Ben is co-founder and CSO of Precision Neuroscience, Assistant Professor of Neurosurgery at the Icahn School of Medicine at Mount Sinai, and Scientific Director at Mount Sinai. Previously, he co-founded Neuralink and Simbionics (acquired by Apple).
Precision is building a minimally invasive brain-computer interface (BCI) that reads from thousands of points on the cortex without penetrating it. The Layer 7 device is implanted through a one-millimeter slit in the skull rather than the larger borehole other approaches require. It is also fully removable.
Precision seeks to help the 5 million people living with severe paralysis in the US (including 800,000 new stroke cases per year). In March 2025, Precision received FDA clearance for a temporary wired version of the system. Over 85 patients have been implanted with and used the device in clinical studies. Wireless implants are planned for 2027.
We go deep on the history of Neurotech from the 1980s to the ML inflection points that triggered Neuralink’s founding, why surface ECoG was a contrarian bet that’s now paying off, the path to treating paralysis and stroke at scale, and why Ben believes neural data is at the same inflection point genomic data was in 2000 — a whole class of biological problems about to become tractable as computer science problems.
Sections
00:00:00 Introduction
00:04:39 Paralysis as a lens to understand the brain
00:05:36 The 1980s breakthrough: population encoding and the birth of BCI
00:14:36 Google Translate, ML, and the founding of Neuralink
00:23:08 What is the long-term vision of Precision Neuroscience
00:31:56 Layer 7 and why transformative technology always looks impossible at first
00:50:21 The surgery: a slit in the skull, not a borehole
00:55:19 The clinical program: who are the patients
01:04:16 FDA clearance and the path to wireless implants in 2027
01:08:32 The patient population: paralysis and stroke at scale
01:16:26 Neural data as the new genomics
01:30:06 BCIs, AI, and the future of the human-machine interface
01:31:22 From medical necessity to lifestyle technology
01:40:36 Precision as a platform — and an optimistic vision
Links from the Podcast
Precision Neuroscience: https://www.precisionneuro.io
Layer 7 BCI: https://www.precisionneuro.io/our-technology
Icahn School of Medicine at Mount Sinai: https://icahn.mssm.edu
Podcast Episode Links
Episode Links
Disclaimer: https://bit.ly/PodcastDisclaimer
Transcript
Juan Benet
My guest today is Ben Rappaport. He’s the founder of Precision Neuroscience. He previously co-founded Neuralink and Symbiotic which was acquired by Apple. He’s assistant professor of neurosurgery at the Icahn School of Medicine at Mount Sinai. He leads research in brain computer interfaces and serves as scientific director of Mount Sinai Biodesign. He studied physics, mathematics.
He got a PhD in EE and computer science, went to med school and then became a neurosurgeon and treats lots of individual patients. He’s authored over 50 papers and 40 patents in neurotech, and medical innovation. So he’s just a legend in the field. I’m very honored to be chatting with you today.
Thanks for taking the time.
Ben Rapoport
Thank you so much for having me. It’s a pleasure. I’m flattered by the introduction.
Juan Benet
Thank you so much for all of the work that you’re doing for people globally. It takes a lot of individual drive and effort of people working for decades, both in the core science and the core innovation to actually get tech translated and available.
So thank you from all of the people that are benefiting from it. So let’s dive in. Let’s talk about the device, which I think is a beautiful work of art. Maybe walk us through the design of layer seven and kind of what were the core insights that you designed around, like why this particular form factor?
How does it sense neurons? Why is it in this particular way? It’s very different than than many others.
Ben Rapoport
It is different. Nothing is totally emerges out of a vacuum. So it’s similar in certain ways and, different in certain ways, but you’re totally right in the brain computer interface world when we started precision, it was heretical in a sense because the whole field really had developed around micro penetrating intracortical, micro electrodes that were developed to record kind of one neuron at a time. In the ‘80s, ‘90s, 2000s, in the academic field of brain computer interfaces, there was a real emphasis on trying to record from individual neurons. The neuron being thought to be the atomic unit of information processing in the brain and the atomic bit of information being the action potential, so-called colloquially referred to as a spike. So a one millisecond duration signal, like the ones and zeros of the brain. That was kind of how the brain computer interface world, academically was thinking about, interfacing with the brain.
In the medical world, there has been for many decades, routine use of electrocorticography. So surface contacting electrodes that sit on the cortex without penetrating in, and are used to map neural activity, with very precise timescales and, in the clinical domain, usually much less fine spatial scales but known to be safe and quite flexibly useful for many decades.
And one of the insights that I think we take for granted on the medical side is sort of that most of the conscious experience of human beings takes place at the cortical surface. So the brain is of course, this three dimensional structure, but really it’s optimized for surface area. If you’ll take a look at the neocortex, it’s kind of like a flat sheet that’s crumbled up into a ball and it has these hills and valleys, as a way of optimizing for surface area.
And the reason is that, at the surface, that’s where all of the conscious thinking happens. A lot of what’s on the inside is essentially white matter or wiring that connects what happens at the surface of the brain to the outside world through the brainstem and spinal cord. The intuition at the beginning of all this was that we really want to be sensing what’s happening at the cortex in a way that maps in the greatest spatial extent, relevant to what’s happening at the cortical surface, but not damage the brain by penetrating into it if we can avoid it.
What if there’s a way of doing that? Well, electrocorticography already existed. No one had built electrocorticography systems that were at comparable scales to the intracortical penetrating electrodes that had been used in the world of brain computer interfaces. So we said let’s just do that. If we do that and we get the electrodes down to the scale of individual neurons, we should be able to sense high quality neural information.
We also knew there are some kind of like in every field, there’s black magic, just like in AI. Like in early machine learning, there’s all the black magic, like when do you stop training?
How the actual stuff gets really done? There’s the stuff that you write papers about and then there’s the stuff that you have to learn by doing which nobody talks about. So one of the things that nobody talked about with intracortical electrodes was that very often you would stop recording from individual neurons, or you’d start recording from one, and then it shifts to another because the electrode moves a little bit or the brain responds a little bit. The impedance of the electrode changes and so you can’t record from individual neurons and you’re really recording from a bulk average of tissue. All of these things were happening and anybody who was in the field knew that was the case.
The reality was that recording from a local average field and recording from individual neurons was basically equally informative from the standpoint of can you decode what the brain wants to do?
Juan Benet
Mm-hmm.
Ben Rapoport
It’s not equally informative if you’re a neuroscientist trying to study the behavior of individual neurons. But from the standpoint of how the brain decides how to make the body move through the world, it’s equivalently informative.
Juan Benet
Is that primarily for motor cortex cases? Or will there turn out to be other areas, of thinking or behavior that end up with these populations of neurons?
Ben Rapoport
I’d say that it is definitely true of the motor cortex.
Juan Benet
Yeah.
Ben Rapoport
It also seems to be true of many other areas of the brain. Are there processes that require more and less fine detail? Probably. A hundred percent. But is there a huge application space that resides in this scale of hundreds of microns of spatial and few milliseconds of temporal resolution?
Absolutely. This shouldn’t surprise the machine learning world either, right? Because like, what was the TPU and the GPU all about? It was about reducing precision in order to increase the scale basically in order, and so the fact that we can lose a few bits of precision in order to increase spatial scale and scalability and addressability of different areas of the brain. and, and just to make a
Juan Benet
safer device
Ben Rapoport
isn’t easier. Yeah,
Juan Benet
Yeah.
Ben Rapoport
Without compromising efficacy at least in these class of problems.
Juan Benet
It seems to me like a classic case of a great contrarian secret when building a technology company. It’s like the rest of the field thinks and behaves a particular way. You have this core insight that, hey, actually the problem gets a lot easier and you can tap a whole range of use cases by applying this other case. You might not get the full holy grail of hundreds of thousands of individual neurons, but a whole wide range of applications are achieved.
Ben Rapoport
That’s not my Holy grail.
Juan Benet
Yeah, yeah,
Ben Rapoport
Like you asked me at the beginning, what’s my holy grail? My holy grail is making products that work for people. My holy grail is not studying every individual neuron in the brain simultaneously. For some people, that’s the holy grail. It’s not mine.
Juan Benet
How many people out there might qualify for this kind of treatment on the road?
Ben Rapoport
For the most severe forms of paralysis. Basically, spinal cord injury from the neck down, severe impairment of both hands. We’re talking tens of thousands of people in the United States per year newly affected. If you looked at a cross-sectional study of the United States today, about 5 million people existing in need. So of 5 million existing and a few tens of thousands per year newly affected. That’s for the most severe forms. So people who basically are paralyzed almost completely from the neck down and don’t have adequate use of their hands to engage in routine desk job work.
My hope is that, as long as the technology is safe and effective, other forms of paralysis will be amenable to treatment with this type of technology. I think that the Precision technology is well poised for this. I’m particularly thinking about stroke, because stroke is the most common form of paralyzing injury.
It’s different from a spinal cord injury in that it is more often incomplete. It does not usually affect both sides of the body in quite the same way. It has, in some ways, less focal impact because of the way disrupting blood flow to the brain works. But nevertheless, I know there is a huge need in restoration of function after stroke. There’s a big segment of the medical community that really believes that one of the next stages in the impact of brain-computer interfaces is for people with stroke. Because this is a little bit different from the AI futurist application world of how the user interface change and we can get there too.
If you think about it in terms of the medical need, in the last 10 years the treatment of stroke has been totally revolutionized. When I was growing up, when you were growing up, stroke was a medical disease.
There was nothing procedural to offer. That changed slowly over time. And in the last 10 years, the ability to intervene acutely and remove a blood clot from a blood vessel that is causing the paralysis, that is causing the stoppage of blood flow and oxygen to the brain, changed. That and a few other things made stroke, as a whole, a more procedurally amenable condition.
And I think there is a very influential stream of thought in medicine today that feels that, even though that has saved life and limb for many millions of people, many of those people whose lives are saved and improved live with a form of paralysis that we do not yet have a way of treating. There is a sense that what is waiting for those patients, and in the United States that is almost a million people per year who are affected by stroke.
Juan Benet
Wow.
Ben Rapoport
This is and was a contrarian position. At the beginning, we were told, “You can’t do that. You can’t get enough information out of the brain just from the surface.” And then, of course, like anything, you have to prove it. So that took time.
Now people ask other questions, and they don’t say, “Can you do it?” They ask, “How well can you do it?” That’s a natural evolution.
Juan Benet
Classic case of you can’t possibly do this. Well, here it is proof. Right. Okay. But you can’t possibly do this other thing. Right?
Ben Rapoport
But to your question, and to where you started, that was the insight.
So the notion was, what do we need to do if we want to get to those scales? We need to make electrocorticography arrays that are on that scale. Well, how do you build something at that scale? Actually, like we said, the real problems are not so easy to solve sometimes.
Because when you have to put a lot of electrodes in a plane array, how do you connect to them? How do you make such a thing? How do you make it small enough? How do you manufacture it in a reliable fashion? How do you manufacture it in a safe fashion? How do you route all the electrodes out? How do you power that device, and so on?
So the answer to how do you make a small patterned device was easy. You use photolithography. That’s how you make all kinds of small electronics.
Easy to say, but not so easy to do because, although well known and universally used in electronics, photolithography is really not used in medical devices. The number of medical devices that use photolithography in a sensor context, you can count on one hand. Precision is now one of them.
But also, this thing has to be flexible because it has to conform to the surface of the brain. By the way, the brain is delicate. It has a Jell-O-like consistency. So the interface itself has to be correspondingly delicate.
And all these electrodes have to be uniform in size, shape, and impedance. Then we had to solve questions like, how fine do you make them? What material do you make them out of? And how big should they be in order to record from the electrodes? These were all questions that had a theory behind them and that we then had to go and solve empirically.
But the fundamentals are that the modular device is a polymer thin film that contains tiny platinum electrodes, most of which are about the size of an individual neuron. The module that we use most frequently contains 1,024 electrodes, but we can place many of them side by side to tile the brain.
The traces are very thin platinum wires, essentially, that are embedded in this polymer thin film. Those traces then connect into a hermetically sealed can that contains the electronics that amplify, digitize, and condition the signal, and ultimately transmit it wirelessly outside of the body.
Juan Benet
It is beautiful and fascinating. You had this idea for how to produce this kind of device. How do you start? How do you make it?
Ben Rapoport
How do you make it at scale when you’re building something that demands incredible quality and reproducibility?
The truth is, there was no supply chain for this. There were research-grade houses that could make small batches of things, but not usually in a controllable fashion, in a reliable way, on time, and under quality controls.
When you’re building something that is going to be implanted in the human brain, it needs to adhere to medical-grade quality control. It needs to be highly well documented, predictable, testable, biocompatible, and meet all of these specifications that we appropriately require of a device that is going to be touching and implanted in the human brain.
We had to bring that capability in-house. We now own and operate a microfabrication facility just outside of Dallas. It is amazing.
Juan Benet
Do you build it from scratch or do you acquire it?
Ben Rapoport
We acquired it.
Juan Benet
Yeah.
Ben Rapoport
It’s very hard to build one of these from scratch.
Juan Benet
Yeah. You wanna make a BCI device, you must first get a fab.
Ben Rapoport
That may be the case.
Juan Benet
At least for the short to medium term.
Ben Rapoport
I think that’s true. As of five years ago, there was no supply chain. During COVID, many of us became acutely aware of manufacturing supply chains and how they limited the development of technology and the progress of industry.
So we started all this early in COVID, and we were thinking through supply chain with incredible sensitivity at that time. And there was, and remains, no piece of the medical technology supply chain that involves microfabrication.
Juan Benet
Especially if you want to tweak the process or do something that is a bit out of the ordinary, you then have to do it yourself.
Ben Rapoport
Yes. So if you have a locked design, then maybe that is one thing. But all of us who are seriously in the field know that it is evolving. So the ability to conduct R&D while we are building product is essential. You hit the nail on the head there.
Juan Benet
Yeah. If you’re serious about your hardware, you have to be serious about your fabs.
Ben Rapoport
Definitely.
Juan Benet
Yeah.
Ben Rapoport
Yes. You have to be serious about the R&D. You have to be building for today and also planning for tomorrow.
Juan Benet
So you design the device, and in order to make it, you try working with external groups. It does not work as well, so you need to bring it in-house and get your own fab. And so now you have this awesome device. We now have some insight into the approach in sensing and sensing populations of neurons. Potentially, some of these can get small enough to sense one or a few neurons at a time. But it also has this very super-flat. What is the thinking there? You also developed this new surgical procedure that is very different from what a lot of the other BCI groups are doing. What is the thinking there?
Ben Rapoport
Okay. So let’s unpack that.
But before we continue, as you were asking the question, I wanted to pause for a second. Because you frame a lot of the questions with “you,” and I want to clarify that I represent a plural “you.”
This is not just me. “You” represents a whole team of people, certainly at Precision and in the field as a whole. So I want to acknowledge that.
We were talking about the microfabrication facility. We did not just acquire a facility. We acquired an incredibly experienced team that had actually been working at that facility, for some of them, for more than a decade.
That team knew how to operate the facility and knew how to work together as a team. So that is not to be taken for granted. Of course, all of this science and technology is developed collectively by expert people. We are not quite at a millennium of experience yet, collectively, but it is a lot. It is a lot of people working together as a team.
So I am here having this conversation with you, but representing a huge team.
Juan Benet
And that know-how is part of what makes fabs so hard to reproduce and hard to scale, rather than just the depth of expertise of a team that is able to run them very well.
Ben Rapoport
No question. And it applies to basically every piece of what we do.
There is the clinical side, which we touched on. But the team that does the clinical interface, working with expert physicians and surgeons and patients and families, that itself is an expertise. The whole medical system and academic medical system in this country and abroad that enables early-stage technology to enter the medical system at an inchoate level, that itself is a whole other conversation.
The electrical engineering that happens on the R&D side, the machine learning, all of these are groups of experts, and the development of the technology and its rollout into the world depends on that.
So, you asked a series of questions, which include the electro array being planar, what the thinking is there, how it is delivered onto the brain and how that is different from how other groups have thought about it, and what signals we are actually recording from the brain and how we think about that.
We talked earlier about how the brain, conscious experience, kind of unfolds in the two-dimensional surface of the cortex. That was the reason the electrode array is flat, so to speak. We’re really focused on neocortical real estate for the most part, although we’ve also interacted with the brainstem and spinal cord. But again, the computing happens mostly at the surface, and the wiring happens under the surface.
So with that insight, we can build interfaces that focus on the computing elements of the brain and on sensing and stimulating it at those interfaces. That’s obviously a simplification, but it’s a really useful simplification.
The planar nature of the electrode array also allows us to deliver the array in some innovative ways. First of all, it makes the array placement in the brain, or on the brain, incredibly safe. Because basically, the array just caresses the brain surface. It sits delicately on the brain surface. It basically adheres through capillary action, and it does not penetrate the brain surface. And yet, it records with great spatial and temporal fidelity.
That means we can place the electrode array, and it can be moved along the surface. So a little bit of trial-and-error adjustment is possible in ways that are not possible when you have a needle-like electrode that needs to be withdrawn and replaced in order to optimize its position.
So that carries certain advantages. It is also possible to be extremely modular. We can place a lot of these electrode arrays simultaneously without having to worry much about whether there is a blood vessel or what have you.
We can also get into the nooks and crannies and folds of the brain, so we can get around onto surfaces of the brainstem that are inaccessible to conventional electrodes. We can place electrodes within the sulci, the folds of the brain, which are really not accessible to penetrating types of electrodes.
So there are certain things that this planar, film-like nature of the electrode allows us to do that are not possible with traditional electrodes.
One of those things is that we can reach parts of the brain that we do not need to expose. What does that mean? Traditionally, in order to place electrodes or sensors in or on the brain, you need to expose the part of the brain that you want to address by making a hole in the skull or cutting a piece of the skull out, and then penetrating the brain’s surface underneath where the skull has been removed.
The surface array that we have can be placed through a little slit in the skull. So we designed it to be placed through a slit in the skull, like a little incision in the skull and the dura, and it can be slid across the brain surface. It can be placed directly adjacent to this slit, or it can be placed many centimeters away.
And we have done that, or rather, our collaborating surgeons have done that. They have made small apertures in the skull and slid the electrode many centimeters away. That can be done with one electrode or with multiple electrodes.
So what the technology allows us to do is really decouple the invasiveness of the surgery from the total amount of information that you can exchange with the brain.
Juan Benet
Yeah.
Ben Rapoport
That was also kind of an “aha” for brain-computer interfaces: the minimally invasive nature, not just in terms of the way the electrode contacts the brain, but also in terms of the procedure itself, and hopefully the risk and risk-reward profile for patients considering undergoing the procedure.
Juan Benet
Maybe to help listeners understand the difference, describe how some of the other surgeries happen, how the devices are implanted, and then the approach you’re taking.
Ben Rapoport
In the research world where this all started, it was required to cut a piece of the skull away. That is a traditional technique of brain surgery. So when patients need brain access to remove a tumor, seal an aneurysm, or something like that, there are tried-and-true ways of accessing the brain that are totally appropriate when there is a need to get in.
But of course, the less you open the brain, from many perspectives, the better.
All of the prior-generation technologies were based on needle-like electrodes. The only way to get a needle, even a very fancy needle, into the brain is to expose the place where you want to put the needle. You need to expose the area directly underneath where you are removing the skull, because that is where you are going to be placing the needle.
So if you are trying to get to the surface of the brain, you need to remove skull over that whole area you want to address. And so that becomes a pretty invasive procedure.
That applies to Neuralink, for example. Even if it is a very small removal of skull, to scale the process, you do need to remove more skull in order to get more electrodes in.
Juan Benet
It seems like most of the BCI companies right now are following this kind of procedure, where they’re doing a borehole right on top of the area they’re trying to sense, especially if they’re trying to scale it out to multiple different sites. That would mean many holes in the skull.
Ben Rapoport
Exactly. So it would be nice to decouple the number of holes, or the size of the hole that you want to make in the skull, from the total number of electrodes, or from the functionality of the device.
And that’s what we did with the Precision system. We can make one small aperture, which in our case is either a small burr hole or a slit. Through that one-millimeter slit or very small burr hole, we can slide many electrode arrays, so many thousands of electrodes can be placed.
That decoupling of the invasiveness of the procedure from information exchange with the brain was a Precision innovation.
Juan Benet
That also seems like another great contrarian secret, where the rest of the industry is going in one particular direction. When you scale that up, or take it to its conclusion with many of the later applications, that seems like a long-term, maybe brittle path. And if you take this minimally invasive approach instead, and you focus on how you achieve scalability through that, that seems like a very good long-term strategy.
Ben Rapoport
That was deliberate on our part. And I think it comes a little bit from the starting point. It comes from combining an engineer’s orientation to the field with a physician’s sense of what is known to work and what is acceptable to people with a problem.
Juan Benet
So you have the device. You have this innovative surgery for delivering it. What is the range of applications that you’re using it for now? You’ve implanted around 50 people. What were those cases, and what were you looking at so far? And if I understand correctly, currently you implant it for a period of time, use it, and then remove it. It does not stay in there for now. What are the kinds of things you’re learning at the moment, and where is this headed?
Ben Rapoport
One of the things that we tried to do when we started Precision was to avoid getting into a situation where brain-computer interface is already high-complexity and high-stakes field. We’re talking about developing a fundamentally new technology for implantation in the human brain, and in the brains of people whose brains may already have been compromised in some way. So the risk tolerance is pretty low. How do we ensure that what we’re developing is going to work?
One way is to spend a lot of time building something in the laboratory, then prove that it’s safe through some incremental process, and then start clinical trials with the end-state device. But we know that something is not going to be quite right, and you would have wanted to know that at the beginning and fix it before you got all the way down the path.
There’s almost no field of technology in which that is how things are developed. You don’t build a multibillion-dollar rocket with many millions of parts without testing subsystems in the real world. You don’t build a high-performance automobile without iterative test laps and a team that can pick the thing up on a lift, get underneath it, and tweak stuff after five minutes of testing. Same thing with software, with consumer electronics, and with almost every other complex system.
So we knew that we wanted to be in a situation where we could test, validate, and verify pieces of the complex system in parallel. And the problem with medical technology, including brain-computer interfaces, is that the end user is not really part of the engineering team.
The end user is a patient, a surgeon, a neurologist, or some combination thereof. So how do we bring that part of the user experience into the design loop?
One of the good things about the Precision technology is that the electrode array we were just discussing is removable and reversible, because it does not penetrate into the brain. It can be moved and removed without traumatizing the brain. And so the risk profile of the device is fundamentally different from that of penetrating electrodes, which, however safe they may be, still do some amount of cutting into the brain.
So the ability to do that in a temporary fashion, or in a research-type fashion, is fundamentally different. Because of the nature of our electrodes, we were able to design and supervise studies with clinical partners at major academic sites, in which patients undergoing standard-of-care neurosurgery, sometimes asleep, sometimes awake, could volunteer to participate in an early-stage study involving the use of the Precision technology.
In that way, we learned a tremendous amount about how the devices interacted with brains in the real world, in real clinical settings, and what the signals looked like. Importantly, even from a very early stage, we proved that in people who were awake, and most of whom were able-bodied, they could execute and imagine movements, and speak, and that we could, using the machine-learning side of this technology platform, which we have only barely touched on, decode the neural correlates of intended, attempted, and executed movements.
That’s the fundamental insight and motivation of our clinical program. Number one, it is to validate the technology, make sure that we are getting the signal quality we need, that the signals we are getting are decodable, and that we have a signal-processing algorithmic pipeline that really works. So we could de-risk that, and not only de-risk it, but really push it forward in a nuanced and accelerated fashion, even as other parts of the system were coming online.
We are well beyond that at this point, five years into the company. But that was the insight early on. Let’s make sure that we are decoupling risks and testing pieces of the complex system together.
So, to your original question, who are the patients who are involved, and what is the nature of the surgery? With different clinical sites, we have different types of procedures in which the device has been used. I’ll give you a few examples.
In some brain surgeries where there is a tumor near language-related areas or movement-related areas, part of the standard of care is to electrically map where the so-called eloquent areas are, where the language-related or movement-related areas are, so that those areas are not removed along with the tumor.
Juan Benet
This is localizing what areas to make sure to really avoid.
Ben Rapoport
Exactly. Which areas to protect, and which areas represent abnormal tissue that can be removed. There are traditional techniques that are used to do that. So it has been of interest to understand, can higher-resolution electrode technologies help do that better?
And also, while that is happening in parallel, once you have identified those eloquent areas and are operating on the others to remove the tumor, can we place the Precision electrode on what is known to be a movement-related area or a language-related area, and while the patient is awake, have them engage in a behavior, in speech, in movement, hand gestures, and so on, that allows us to test and validate that we can identify distinct signatures of those neural behaviors, those intentions, those behaviors, and really make sure that when the technology rolls out to people who can intend the movements, who can intend the speech, but cannot execute them because they are paralyzed, the device and the system will be able to perform.
So a lot of the early work that we were doing involved situations like that. Some of the patients had brain tumors and were being operated on partially awake. Some of the surgeries were for Parkinson’s disease, in which an electrode was being placed in a different area of the brain, but nevertheless that allowed access to the movement-related areas of the brain in people who, for a portion of that procedure, are awake.
And I just want to, as a sidebar, note that in this type of clinical study, the people who are undergoing the surgery are volunteering to do something pretty extraordinary.
Juan Benet
Yeah.
Ben Rapoport
They’re really partnering with us and with their surgeons to say, “Look, we understand that there are people a little bit like us, or who we may know, who down the line are going to benefit from the data, knowledge, and insight that we’re contributing. And that is going to help other people who are not us.”
So we’re willing to engage in this, to have our surgeon, neurologist, or treatment team spend a little bit of extra time, in parallel with our treatment, to help push the boundary of what is known and help somebody else down the line.
And I think, to me, that is part of how medical science advances. It’s amazing.
Juan Benet
It is extraordinary pioneering work that just benefits so many other people downstream.
Ben Rapoport
Totally. I think that has to be acknowledged. These people and their families are amazing. It is a very special person who does this, and I think that needs to be said.
Juan Benet
Yeah, absolutely. So many people in the future will be deeply thankful to those people who took action now to help push the frontier, understand what’s going on, and shape therapies.
Ben Rapoport
They are usually energetic, incredibly fascinating, and curious people you want to be friends with and partners with. They feel as invested as we do in learning, pushing the boundaries forward, and contributing something to people beyond themselves. But nevertheless, those are the people who are really on the spot at that time, and they usually have their own things that they’re worried about.
So to zoom out a little bit and think about somebody who may come along later, who you may never know or meet, nobody will know your name and you may not know theirs, but somehow you’re contributing something, it’s special. It’s altruistic, and I think not everybody realizes that that’s how it works.
Those are the kinds of interactions that we’re having. Earlier this year, we did receive FDA clearance for a version of our device, which is a percutaneous, wired version. Of course, the permanent implant is a wireless version of the system that records the neural signals and wirelessly transmits them outside of the brain so that we can control computer systems.
But some of the R&D versions of the system that we rely on a lot in our R&D are wired, including the ones that we use during the temporary implantations. So we now, as of March, have FDA clearance for a temporary version of the system that can be implanted for up to 30 days. That type of system is the type that can be used for this kind of R&D work.
I have to be careful about exactly the language that I use with respect to what is actually cleared by the FDA. So just understand that the R&D work and our collaborations with all of the clinical sites are all investigational studies, which are not part of the clinical use of the technology, but they give you a sense of what the capabilities of the system are.
Juan Benet
Yeah. You’re doing this range of R&D work now, this set of studies. At what point do you make the wireless device for long-term implantation and actually start treating patients?
Ben Rapoport
Yes, as intended. Treating people with paralysis.
The current plan is that we have a wireless version of the system that is currently undergoing the validation and verification required for an implantable medical device. We expect that, in 2027, the first of those devices will be implanted in human patients in what’s called an early feasibility study.
That’s our current plan. That first group of patients, assuming things go smoothly, will proceed to a slightly larger pivotal clinical study. Between now and then, basically in 2026, while we’re doing the validation and verification required to advance into that feasibility study in human patients with the wireless device, we’re also spending a lot of time, in parallel, in clinical studies at partner sites to robustly and validate the algorithms and the software, so that we can decode intended movement and do all kinds of things that represent the functionality we would want to provide.
We want to be as certain as possible that we will be able to provide that functionality to the patients who enroll in the study starting in 2027.
Juan Benet
How long is that study? If I’m thinking about people who may have the forms of paralysis that you might be able to treat down the road, what does the timeline look like for them? So you’ll go into that 2027 study. At what point does this become commercially available for them?
Ben Rapoport
Let me say that, approximately, the 2027 feasibility study will be on the order of 10 patients, maybe fewer. And the pivotal study will be, we don’t know exactly, but hopefully a modest number of patients.
So I think we’re talking about a single-digit number of years before. Both of those studies need follow-up. You need to enroll the patients, and they need a certain amount of follow-up. Some of that is still to be negotiated and agreed upon with the FDA, so I don’t want to say too much in a premature fashion.
But I think it’s reasonable to say that we’re looking at a single-digit number of years, if all goes well, to devices becoming part of the standard of care and starting to treat people with paralysis, and being available for people with paralysis.
Juan Benet
Amazing.
Ben Rapoport
So it’s like 800,000-plus people a year in the United States have a stroke, some of whom will fully recover, some of whom are extremely severely debilitated, and about a third of whom go on to live a full life expectancy but with a significant paralyzing deficit. I think many of us know people like that.
So the question is, what do we have to offer them today? And there isn’t much, actually. So there’s a stream of thought, and I subscribe to this, that brain-computer interfaces will have something to offer that group of people. And that’s a lot of people.
There are other areas, but to me, if you think just about paralysis alone, without thinking about mood disorders, the visual system, the sensory system, executive functioning, memory, and all the different things that the brain does that can go right or wrong, or can be optimized or augmented, to me that’s an area where I see the future of medicine changing through brain-computer interfaces.
Juan Benet
Do those numbers look similar in the rest of the world as well?
Ben Rapoport
Yes. I would say, as a matter of prevalence, yes. It’s stroke. There are some segments of the world where it is a little more or a little less common, but stroke is a common condition the world over.
Juan Benet
So the outlook is a set of trials now through 2027, maybe 2028, and beyond, with a single-digit number of years before hopefully being clinically available for a range of potential problems, both in the US and abroad. That’s a phenomenal outlook. Once you get through the studies, how does that scale from there?
Ben Rapoport
I’ll say two things. One is that I also think one of the things we’re learning, that we did not set out to learn, is that by engaging with the world of experts, with medical and surgical experts and with patients, and by getting early versions of the technology out into the world, we’re discovering that there are other smart, knowledgeable people with experience and ideas about what to do with the Precision system as a platform technology.
Juan Benet
Yeah.
Ben Rapoport
We don’t pretend to have all of the ideas or insight. But what we are finding is that people are coming to us and saying, “We’re seeing these signals. We’re seeing this technology that you have. Can we use it? Can we partner with you?” And that is a kind of signal that you seem to be on the right track.
So we have to stay extremely focused on building one particular product, but we are also spending some time and capital on building out future use cases and supporting a community of users in doing additional exploratory work. Some of that we have done a little bit of ourselves.
I think we do have a sense that we do not have all the answers, and that what we are building has a platform nature to it. Of course, in order for there to be a platform, you have to have one application. I do not want the cart to go before the horse. But we do hope that what we build will be a tool that enables discovery, and that enables clinically oriented discovery into areas that may not be our first use case, and that we may not be thinking about now, but certainly we are able to access and gather data from all kinds of areas of the brain, brainstem, and spinal cord and we want to enable others to do that work, either independently from us or in partnership with us, to build on the platform, especially in a software-enabled fashion.
So that is part of the vision and I do hope that some of the other applications of the technology will be thought of and moved forward by others too. We want to set the stage for that.
In practical terms, we do have quite a few partners. Some of them are clinical research partners, and some of them are other types of partners. We are trying to balance how to prioritize the main thrust of what we have to do with building for the future.
Juan Benet
It seems to me that there is this huge bottleneck and overhang in the capability set, because as soon as you are able to have devices that can read and write to the brain at some reasonable level of bandwidth, that opens up a wide range of use cases and possibilities that just have not been done before.
So there seems to be a lot of not exactly low-hanging fruit, because it is very difficult to build these kinds of devices, make sure that they are safe, and make sure they work really well for patients and so on. But there is mid-hanging fruit, and a lot of it, that could open up over the next 5 to 15 years once you are able to cross this hard part.
Ben Rapoport
Yeah. Let me give two examples of how this is helpful.
One is MRI, magnetic resonance imaging. If you had told somebody in the early days of MRI, “You’re going to go into a superconducting magnet that costs many millions of dollars to build, and it needs to be maintained with liquid helium at four degrees, and you’re going to get a high-resolution picture of your brain because of it,” they would have said, “What are you talking about? That’s crazy.”
Maybe they had heard of nuclear magnetic resonance being used to study individual molecules for chemical structure. But if you told them, “I’m going to put my brain into that, and it’s going to give me an image of it,” they would have said, “You’re crazy,” because you can’t scale that up. How are you going to get the resolution? Too expensive. Crazy. Okay.
But over time, at scale, MRI is now an everyday reality that you can pay for. It is cost-effective. It has totally changed the way we diagnose diseases of the brain, and other parts of the body.
Juan Benet
Saved millions of lives if not tens of millions.
Ben Rapoport
Absolutely. And by the way, within probably a mile of us, there are probably a dozen MRI scanners. So it’s accessible to everybody. Insurance pays for it. It’s cost-effective. People know how to run them. It’s push-button, in a sense.
So at the beginning, in 1970, you would have said, “That’s insane. No way.” And who needs it, because there are other technologies that allow me to diagnose all of this stuff? So that’s one example. Maybe a little mundane, but nevertheless the technology behind MRI is nontrivial. It’s a lot of physics, electrical engineering, and understanding of biology and chemistry that goes into making that a real product.
Now, is it a consumer product? Not quite. Is it a medical standard of care that’s universally available, not even just in the developed world? A hundred percent. So it takes time. But that reality can come.
Another example that is a little more directly relevant is genomics, gene sequencing. This is something that, in your life and in mine, totally transformed medicine in ways that, at the beginning, in 2000 or 2001, would have seemed impossible. It cost many millions of dollars to sequence the first human genome. Many people were involved. It involved chemistry and computer science and biology, and a massive infrastructure and logistics to get all of this done.
That’s interesting because it’s a biological code. Once the code was read, the tools of computer science could be applied to do something in biology that was never possible before. And I think in neuroscience we’re seeing something similar. Not exactly analogous, but similar. The availability of data in a format that is portable, relatively uniform, and that packages neural data in a way that makes it amenable to the tools we’ve developed for other things, including image and video processing and modern machine learning in a highly parallel compute world.
Unlike the linear and fixed-in-time structure of the genetic code, the neural code is basically a two-plus-dimensional code. It unfolds on a planar surface at a timescale of a thousand hertz, on physical scales of tens of microns, hundreds of microns. But we now have a technology that interfaces on those spatial and temporal scales. We have the ability to store data on that scale. And the Precision technology, we’ve learned, is easily applied in many contexts to harvest data, save that data, and compute on it.
So I think we’re starting to see the edge of an exponential. It’s hard to say that 50 patients represents exponential growth. But if you think that, as of 2024, the number of patients who had ever been implanted with a brain-computer interface was about 75, so 50 patients is almost like a doubling of that, in one company in two years.
I don’t want to predict too much, but I think there will be a lot more patients in a year’s time. In one sense, that is a huge amount of data compared to the petabytes of data you might get in other fields, it is a thimbleful, but it definitely starts to add up over time. It shows you that there’s significant growth here.
Juan Benet
Let’s get into the neuro data and AI segment. As you collect data and use the device to map and understand what different signals correspond to, you’ve talked about being able to aggregate this data across a number of patients over time to build more robust models of what’s going on and then enable more patients.
We’ve already had some good results where, in a range of applications, it might take a lot of time for one person individually to train to use a particular BCI. But if you’re able to aggregate the information across many such users and build better ML models, you can cut that down significantly, to the point where you’re able to extract much better signal from brains in general, and supporting each additional patient is much easier.
Plus, you might also be able to decode other kinds of signals that previously were super difficult to decode, or that people did not think you could decode.
So where does that vision take you, and what are you thinking in terms of scale here? How many people might this take to start yielding really strong results, and what path are you thinking about taking over time?
Ben Rapoport
Absolutely. To both directions that you articulated, I would say yes.
Direction one is can we achieve accurate neural decoding more quickly in patient n+1 on the basis of patients 1 through n and what we have learned from them. The answer to that is absolutely yes. We are already seeing evidence of that.
By the way, that is incredibly powerful and not necessarily a given. It is easy to see in 20:20 hindsight, but it was not. In the early 2010s, when every patient was an n of one and a lot of time was spent calibrating and recalibrating these systems, for those who know the field closely, it was not a given that you would be able to build generalizable models that would be able to decode neural signals in a patient’s brain when they had never been seen before, sort of off-the-shelf models.
An analogy that makes it seem obvious is speech recognition, where Siri or Nuance PowerScribe, or whatever your chosen system is, works out of the box and works better the more you use it. So it is true of brain-computer interfaces, and we have proven that now.
To your question of how many it took, we got an inkling of it in the first handful of patients. But by patient 10, I think there was a sense that we were seeing some things that generalized, and we were able to show that to our own satisfaction. Of course, more is better, in a logarithmic fashion, like you would expect from image recognition and other areas.
So definitely, and that is really important from a product standpoint. It is not just true that you get things that generalize. It is also true that more data improves performance. Accuracy in particular, and in some cases speed, but definitely accuracy, benefits from having more data. That is not a surprise, but it is really important. Then, to your point of whether we learn that we can decode things that we did not think we could decode before, I will say yes to that, but I do not want to go into detail.
Juan Benet
Interesting.
Ben Rapoport
For the future, but it definitely is an extremely exciting area for us, and more on that will emerge over time.
I will say that genomics is a good example here. This is something that we’re really trying to do somewhat within Precision and definitely external to Precision with our partners. If you think about what we can do with the data and what we can do to move the field forward in a federated fashion, we cannot do everything ourselves. And we want to position the community for success because everybody is going to benefit from this.
This really means thinking about what it takes to map the electrical activity of the neural code across people’s brains, across time, and across function. This is not as discrete and finite a problem in neuroscience as it was in genomics, but there are many analogies.
In genomics, you kind of have the elements of your genetic code, your C, T, G, and A’s, and all the annotations there and then you have phenotype. Phenotype could be traits or diseases. We have all kinds of things. In a sense, the phenotypes are infinite, or certainly a large finite number. Those are your annotations, and you’re trying to map sequence elements to phenotype. But that sequence is linear and fixed in time.
In the neural code example, we think of it as a two-dimensional dynamic code. It is two-dimensional and unfolds in time. What are the annotations? From an ML standpoint, you want to make sure that the data is high quality, relatively uniform in some sense, and that you can compute on it using primitives and techniques that are relatively familiar. But it also has to be annotated. You really have to learn against something.
For most of this conversation, the implied annotations were related to movement or language, moving my hand in a particular way, making a particular gesture, moving my arm. We can talk about making a gesture, but how do we define that? Do we define movement in three-space? Do we define it in some other way? But you get the general idea that there are some elements of movement that are the dynamic, in-time annotations of the neural data.
But there are also other annotations that we can provide. Those may be disease-specific annotations, like whether somebody is having a seizure. There are certain ones that we care a lot about. Speech and language are some. Memory, vision, and sensation are others. And there are many more.
Juan Benet
You can probably find precursors to certain problems.
Ben Rapoport
Yeah, exactly right. So degenerative disease: is somebody going to develop Alzheimer’s disease? Is somebody going to have a memory problem? You can think of any number of things that you might want to annotate.
Those may be transient states, like movement is a transient state, or they may be less transient states, like a disease state. Some disease states are very permanent and are there all the time, and some of them, like an epileptic seizure, are also transient. But maybe there are features of abnormal activity that are present.
So part of what we are trying to do by mapping this space is to map the electrical activity patterns, label them on the anatomy of where the electrodes are receiving data from the brain, and on top of that layer in what these features are, speech, movement, and so on.
And to do that in a way that is curated, well structured, and accessible to people. I think we are in the early days of that. We have to build some of that pipeline for ourselves, and we are building a very limited version of that for our core products. But we are starting to build that in a more general-purpose fashion with some of our collaborators.
I think that is a tremendously exciting resource for the community. And I think it will be something, if we do it right, that really will be as impactful in this space as the genome was in cancer, infectious disease, the understanding of human origins, and many other things.
Juan Benet
Yeah. I would imagine also the data will be able to be aggregated across many different device types over time, and it’ll just kind of contribute to higher and higher quality models.
Ben Rapoport
So I hope that’s the case, and I have a little less insight into that. I know that you guys at Protocol Labs have been interested in trying to feel a way forward for the field to collaborate in a way that allows multiple platforms to interplay. And I think that’s a good aspiration.
I’ve spent a lot of time thinking about how we can do that for data coming from the Precision system. I’ve spent less time thinking about how it could be done for a general-purpose device. But I think there are ways. There are some open platforms now that people use for multiple device types.
Juan Benet
Yeah. And this might follow the path of other ML modalities, where certainly the data from the same device will be very useful for being able to use that device. But there will be a set of applications that become possible or useful once you start aggregating of these different modalities.
Ben Rapoport
I definitely see it from that perspective. If you have data on a problem from multiple sensor types or multiple device types, you should be able to learn about that specific domain from the different sensors. It does not matter what imagery you use if you are taking pictures of the relevant problem.
Being focused on my problem, it is not always clear that data gathered with somebody else’s sensors in my area of the brain. The truth we’ve learned from a lot of what other people have done.
Juan Benet
I’m sure it may be the sort of thing that only turns out to be very useful down the road. So first we have to go through this large collection process and start using it for the shorter-term applications, and then maybe later it becomes really useful.
Ben Rapoport
Yeah. There are quite a few areas, medical imaging and genomics and well beyond medicine, where federated approaches to sharing data, harmonizing data sets, and making sure that annotations are uniformly applied in a high-quality way have been a huge boost to the field. All of modern ML basically has groups like that to thank.
Juan Benet
Yeah. And this might also be a case where the Precision technology can be used to collect tons of data that may not directly lead to short-term applications, where the data that you have is maybe not useful enough or sufficient. But then down the road, it turns out that you’re able to decode various kinds of information that people just didn’t think were at all going to be possible today with this type of technology.
Ben Rapoport
I would not be surprised. We’ve already surprised ourselves in that sense. Sometimes you do something by accident that you didn’t realize.
Juan Benet
Yeah. It seems to me there are areas of technology where, once you start opening a bottleneck and you get the ability to start instrumenting and get a lot more insight into the problem, all kinds of things turn out to be way easier than people expected. Certainly still super hard, but certain things just become possible that people did not think at all were possible.
Ben Rapoport
That’s definitely true. And I think one of the things, in a general sense, in biology, in cancer biology, infectious disease, and other related areas, is that we did not necessarily know it at the time, but it turns out those areas were really data-impoverished.
The data were very difficult to compute on. And now we have discrete, universally agreed-upon data standards. They are digitized and amenable to compute.
Neuroscience is now coming into that domain where what’s happening at a fine-grained scale in the brain is accessible. It’s accessible in a way that is digitized and can be computed on.
Up until five years ago, if you wanted to get your hands on a dataset of what’s happening in the motor cortex or in the sensory cortex when somebody is doing something pretty sophisticated or thinking, there were only a few datasets like that in the world. And now we have almost 50 of them.
So I think you’re right, and that will scale. I think part of what we’re doing here is, because we’re at the beginning of it, it may be hard to recognize, but part of what we’re doing here is digitizing neural data in a way that has never been possible before. And in doing so, making it amenable to compute, and making some of the problems that seem like biological problems, or even impossible-to-articulate or impossible-to-crack problems, into problems that can be framed as a computer science problem.
That’s what happened in genomics. That’s what’s happening now. And some things that are very hard in the biological domain, like developing a vaccine, in the computer science domain are tractable.
So I think that’s what we’re going to start seeing now. But we have to be deliberate about how we handle the data, curate the data, and make them available. I think you’re right that some of the things we do now, if we’re careful about how we do them, will open doors.
Juan Benet
Yeah, it’s super exciting. The next 5 to 10 years of development and application finding may turn into being able to interface with computers, AI, the internet, and each other better.
It seems like the Precision technology will be able to do more than just restore basic mobility and the ability to navigate basic computers and digital life. It is also arriving at a time when the nature of interfaces is going to radically change as AI becomes much more capable.
So this opens the door for a whole range of other applications and uses of BCIs, where it becomes less about, or in addition to, all of the repair use cases, and more about being able to use a different range of interfaces to interact with AI, or with each other, and so on.
There are a whole bunch of possible use cases that have maybe been explored in sci-fi and in various vision-paper-type settings. It really feels like we’re on the cusp of that range of use cases becoming possible.
How much do you think about this in the long term, and how do you think this might play out over the next 10 to 15 years?
Ben Rapoport
Yeah. I find that a little hard to predict, and maybe I’ll be criticized for not being imaginative enough. But the way I see what has happened in the past being relevant to what is happening now is this: if you think about the way electronics have interfaced with the human body over the last several decades, things that were avant-garde a generation ago are now just part of everyday life.
Examples of that are implantable cardiac devices like pacemakers and defibrillators. A generation and a half ago, that was science fiction. Then it became an ordeal, but something that was possible. And now you may have a relative, or maybe several relatives, who have such devices, and it is basically an office procedure. They have a computer that controls their heart, and nobody even knows about it. They forget to tell the doctor that they have one. And by the way, their cardiac data is being uploaded and downloaded by their cardiologist, and maybe even by them on a smartphone.
So that became pervasive over time, and more highly functional, and almost kind of a lifestyle change for many people. It is basically a lifestyle choice at this point. It used to be a major surgery.
Cochlear implants are another example, maybe one that is a little closer to home for brain-computer interfaces. They were initially for people with severe deafness or congenital deafness, really a very medical type of application. They improved dramatically, and a big inflection point that was very influential in my own life was seeing the deaf community accept that parents of a deaf child could make a choice to have a cochlear implant implanted in an infant just a few months old.
To me, that signified that the technology was so robust, reliable, and effective that even a community that knows it has its own culture and language and grammar and literature could nevertheless decide that parents could reasonably choose to have an elective major surgery in an infant. And now, by the way, cochlear implants are used as effectively as a hearing aid. You have people who decide to have a surgery to have a cochlear implant implanted to essentially augment their hearing.
That process, going from cochlear implants coming into the world in the 1980s to becoming acceptable for people with a condition in the early 2000s, was 20 years later. Now we are another 20 years later, and you have people of our parents’ generation, or even our own generation, deciding to have a surgical implantation of a hearing aid device.
So that is the path this technology has taken, from explicitly and only medical technology, maybe an avant-garde medical technology, to kind of lifestyle modification. And there are other examples of that. Glucose monitors are an example. I could point to quite a few.
For all of these, in a way, this is the body interacting with the digital world. They are not quite what we are talking about in terms of interacting with modern artificial intelligence and the internet and so on. But they are advanced implantable electronic technologies interfacing with the world and shifting from something that is a medical necessity to something that is essentially a lifestyle choice.
Could I see something analogous happening in brain-computer interfaces? One hundred percent. We are at the very beginning of this process, where no matter what we do, opening the door to that possibility requires showing safety and efficacy in a medical context. So the next five years are devoted to that. We need to do that safely, effectively, and responsibly in order to open the door to possibilities for what may come next.
So it is fun to have these conversations about what may be here in 5, 10, 15, or 20 years. But I think what we need to do today to make that possible is clear.
Juan Benet
Yeah.
Ben Rapoport
Right. So the speculation is important. It’s fun, it’s interesting, and I think some of it is what we probably want to be doing now, because there is ethics, data curation, and some fundamentals that I think we probably want to put in place. No matter how the field evolves, I think we will be glad we did it right at the beginning.
But yeah, I could envision that. And by the way, people’s concept of what’s acceptable also changes. I’ve said this many times, but I have two kids, and they know a little bit about what I do. To them, it’s normal. So the next generation, the people who are five years old today and who are going to be the young engineers, physicians, and computer scientists coming of age and deciding what to do, when there is already a platform for development in this space in 5, 10, or 15 years, their concept of what’s possible, what’s normal, what’s acceptable, and what’s taboo is going to be very different from ours.
Juan Benet
Yeah, absolutely. But it sounds like you still imagine that taking many decades to develop, whereas a range of other folks in the field, and I personally, think it might happen a lot faster because the different piece now is that we have a much more online world, where people are interfacing with these systems much more actively, like the diffusion of something like ChatGPT, going from zero to “hey, this is possible now,” to percolating through society, happened in a few months to a year and a half or so, depending on how you look at it. But now it is widely used.
Ben Rapoport
Totally. This is an interesting conversation to have, because the diffusion of different technologies is limited by different things. Something that is purely an internet-based software application can diffuse globally in weeks or faster.
Juan Benet
Yeah. And obviously, the whole process of making sure that a device is safe to implant, which takes a long study process and so on, makes sense. What I’m getting at more is the level of shift that humans might go through over the next 10 to 20 years as AI becomes more capable and starts seeping into our societies and economies. There are a range of people now who are starting to have very significant and meaningful relationships with AI companion or counterpart. This is becoming a phenomenon now. So we’re on some shift curve that might end up being faster. I’m just kind of pushing on that.
Ben Rapoport
Yeah. I think that’s definitely true. There is no question that there are predominantly software-based, and wearable- and portable-device-based applications of artificial intelligence that we are all experiencing. You and I both experience the impact of changing AI tools many times a day.
So in the wearables and software world, that is a definite reality. I think the question is how implantable brain-computer interfaces play into that. I think there is maybe a slightly different timescale involved.
But certainly, once there is a cohort of people with implanted technologies, the ability to improve what is possible using the technology is an economy of scale. This hearkens back to something we were talking about earlier in the conversation, which is that the more people we are learning from how to decode, the more everybody benefits from our ability to decode effectively.
So there are a number of these kinds of economies of scale that I think we will see. And certainly, when you have a hardware platform that people are using, whether it is wearable, implantable, or something else, then the software that lives on that, or lives partly on that, whether that is AI-based software or anything else, will impact their lives at a rapid scale, at a scale that we have not seen before in other areas of medicine. It will be more familiar to pushing out software updates.
Juan Benet
That’s a great insight. There’s this hard bottleneck now of establishing the first set of interfaces that can actually be worn at length, are safe, and work really well, and where the entire product experience of having this device implanted, used, and removed, all of that lifecycle, is really figured out.
And once you’re able to do that, then a lot of this turns into primarily a software problem or at least a range of applications become possible is just purely software problem.
Ben Rapoport
I think that’s accurate. Exactly what that looks like and how much time it takes is not totally clear. But I think we can learn some things from other implantable medical devices. The nature of what a brain-computer interface does is obviously different from what a cardiac device does. It’s different from what a cochlear implant does. It’s different from what a glucose monitoring device does. But the interaction between software and an implanted medical device can give you some insight into that. Once you’re on the platform, the ability to adapt, program, and change is dramatically different.
Juan Benet
What first got you into neuroscience? What attracted you about the field?
Ben Rapoport
Well, I’m kind of a child of the field. My dad is a neurologist who also has both a PhD and an MD, and he’s an expert in clinical electrophysiology. His career kind of came of age with the beginning of clinical electrophysiology, at the very beginning of when electrodiagnostic tools started to be used in patients. Of course, the link between electrical engineering and neuroscience goes way back, almost to some of the pictures that are on our walls here. It really goes back to the beginning of the 20th century. So the electrophysiologic nature of the brain and nervous system has always been a part of neuroscience. But until the 1960s and 1970s, it was more a part of research neuroscience than clinical neuroscience.
My father started his career as an electrical engineer and became a neurologist, and his specialty became, and still is today, clinical electrophysiology, at the beginning of that era in the 1970s. So I grew up with that as a backdrop to my life. And what was emerging when I was in my late teens and early twenties was next-generation electrophysiology, which today brain-computer interfaces are a part of.
Juan Benet
When you think about the field of neuroscience, or neuroscience and neurotechnology together, what are some of the grand visions that the field has been trying to chase? Which of those have come true and been realized, and which ones are still forthcoming?
Ben Rapoport
Well, I think broadly speaking, the interest of neuroscience has been to understand the human brain. That is kind of a grand vision, and the specifics of what that means can be boiled down to different targeted questions. But in a broad sense, that is the grand vision.
In clinical neuroscience and medicine, the question is often: how can you understand what is going on in the brain in a way that heals a brain or a nervous system when something is not quite right? That means you have to understand the brain in the normal state, and you have to understand the disease process or the disorder well enough to address it and treat it in some way.
My focus growing up has always been that part, really understanding the brain in and of itself is, of course, super interesting. It is endlessly interesting. I do not think it will ever stop being interesting. To me, it is the most interesting thing in the world. That has driven my entire life and passion.
But more specifically, trying to understand when something is not right in the brain or nervous system, how do we fix it? That has been my lens.
And so the grand vision changes a little bit in a generational context, as sometimes problems become more important to a generation for one reason or another, for historical reasons.
In the early 2000s, various forms of paralysis became very important in the national and international conscience, partly because people were surviving injuries that were not survivable in other generations. And partly it was a technology push. So sometimes it is a clinical pull and a technology push.
There are various forms of paralysis that exist today that stem from stroke or spinal cord injury, or diseases like amyotrophic lateral sclerosis, so-called Lou Gehrig’s disease, which is a neurodegenerative disease. So paralysis ends up being an incredibly interesting lens through which to try to understand the brain.
A lot of the way we interact with the world is through movement, even in ways that we do not totally understand, because speech is movement too. That became a road into the modern world of brain-computer interfaces. So one of the grand challenges is: how do we treat paralysis?
Juan Benet
And how did you first become interested in building technology to treat these conditions? Were you encountering a lot of these cases as a neurosurgeon and then thinking about the problem space and realizing, “Hey, actually we might be able to start building some devices that can help patients here,”
Ben Rapoport
For me, it actually started in reverse, and I think this is true of a lot of people in the field. In the 1980s and 1990s, it was both a pure science push and a technology push, and some of what was being discovered then opened up possibilities for treating disease.
That is true of the beginning of the field of brain-computer interfaces. In the 1980s, it really was not understood how movement gets computed in the brain, how it gets planned, how it gets executed, and how the brain coordinates fine movement of the hands, the limbs, and so on.
One of the major discoveries in the field at that time was that fine motor control is coordinated by populations of neurons, not by one neuron at a time, and not by one bulk aggregate of brain tissue, but by populations of neurons firing in coordinated patterns to basically tell the muscles what to do in a time-synchronized fashion.
That set of discoveries was made in the 1980s, and those mechanisms were elucidated at that time, partly with the help of being able to bring multiple tiny electrodes into the brain, simultaneously record from them, and begin to parse out what the time-varying signals from multiple tiny electrodes meant.
So that population encoding became the basis for a whole movement into brain-computer interfaces. Really, it was basic science at that time, the basic science of how the brain coordinates movement. And early on in that process, people started to envision that if we can understand this, maybe this is a way to treat paralysis.
Juan Benet
In that case, these sets of neurons that are firing together, are they firing in parallel through different downstream sets of connections? Or are they ending up being joined by other neurons?
Ben Rapoport
One of the nice things about movement is that it is kind of an isolated system in the brain. Much of what happens in the brain is feedback among multiple circuits that interact with one another in ways that lead you to ask what is the chicken and what is the egg. It is very hard to untie these feedback loops and really understand causality.
Of course, there are some nuances to all of this, but in the coordination of movement in the brain. People who are knowledgeable in this area are going to listen and criticize, but let’s simplify. There is an area called the primary motor cortex, or M1, and that is located in one of the hills of the brain, right around here. Neurons in that area of the brain basically project long axons that go down the spinal cord and make one synapse to another neuron that actually connects to a muscle.
Juan Benet
Then to a single muscle fiber?
Ben Rapoport
Basically, yes. If we’re getting really technical, they’re called layer five pyramidal neurons. They’re large neurons. They’re called pyramidal because they have a pyramidal shape, and they project long axons, a meter or so long, that go all the way from the surface of the brain down through the brainstem and spinal cord. In the spinal cord, they connect with another neuron, an interneuron, and that interneuron then connects directly to a muscle endplate. So that one-two connection is what gives rise to movement.
Of course, one neuron firing is not enough to yield movement of a muscle. It takes the coordinated activity of many neurons firing together, synchronized in time and space, to generate a muscle endplate potential that contracts a muscle. But basically, that’s how it works.
On the sensory side, the body understands when a muscle has moved, and on the planning side, it understands how to move muscles in time to yield the desired movement. All of that is pretty nuanced. But the basic aspects of how movement happens work like that.
Then you get into asking, how do you actually move in a coordinated fashion to move a hand or an arm in a particular direction? Do you do it in order to achieve a result, or do you do it in a coordinate frame? Are you really moving in terms of kinematics, or are you moving in terms of concept? There are all kinds of questions that you can ask.
But the nice thing about movement is that a lot of the action happens in this one clearly labeled part of the brain that we can access and study. We learned in the 1980s that it was groups of neurons that gave rise to these movements, and that by electrically listening in on what they were doing, you could get a sense of what movement was being planned and executed.
The motor cortex, because of what I just described, kind of does it more or less in real time. The planning happens hundreds of milliseconds before, and the sensation and feedback happen 10 to hundreds of milliseconds after. But the real-time aspect of what the brain is doing to move is happening in the primary motor cortex. So you can listen to groups of neurons to understand what the brain is actually executing in real time.
That was a breakthrough in science. The corresponding breakthrough in engineering, more or less around the same time, was that whereas up until then neuroscientists had been listening to neurons, groups of neurons, muscles, anything electrical in nature in the nervous system, one electrode at a time, one amplifier at a time, there emerged the possibility to listen with many electrodes at a time.
There were many technologies used to do this, and part of it was that amplifiers became more readily available. Neuroscientists have always kind of been physicists and electrical engineers all rolled into one. Whatever was the data science of the era was also being used. Whatever was the computer science of the era was also being used.
When my father was starting out, everything was a physical trace of a pen on a rotating wheel paper script. Digital storage did not become commonplace until the 1980s and 1990s. So the ability to really apply what we think of as sophisticated compute did not come around until the 2010s.
Juan Benet
Wow.
Ben Rapoport
Fast-forwarding to where we are today, if you’re thinking about how brain-computer interfaces got to where we are today, it’s really because of the availability of multi-electrode arrays, of which there are different technologies that we can talk about, and the ability to store and compute in real time on all of that data, which didn’t come around until the GPU was basically a desktop reality and easily programmed.
Of course, it began with some understanding of the physiology. That was the first piece to emerge. The enabling technologies came next. And then, as you mentioned at the beginning of all of this, understanding that there were tools here that could be used not just to study the brain, but to understand how to try to heal the brain or the nervous system when there has been an injury or an insult of some kind.
That in itself is not just one enabling technology. That’s a team effort of many scientists and technologists. And even beyond pure science and technology, there’s policy and other realities in the world to actually try to get something from feasible to real and part of the standard of care.
And for me, going back to your grand challenge question, the goal is to have this technology available as part of the standard of care in a way that really touches people’s lives and helps to heal. And I think that is a palpable potential reality now. I think we’re very close to it.
We’re engaged now in clinical studies and with real patients who are either being helped or helping to help others who will soon come after them. And to me, that’s an incredibly exciting reality of today.
Juan Benet
Fantastic. Through studying the brain and learning about all of the advances in technology that could enable a range of things, it sounds like you then had the perspective and insight, and surely many other people in the neurotech field also thought this way, that you could finally start producing devices that could really help people heal. So from there to actually building one of these devices, inventing it, building a company, and so on, there were lots of steps. You co-founded Neuralink. How did you then go from there to Precision?
Ben Rapoport
Always thinking back through it, you try to make sense of it in a linear fashion.
But as I mentioned a few minutes ago, if you think back to what was happening in the mid-2010s, the first applications of machine learning were really starting to hit the public consciousness. One of the first, arguably, was Google Translate. People do not talk about that so much now. They talk about AlexNet, images, and computer vision. But one of the first overnight changes that happened was Google Translate.
Google Translate used to be terrible, almost a joke. And then almost overnight it was completely revamped to the point where you actually had high-quality translation across multiple languages. Very quickly, that moved from text-based translation to almost real time. There were apps where you could translate spoken words. This happened in the mid-2010s.
The reason I use that as an example is because, basically, in brain-computer interfaces, we are really solving a translation problem. One of the most important applications of machine learning in brain-computer interfaces is solving a translation problem between the electrical, electrophysiologic data streams that percolate in your brain and mine and everybody else’s, which are a similar grammar, but with different words and accents and so on. Nobody’s neural code is exactly the same.
So if you want to have a device that translates that neural code and makes it able to control other digital systems, you need to translate the individual neural code of somebody’s brain into the control output that you want to use.
Juan Benet
That whole generation of machine learning, including the transformer is critical in enabling this tech.
Ben Rapoport
Transformers didn’t even exist back then. At least not as, we think of it now.
Juan Benet
Certainly not the scale of transformation that we have now.
Ben Rapoport
But what was happening was that the last piece of the technological puzzle to fall into place was high-quality compute with an understanding of basic machine learning models that were obviously applicable to the translation problem that is neural decoding.
And with that, it kind of dawned on a number of people simultaneously, including Elon, that there was an opportunity to take what was a solution in the laboratory and make it into a technology that could actually do good in the world, and also perhaps have something to do with the way humans interacted with artificial intelligence in the future.
I think once people started to see a clear exponential growth in the capabilities of artificial intelligence and the speed of development of artificial intelligence, a number of questions emerged. One of them was, how is the human brain going to interact with artificial intelligence?
And that is still, as you know, a question that a lot of people ask with nuance and thought. What does that look like today, tomorrow, in five years, in ten years? And how does the technology we use to interface with artificial intelligence, the human brain interfacing with artificial intelligence, change over time? Is it fundamentally different from the way we currently use human-computer interfaces? Is it conceptually different? And I think there is this intuition that it is going to be very different.
So a number of groups at that time, back in 2016, Elon, Mark Zuckerberg, Brian Johnson at Kernel, and a bunch of others, decided to invest in trying to bring those emerging technologies out of the laboratory and into the real world, and to put significant investment dollars into doing that.
And I think there was also a consensus in the scientific community, which was small at that time, that the technology was as ready as it could be in an academic sense. A lot of the problems had “been solved”. And I say this with a laugh because it is never really quite the case.
Juan Benet
I describe it as R&D pipeline myopia, where no matter what part of the pipeline you’re working on, whether it’s the basic science, the invention, productizing, or even selling it into the market, the people working in that area encounter such large challenges that it feels like that’s the hard part. And that everything else is implementation detail.
But all of these parts are actually really hard. Doing the basic science is super hard. Doing the core invention is super hard. Doing the engineering is super hard. Building really high-quality products is super hard. Figuring out the strategy to deploy into the world is super hard. It’s all hard. One of the core things that I think ends up causing these super important technologies to be built and scaled is people who are able to span multiple segments in the pipeline and do the actual translation work.
Where this kind of breaks down is when you have such a division of labor that people do not span those areas well enough, and then things get stuck. There’s a lot of important science that is stuck in paper form, where the “hard problems of science” have been figured out, but there is no invention or downstream technology being built.
So by having that span over the R&D pipeline, you’re able to pull things forward, and you have the depth of knowledge of both the science and the technology and engineering to actually figure out what the core problems are to solve. So in that time, in that era, there were a range of people figuring out whether we could take this from the core science, and from the stage where things were, to now starting to build initial versions of products.
Ben Rapoport
“All the hard problems have been solved,” like you said, and all that was needed was capital to take it to the next step. That was not so far from the truth. A lot of really important problems had been solved.
Around that time, quite a few people who had been engaged in the science of brain-computer interfaces, which was a very small community then, much bigger now because of everything that has happened over the last ten years, pretty much everybody knew everybody, if not by one degree of separation then by two.
So if you were one of those people trying to get something started and bring it out of academia into the real world, it was not so hard to locate people who could help get things going.
Those of us who were a little younger at the time, and not established professors or established professionals, and at that time I was also single, the people who were most mobile, highest energy, and newly minted PhDs or professionals or engineers were the likeliest people to help get something going. That sort of describes me at that time in my life.
Of course, I was incredibly passionate about it. I definitely believed, as I do now, that there was something incredible to be done in brain-computer interfaces. And maybe I had, like you said, a slightly different skill set to bring to bear.
It obviously takes a huge team with a variety of different areas of expertise. There is the core engineering and the neuroscience, then there is a clinical understanding of how the systems work, and there is how the experimental neuroscience works, how you design studies around it, and how you scale things up.
There are so many aspects to how a team needs to be built to bring something that is a proof of concept into a scaled reality.
So yeah, that’s how it started. I was headhunted, and I was lucky to be headhunted and to join an incredibly talented team that grew very quickly.
I think it also became clear that the space was so huge, and the potential to do really high-impact things in the area of brain-computer interfaces was more than any one group could do with any one enabling technology.
So pretty early on, I also had a sense that to really scale the technology, to access multiple areas of the brain, to study those areas, and to deliver the technology to patients who already had some compromise to the brain, which itself is pretty delicate, the trade-offs you wanted to be able to offer had to emphasize, in my view, at least for some portion of the market, a technology that didn’t penetrate the brain but was also incredibly high in spatial and temporal resolution.
And so that’s how Precision was born. I left Neuralink in 2018, and basically in 2020, Michael Mager and I, who had known each other quite well before, got back together and started Precision with that vision of really bringing neural interface technology to the medical community to treat neurologic diseases that currently don’t have a treatment, with an emphasis at the beginning on paralysis, because that’s the clearest, most well-defined need, but with a view toward doing much more.
Juan Benet
What’s the long term vision of precision? What is the range of things that you wanna be able to do in the long term?
Ben Rapoport
I would say I want to be very concrete. We want to have, in the next five years and beyond, an effective technology for, I cannot exactly say treating paralysis, but something to offer people with various forms of paralysis to restore lost function in a really meaningful way that brings people who are not currently able to engage in the world in the ways that you and I take for granted back into that world.
For some people, that is about communicating with other people. For some people, it is about financial independence. For some people, it is about different things. But what we know we can do is deliver seamless integration with the digital world that, for many of us, is a major part of the reality that we take for granted. And for people with forms of paralysis, it is not.
So that is our goal over the next five-plus years. Very severe forms of paralysis, like spinal cord injury, will be the first that we address. But there are many forms of paralysis, and that includes paralysis caused by stroke, which is not always whole-body paralysis. It can be semibody paralysis, or it can even affect parts of the body. And there are other diseases of the nervous system that cause paralysis.
We have developed, over the last few years, a much more nuanced understanding of how forms of paralysis affect people in their lives, families, and communities, and the people around an individual who suffers from a paralyzing condition. So that is our five-plus-year goal.
At the same time, we have discovered that the Precision technology represents a platform for understanding and studying the brain, and for interfacing with not just the motor areas of the brain that we started this conversation on, but really, essentially, the entire brain.
We have now implanted the Precision technology in more than 50 people, and the electrode technology has touched parts of the brain that almost no other single-electrode technology has touched, everything from motor and sensory areas to prefrontal cortex for decision-making, to inside the valleys, inside the sulci of the cortex, which is the thinking part of the brain, the brainstem, the spinal cord, and areas of the brainstem that are very difficult to address.
So what we have discovered is that we have basically built a platform for discovery, diagnosis, and intervention. I think it is definitely the case that we cannot even envision today. We have a pipeline of use cases that we are working on, but I think that the possibilities are very significant. I won’t say endless, but like very significant. And we want to enable others to build on that platform.
Juan Benet
And maybe as you look ahead into the future, what is your optimistic vision of the future? The stories that tend to be told most often have high drama, which is why popular fiction ends up with highly dystopian future worlds, because that sells much better. So it is kind of up to us, and a lot of other people, to paint really positive visions of the future so we can aim for them.
What are the kinds of things you think about when you think 30, 50, or 100 years out? What is possible that you are working toward, that you see other people working toward, or that you want to inspire people out there to work toward?
Ben Rapoport
Let’s try to project a generation out, even though that is hard for me. If I think about what I am able to work on relative to what my father was able to work on, and what I think my kids will see, I think we are going to change the nature of what it means to have certain kinds of disability in society and in the world. I think that is going to be incredibly powerful.
Exactly how we change that, and exactly what we make possible, is hard to say. But just as cochlear implants have profoundly changed what it means to be hearing impaired, I think brain-computer interfaces are going to have a profound impact on what it means to be physically disabled in society. That is a massive change.
I think we are going to see that in the next decade. Are we going to cure all forms of paralysis? No, not in 10 years. But are we going to change that significantly? Yes, I think in the next 10 years.
Also, in 10 to 20 years, we are going to have brought forth a very powerful technology for understanding how the brain works. I think some problems that seem hard now are not going to be problems people care about anymore in 10 or 20 years, because they will be taught in grade school. That has been the case with genomics, and I think it is going to be the case with certain problems in neuroscience.
Some of the things that we think of as profound, like questions of consciousness and questions of neural computation, are just going to be things people understand routinely. Even my kids know how to query some of the AI software now.
So I think it is going to be an amazing step forward that some of this technology unlocks. I think there is going to be a big impact on disease. There is going to be an impact on understanding medicine and neuroscience. And whatever application layer is built on top of that, I think part of that is for us to enable, and part of that is for another generation of scientists and engineers to contribute to.
But I have a very optimistic view of the future, and I really do not worry too much about dystopian future states of neurotechnology. There are a lot of thoughtful people trying to build this in an appropriate manner, and I think we are going to see tremendous strides forward in a number of areas.
Juan Benet
Thank you very much. This was a phenomenal conversation. Thank you for spending the time and good luck with all of the work you’re doing.
Ben Rapoport
Thanks for being a partner in this and thank you for having me on the show.
Juan Benet
I hope you enjoyed this episode. This is a new podcast, so we need your help to get the word out. Like rate and subscribe on your favorite platform and please share it with people you think would find it interesting. Thank you and see you next time.
