of them entering a relapse. And that can be life saving for people, you know, who may become suicidal due to severe refractory depression. So being able to predict those periods before they happen and either deliver stimulation or change their medications or alert, you know, alert care team that that is in the process of happening. And that kind of concept actually is common across a number of important disease states, including epilepsy and depression and, and others. So this is a direction that, you know, that we thought a lot about, that we've had good, good discussions with, you know, the industry about. And it's actually not that dissimilar from some of the molecular studies that people do nowadays. You know, like in the era of gene sequencing, you know, low cost gene sequencing has enabled many people to do, to learn a lot about their own biology and predict things about them that they wouldn't have already known about. So it may be that, you know, one direction for neurotechnology is something like this.

A (33:30)

Go ahead.

B (33:31)

No, I think Ben's getting at a really important theme that we talk about a lot in the company, which is, you know, why has there been more progress in other areas of biology than there has in neurology? And one of, one of the reasons, I think, as Ben is alluding to, is that we've been able to digitize biology in other areas. Genomics revolution being a perfect example of that. And that has led to a number of breakthroughs and very rapid progress today. It's been tough to convert the neural signals of the brain into something that we can apply compute to the brain is hard to access. You know, Ben knows this better than we do. It's hard to get there. And once you do.

A (34:13)

Good design, keeping that thing protected.

B (34:15)

That's right. The skull does serve a purpose. And, you know, the biology once you get there is sort of this, this mushy mass. And so figuring out a way to effectively digitize this biological system in a way that is scalable, which again, I think, you know, the fact that our system is scalable across different areas of the brain at the Same time at super high resolution. This is something that we think is going to end up unlocking a number of breakthroughs, which, frankly, today are hard to predict.

C (34:47)

Yeah, I know you wanted to get to where does this take us in the world of AI and foundation models and modern machine learning? And how does that connect to what's happening in brain computer interfaces? And I think this is a good segue for that and just a bit of intuition that I would want to provide. This is something special about the precision system because the electrodes that you were holding in your hand a minute ago, they form a regular lattice and they have a spatial relationship with one another that's kind of like the spatial relationship among pixels on an, on a screen. It's the same every time. And so when those are placed onto the brain of one patient, the data format that they read out is the same data format and has structural elements that are the same from patient to patient. So it brings commonalities across patients that we've studied into very sharp focus. And that has been a major limitation of the ways that we've interfaced with the brain for generations, including with the penetrating electrodes that are used by other systems. Because when you penetrate into the brain using needle like electrodes, the spatial relationship of what you're recording from is kind of a little bit random.

A (36:05)

Right.

C (36:06)

And so that relationship is not preserved from patient to patient, and it makes it very difficult to apply learnings from one patient to the next patient. In, in the precision system, one of the inherent advantages is that the data is so regular in structure that we're able to compress it. We're able to learn across patients, across populations, and leverage those learnings in the machine learning algorithms that we develop, and that we have found to be just a, you know, a massive advantage.

A (36:36)

I want to take it one level deeper and ask about decoding full brain activity and making the brain less mysterious, but why don't we do that right after this? So let's take a break and come right back. We're back here on Big Technology Podcast with Michael Major and Ben Rapoport, the co founders of Precision Neuroscience. Before the break, we talked a little bit about unraveling the mysteries of the brain through electrical signals. And at the moment, we could all agree the brain is a mystery, even though the technology companies like yours are getting better at decoding parts of it and, you know, being able to do something with that information. But do you ever anticipate a moment where the, the totality, like I started this show saying, could we Build a foundational model for the brain. And I guess that was like a way of saying, can we find a moment where the totality of everything happening in the brain is decoded by technology? And if that happens, what does that lead to?

C (37:34)

We talk about this a lot. This is sort of in the zeitgeist right now in the tech world, this question of the whole brain interface. And I'm happy to discuss it, we have kind of our own view of what it means to have a whole brain interface. And I think understanding what that implies requires a few things. One is that as we mentioned earlier, the distribution of information through the brain is not uniform. There are areas of the brain that are much more relevant to our interaction with the world than others. So most of the brain is. Is actually not relevant for communicating with the outside world or with artificial intelligence. Most of the brain is taking care of the body and not in ways that are particularly relevant to interfacing with the outside world.

A (38:32)

Does that mean like controlling like blood flow and stuff like that? Does that happen in the brain?

C (38:35)

Let's call it our. Our vital functions and unconscious functions and things like that.

A (38:39)

So that's all being directed by the brain?

C (38:41)

A lot of it is, and not all of it is, but some. Some of it is. And certainly because I guess like we.

A (38:45)

Think that the heart beats and the blood goes, and that has nothing to do with the brain, but the brain really is the command center for a lot of this stuff.

C (38:51)

The brain is. Is involved. It's not necessarily doing every thing moment to moment. Certainly the heart has its own intrinsic ability to function, but the brain is heavily involved in a lot of what goes on in the body. And. But my point is just that. And also a lot of the brain, as we mentioned before, is white matter connections between, you know, between different parts of the brain and from the brain to, for example, the spinal cord. And those are incredibly important for how the brain functions in a biological system. But with respect to interfacing with the outside world, it's. Those are much less relevant when you think about a whole brain, what we think about as a whole brain interface intuitively. So that's. I want to start with that, that even though the brain, there's this notion that you want to have a whole brain interface. Actually, what I think we really mean by that is we want to be able to interface with the parts of the brain that are responsible for our conscious interaction with the world. And that actually is a spatially limited portions of the brain.

A (39:58)

But isn't that relatively unambitious I mean, I know it's very ambitious, but I, this is easy for me to ask, but I'm saying that like to me the thought is what about taking my memories and kind of dragging them from.

C (40:08)

The brain onto a computer? I totally get that. Right. And so the point I'm trying to make is that your memories have a spatial location within the brain for, you know, to, to a significant extent. And so the problem of how to do that is not the same as how do we record the continuous state of every single cell in the, you know, 1500 grams of the brain. I think it's very important to understand the brain is a physical thing. It has structure. And how we think about interfacing with the brain begins in part with, with understanding that there are some parts that are more relevant to forming that interface than others. And then how do we get there and how do we listen in or interact or interact with those areas of the brain? So as we mentioned earlier in the, in the conversation, we're really focused right now on movement related areas, the so called motor cortex. Those. That's an area that's very, very salient to our interactions with the, with the outside world in moment to moment. But you know, the vision related areas and sensation related areas and hearing related areas and memory related areas and decision making areas, these are all surfaces within the brain that if you want to build an interface that, that encompasses all of those functions, you have to touch those surfaces. So in, in some way can we.

A (41:28)

Talk about two of those? Memory and decisions. So do you anticipate at one point the science might, or your companies like yours might be able to effectively go in and download our memories or maybe go in and like, you know, decision scientists would have a field day with this, like basically decode how we make decisions just by reading the signals off the brain.

C (41:51)

I think from the standpoint of those, those are two very different problems. From the standpoint of decision making, I think the answer is much closer to yes. And we have an understanding of how that sort of thing might happen. From the standpoint of memory, it's a little bit of, a little bit different. And we can take those two questions, you know, one at a time. I'll just say that from memory it's important to understand that, that the way the brain stores memory is very different from the way we think of memory being stored in the digital world.

A (42:24)

Yeah, this is important.

C (42:25)

Yeah, I think this is important to understand that memory as it's stored in the digital world, really the bits have a physical manifestation that on some level you could, you interrogate them. Right. When you, when you store a bit, it's a spin state or, you know, or something like that. And so the reading of those memories is really the reading of the state of something that, you know, at a very, very small physical level has a representation in the physical world.

A (42:59)

Yeah. Click on a file, it will go into the sort of semiconductor mainframe and then access where that file has lived.

C (43:05)

Lived. Right. A transistor, transistor state is changed. Right. You know, or a spin state is changed or some, something in solid state is actually, is changed to represent the bits. So when we think about, you know, people talk about bits and atoms as being, you know, intrinsically linked, they, they are. Right. There is, there is a really a one to one representation between a particular bit that you're trying to store and some state in the physical world.

A (43:32)

So how's the brain different?

C (43:33)

The brain is different because, and this really relates to how. One of the, one of the ways that neuroscience has been so important in motivating developments in artificial intelligence is that the way that a particular memory is stored is not really in the flipping of a state. In order to read out a memory from the brain, you have to stimulate the network. What we think of the network as of the network that represents that collective, you know, memory. And you have to stimulate it with something that triggers the recollection and the network then either completes or reproduces that. Right. So if as an example, you know, you, you can be reminded of a memory by, by a particular trigger. Right. Or you can, you can trigger yourself to remember something. Right. But there's no scan that you can do or that we can do that looks into the brain and says there is the face of your family or there is the, you know, the, the combination of your combination lock.

A (44:39)

Do you see the difference? Weird question then, like, where do the memories live when they're not there?

C (44:44)

They, they, they, they don't really exist as such. Wow. They, they, they. The brain is a system that, that can produce the recall of the memories when appropriately triggered. But it's not like somebody by reading the physical state of the brain with a scan the way you could, a disk drive, for example, can, can find all of those bits of information. Does that make sense? It's like, it's very different.

A (45:13)

Where do they exist? So they just don't exist.

C (45:15)

It's not that they don't exist, but this, the storage mechanism is very different. It's like saying that in order to, in order to get the, in order to retrieve the memory, you need to trigger it. Right. So in order to, in order to get the memory of the combination lock out of your brain, you need to basically say, what's the combination to your combination lock? And then you'll retrieve the answer. Right. But it's much more difficult to, like there's no file address that we know to go to in your brain that contains the digits of the combination lock.

A (45:45)

Knowing what you know about the technology, do you think we'll eventually find that filing cabinet in the brain where all this lives?

C (45:52)

I don't. What I'm trying to say is that it doesn't exist as such to the best of our understanding of how the brain works.

A (45:58)

So then how could a technology company then go and basically stimulate the brain to share memories? Maybe there's a way that, you know, I'd like to, for instance, like relive a memory. Or maybe, you know, there's something that my father told me 20 years ago that I forgot, but I really want to remember what he said word for word. Can technology one day be able be used to stimulate that and then recall those memories?

C (46:25)

I don't think, I don't think we know the. I think we're far from knowing the answer to this. I think. Well, let's, let's put it this way. It's known that by electrically stimulating the brain, you can reproduce certain memories. But it's much at this stage of our understanding of neuroscience, the predictability of how we do that is really not well understood. Okay.

B (46:45)

And, but one sort of way that I think we think about some of the questions that you're getting at is that we've just never had the tools before in neuroscience to interrogate some of these questions. So we've never had as high resolution a picture of the awake human brain as we now have with the precision system. So, you know, the electrodes that I mentioned earlier, the 1024 tiny platinum electrodes, most of them are 50 microns in diameter, which is actually the size of a neuron.

C (47:14)

Right.

B (47:15)

And we're starting with a postage stamp sized electrode array over motor cortex. But the medium term vision for precision is a much larger electrode array covering much greater areas of the brain's cortex with hundreds of thousands, someday millions of electrodes. And I think once we're able to achieve that and apply, you know, the cutting edge compute algorithms that are available today, we're going to learn answers to questions that right now we just haven't been able to interrogate properly.

C (47:52)

I think that kind of interface will allow us to fluidly interact with the kinds of digital memory that we kind of have more of an intuition for in, you know, in our daily interaction with technology. And certainly many of the ways that we think of expanding our memory type capability is in that kind of file system storage way with addresses. So, you know, certainly we can think of ways and the way we interact with those memories are with the kinds of brain computer interface type technology that we're already talking about. Right. So I definitely foresee a future in which we can, you know, a near future in which we can augment, you know, memories through that kind of fluid interaction.

A (48:36)

So we could have like an external hard drive that the BCI we kind.

C (48:40)

Of already have that, you know, with, with assistive technology today. I mean you carry it in your pocket instead of in the cloud, but.

A (48:47)

A direct link between a device and.

C (48:49)

The brain would be lost. I think that, I think that, that, that that's where we are already building towards, you know, a state of brain computer interface technology that will facilitate that. No question about that.

B (49:00)

And I think that that's important because it's based on just an extension of what we're already doing. You know, right now we're already cyborgs in some way. We have this digital black box at the end of our hands that we tap on furiously and that's how we access, you know, our external hard drives. And in the way, you know, whether it's, you know, a Dropbox in the cloud or Wikipedia or whatever. You know, I think the evolution of the way that human beings are able to control technology and compute has changed. It's changed repeatedly over the course of the past 40 or 50 years from mainframe computers where we had like punch cards, to desktops and laptops where, you know, we use our hands to control a keyboard or a mouse. Now it's mostly phones. Increasingly, I think this is moving to wearables. Think about AirPods that people forget they even have in and you know, makes them sort of quasi cyborgs. I think over time it is likely that a more seamless, a more intuitive way for us to engage with the digital world is, is, is going to happen. And I think this concept of sort of thought based control of computers, which right now still sounds like science fiction, even though we're doing it in clin the country and even though it's been done in academia for a couple decades, it still sounds sort of fantastical. But, but the, the moment for real clinical adoption is very near.

C (50:28)

Right.

B (50:28)

And this concept of, of seeing people control computers with Their thoughts is going to become much less amazing and much more commonplace. And I think as that happens, our, our attitudes towards how to control computers is going to change.

A (50:41)

What about studying decision making?

C (50:43)

Yeah, so I think decision making, we picked, we picked the more difficult one.

A (50:47)

First because I think, I think memory.

C (50:50)

Is more just, I think memory, memory is a little more difficult. Decision making, I think is, we're already doing that in, in many ways. You know, I think we have a much better understanding of certain aspects of decision making because a lot of that relates to the pre planning of speech and motor function. And so we have a better, a better lens on that now. And a lot already is understood about the neural systems that serve decision making. So predicting decisions before they happen, at least at the fraction of a second level, is already possible. And we have already some understanding of the areas of the brain that are responsible for that. So both decision support and decision prediction is kind of already in the near term. In the near term, roadmap stock traders.

A (51:35)

Are going to have a field day with that.

B (51:38)

And by the way, I think that that has the pot actually to be influential in some of the more cutting edge foundational models that are being used or they're being developed in the area of artificial intelligence today. So interestingly, a lot of AI has been inspired by the architecture of the brain. Neural nets are where we've started. And some of these more abstract ways of decision making are likely to help with breakthroughs towards sort of the next stage of generative AI. And that's why you're starting to see some convergence of the pure software players with folks like us who are interested, who are actually developing hardware that interacts with biology.

A (52:23)

What about decoding states of consciousness? So we talked, so we've talked a couple times now before we've done this episode, just so I can try to wrap my head around what you guys are doing. And one of the things we spoke about was coma, as right now coma, I think is very little. It's minimally understood. People just sort of say, okay, they're laying there in kind of like a half sleep. But what can, for instance, putting a series of these brain computer interfaces on the brain of a coma patient potentially tell us about what's actually happening inside their mind as they're laying there.

C (52:57)

Yeah, I mean, this is something that we think is profound, that we're actually actively working on. And it's due in part to work done over the last decade or decade and a half in the neurology and neuroscience community to try to understand coma. And, you know, this notion of consciousness is something that very, very deep in philosophy and neuroscience. And, you know, a lot of. A lot of time and effort has been spent trying to even understand what that means. But everybody has a. Some intuition for what consciousness means. And that coma represents a kind of disorder of consciousness or lack of, or almost lack of consciousness. But there is a spectrum between coma and normal wakefulness that all of us here in the room experience. And it's not exactly even a linear spectrum. So, you know, we. We all familiar with sleep. Sleep, right. And different sort of phases of wake, different degrees of wakefulness. And, and people who have certain forms of brain injury, for example, severe trauma or debilitating strokes, they. They. Especially in the early phases of their disease. And, and, you know, many of the listeners may be familiar with family members who've had very severe injury that results in a coma or a severe change in the state of consciousness. And for. For decades, this has been a really vexing problem. Trying to understand is the. Is the person who's currently unconscious or currently in a coma going to emerge from that state? And if they do, will they emerge as the person that we knew? And in the period in which they're not wakeful and able to communicate, are. Are they in there? So to speak? Right? Is that person in there? Are they. Are they thinking and not just not able to communicate? Or are they not there at all in the way that we knew them? And it turns out that for some disorders of consciousness, some people who are. Are, you know, seemingly not able to interact with the outside world. Their eyes are closed, they are not moving or speaking. Some of those people, it's now known about maybe even 15 or more percent in some cases, have the ability to think at least for some portion of the day and, and even can modulate their neural activity in kind of the same ways that we do in order to. To drive speech or to drive movement, but they can't make that movement manifest in the outside world through vocalized speech or movement, which are the ways that we normally communicate and express our consciousness. And that, if you think. If you stop to think about it, is. Can be both troubling, you know, on a deep level, but also represents kind of an opportunity, which is that the same brain computer interface technology that we've been talking about to restore movement and people who we know to be totally lucid but paralyzed, that same technology actually can serve as a bridge both to help diagnose and provide prognostic information for whether the person who has this brain injury is Likely to recover from it, and even in that period when they're not fully recovered, to provide a window into what's going on inside so that they can actually communicate in some ways with the outside.

A (56:34)

So there's a world, you're saying, where it might be possible to effectively talk with coma patients through bcis?

C (56:40)

Not coma patients, because coma means that they don't have a level of consciousness that can provide that. But there is an intermediate state set of states. They're called, for example, minimally conscious state, for example, or cognitive motor dissociation is the term that's often used for these states, which are in between coma and full wakefulness on this sort of spectrum. And those patients, they look effectively like patients who are in a coma. And it's very difficult to distinguish them in some cases. And so we feel, and we're working on this, that brain computer interface technology can provide a tool for distinguishing those patients from true coma and, yes, providing a way for them to communicate.

A (57:28)

That's unbelievable.

B (57:29)

And, you know, this has important predictive power in terms of, as Ben mentioned, determining the chances that they recover and become themselves again. Right now, you know, this sort of continuum of consciousness is most often diagnosed by nurses who are just trying to perceive whether there's any wakefulness or movement or an ability to respond. But that's in the context of, you know, loud hospitals and lots of patients who command their time. And so the error rates are extremely high right now. So being able to do this in a way that's much more definitive and also, which is, as Ben mentioned, gives people who are in there who are able to modulate brain activity so they can imagine, you know, you ask them a series of yes, no questions, and they can actually answer them by imagining making certain movements, allowing them to express themselves and communicate out to the world. I mean, it's just. It's sort of an unimaginable thing to be in that state and to be conscious or to be at least aware of what's happening and to be completely uncommunicative because you can't control the muscles in your body. And so I think this. This is a bridge that has the potential to be really important.

A (58:45)

Crazy. All right, I have two questions for you before we leave. The first is we kind of touched on it a couple times. But what happens when people trust their brains to BCI companies? Let's say this, you know, 10 years or 30 years down the road becomes commonplace to have some sort of device in your brain. I know where right now or right now, you're mostly using this for disabled people or exclusively using it for folks who, like, need it to be able to function. Is there a worry that someday that technology, if it becomes too commonplace, can be used to hack into brains or to write signals in a way that, you know, you get a bad actor in there? Like, there's this study. I think we're going to talk about it with Sally Adir. She's already been on the show to talk about it, but there were like these rats that were able to hit this pleasure lever and, and that would send an electrical signal into their brain, and they would basically do that all day and not do anything else. So what do you think about the ability to write and should we be afraid of it?

B (59:44)

I feel like that button for rats is like Instagram. Right.

A (59:48)

Look, there's definitely parallels in the human experience today. No doubt.

B (59:51)

Yeah. I mean, these are incredibly important questions. The issue of neural data privacy, neural data security, you know, there's nothing more inherent to who we are than our. Than our brain activity. I'd make a few points about this, and it's obviously an evolving space. One is we're a healthcare company. We're developing medical technology in the context of the FDA regulatory regime as well as.

A (60:16)

I'm less worried about your device right now being used to hack brains, but.

B (60:21)

I think these concepts are being considered now. It's early. I think we're developing a tool for paralyzed people to live higher quality of life. But over time, I think these issues will certainly emerge. And the FDA has actually taken a very proactive role in helping define the regulatory regime, not just for today, but for tomorrow as well. They've helped create something called the collaborative community, which they've done in a few different areas of emerging medical technology. And it's a way to convene a lot of the different stakeholders into one place to map out clinical practice guidelines, you know, reimbursement, as well as some of the ethical consider considerations which are different depending on the technology. So this is, you know, academics, these are the patient advocates, this is industry, this is clinicians, these are hospital administrators, these are payers like insurance companies. And so one of the work streams of this collaborative community, it's called the implantable brain computer interface Collaborative community. It's a little bit of a mouthful, but is specifically focused on data privacy, data security, and ethical considerations. And so I think that's an incredibly powerful sort of forum in which to. To start mapping out what this looks like in. In coming years.

C (61:44)

I mean, let me take that in a different direction just because, I mean, as you can tell, as futuristic as brain computer interfaces are, we're pretty practical minded as a company, really, really trying to bring this technology into the real world to impact people and become part of the standard of care. But having said that, you know, I think we're profoundly influenced by the science fiction of our childhood and of modern times. And there's a long track record of, you know, the science fiction of today helping to influence the science reality of tomorrow. So we take these, we take our responsibility in this regard very seriously and we take these thought experiments very seriously. And as Michael mentioned, I think we and others in the space are trying to ensure that this all develops in as responsible a way as possible, understanding that it can be hard to predict what happens and it can certainly be hard to legislate around, you know, all future eventualities, but we definitely have our eye on it.

A (62:51)

Okay, let me end with this. On the theme of science fiction. We talked today a lot about transposing the brain into compute, taking thoughts from the brain and bringing it into technology. What about the other way around? So I'm curious if you think there is a way for AI in some way to merge with the human brain. And then I guess as a corollary to that, knowing what you know about human consciousness, do you think it's possible for, at all, for AI to achieve consciousness or self awareness?

C (63:27)

Yes.

A (63:29)

Say more.

C (63:29)

Yes. You should have us back on the show for another episode on whole brain interfaces merging with artificial intelligence and AI consciousness. I mean, that's a whole another couple of hours.

A (63:42)

But you're a neurosurgeon, you believe AI can achieve consciousness.

C (63:45)

I do, yeah. How? I mean, I don't think that. To me, it's not even such a. I, I don't even see that as such a, I don't even see that as such a difficult or troubling question.

A (63:59)

Okay, so we will have to do another hour then is what you're saying. And then what about this idea of, of AI and human brains merging? Any thoughts on that?

C (64:09)

It's our. But it's already happening, right? I mean, in some ways that's, that's exactly what we're developing. And we, we see the brain computer interfaces today, as you alluded to and as Michael mentioned, as kind of like the, in some ways the foundational layer of, you know, a merger between the brain and artificial intelligence. Right now it has some very practical manifestations, which is effectively to become a different kind of user interface. You know, as Michael mentioned, we have ways today that we've become accustomed to of how we interact with the digital world. And it's usually with voice or hand control. But the technology that we're building is to enable direct brain to digital world control. Right now, actually what we're doing almost of necessity because so much technology is just built around voice and gestural and you know, hand motor control is kind of a two step bridge between neural intent and a conversion to what would be, for example, typing on a keyboard or moving a cursor or speaking some commands to a computer. But that's just kind of an artifact of the way the user interfaces of today are built. We already know actually that the latency between your brain and your hand and the ability to think something and type it is around 25 milliseconds. So that actually puts a biological hard limit on how fast you can interact with the digital world. Even though you're thinking faster than that. Right within a brain computer interface, the, the latency of the system is right now in the single digit milliseconds will be even faster than that.

A (66:02)

That's why Nolan said he's kind of like he has superhuman abilities when he plays.

C (66:06)

Exactly right. So many, many, many participants in these clinical trials have described it in similar terms. Kind of like the neural interfaces predicting what they're thinking. This gets to your question earlier about decision making and where and when is the decision made and when can a neural interface infer that or predict it. But I'm using this specifically to just point you in the direction of what the user interface of the future, where brain computer interfaces are pervasive looks like. Like, it looks like not a keyboard or voice activation or gestural control. It looks like something that almost is, is predicting or intuiting what you're thinking. And it's actually, it's counterintuitive to how we interact with the world because we are built and wired and have an intuition around actually interactions with the world that are, that are built around this 25 or so millisecond latency. Anything that happens faster than that, you don't realize that it has that latency. And so when you see it happen faster, it almost seems like magic. But that's what an inherent brain computer interface, user interface looks like that's happening. And that's just the first step in what, you know, kind of a merger with artificial intelligence looks like.

A (67:20)

Okay, Michael, let's give you the last word. I want you to just give us like a realistic timeline of like what the next couple of years are going to look like in this technology and then what size of a business that could be if it works according to plan.

B (67:32)

Yeah. And just to. I'll answer both those questions, thank you. But just to follow up on what Ben was saying, just in layman's terms, we already are augmented by AI. It's just slow. We have to type. And I think that there will be an ability to access information much more quickly and also much more intuitively with context, which is part of the reason that companies like Meta and Google are developing wearables. Context is going to really improve the functionality of these systems and how we use them. And I think that that's going to be part of brain computer interfaces as well in terms of timelines and market size. You know, I think that there is growing recognition that this is going to be a big industry. Morgan Stanley wrote a report last year that estimated a $400 billion TAM and that that will build first in some of the medical applications that we described. We expect within five years there to be precision system on the market and maybe one or two others that are making a big clinical impact for people who are severely paralyzed and then expanding from the sort of $2.5 billion a year market that the severe paralysis represents to something that's 10 to 15 times that as the applications for the technology become wider. I think what we have at precision that's unique within the context of brain computer interfaces is the ability to commercialize a temporary version of the system. Ben mentioned that we have our first FDA clearance, which is a tremendous milestone for precision, but it's also just a sign of the progress of this industry towards real commercial and clinical impact. That is not instead of our permanent implant, that is in addition to, in parallel to our permanent implant. The, the constraint to a temporary implant is that it can be implanted up to 30 days. But there are a number of applications for a 30 day implant, some of which Ben described in the disorders of consciousness, which we think are actually going to create businesses that are several hundred million dollars in annual revenue and which do a tremendous amount of good in terms of human health.

A (69:44)

All right, well folks, if you stayed till the end, I promised you we were going to get into some weird and good stuff and I think that we delivered. So thank you for staying with us all the way until this late moment. In the interview I called brain computer interfaces, I said that 2025 was going to be the year of brain computer interfaces. And I think the conversation that you just heard really shows that it is unfolding exactly that way. So, Michael, And Ben, so great to see you. It's always fun to talk. And I hope you do come back back and give us that extra hour on whole brain mapping and whether AI and the human brain can merge.

C (70:19)

Sounds good. Looking forward to it.

A (70:21)

All right, thank you so much for being here. And thank you, everybody, for listening and for watching. We'll see you next time on big Technology Podcast.