Biocomputing on human neurons

Today we’re joined by Ewelina Kurtys, or Kurtys… Or Kurtys…

Ewelina Kurtys. Nice to meet you.

Yes. Third time’s a charm. A scientist-turned-entrepreneur with a PhD in neuroscience, with 20-plus peer-reviewed papers. So you’re the real deal, Ewelina. Real deal.

Wow.

Thank you.

You’re welcome. Thank you for showing up, coming on our show and talking about… Neurons. Neurons. This will be an interesting conversation. I’m a little bit out of my league here, if I’m not going to lie… Because I saw on your website, finalspark.com, on the neural platform page it says “Instant access to human neurons.” And I was like, “What does that even mean?” I have no idea what it means. So please, demystify a little bit that, and we can dig into the science as well.

So that means that our lab is available remotely. So everyone from all over the world can access our laboratory. Through the website browser they can log in and they can write Python code to do experiments, because everything is connected to real neurons. Because we are trying to build computers using living neurons. We want to use neurons as a processor, because they are very energy-efficient… So that’s the reason. And at the moment it’s still R&D, of course. We don’t have these computers yet. But for the moment, it’s possible to do experiments only, to try to program neurons. But it’s not possible yet to process information like images, or sounds, or videos… But we hope to do this in the future.

So we recently had Greg Osuri on the show, talking about the AI energy crisis and all these ways that we can potentially power this new compute demand which is burgeoning… And it sounds like those ways were hard, and maybe if we figure this way out, it’s way better. Exactly how much more energy-efficient is it to compute on neurons versus silicon?

So neurons are one million times more energy-efficient. Of course, this is all an estimation, because we can have some idea about this by looking at the human brain, which is built out of neurons. And we can have some idea what will be the efficiency of the processor.

Okay. So when I think of a platform where you’re provided instant access to human neurons - well, I just added the word ‘human’ in there. Maybe they’re not human.

No, they are human. Absolutely.

They are human. So I’m thinking about a bunch of brains floating in water, or some sort of formaldehyde…

No, actually, there are no brains…

No brains. [laughter]

I told you I’m out of my league here. I’m just – everything science fiction is coming out of me here.

No, sometimes you can see on social media such pictures of brains enslaved in the lab… But that’s not what is happening.

Okay. Phew.

We are just using the same building blocks, which are in the brain… But it’s like bricks - you can build a house, or you can build something else. So we just use these building blocks, but we don’t want to build brains in the lab. We would like to build computers, which would be totally different; probably much, much bigger, because we imagine these neurons can have huge structures in the lab, as we don’t have to make it so small as a human brain.

So these are only building blocks. So we are not trying to reproduce a brain. It would be actually very difficult and impossible at this stage, actually, of science, because the brain is very, very complicated. There are a lot of little structures, so we don’t try to make this. We just use living neurons. And they are human neurons, indeed, and they are derived from the human skin. So you can reprogram the cells of the skin so that they become stem cells. And from this, you can have, theoretically, any cells you want.

[00:08:01.21] Okay. So no brains, but human skin cells, and the neurons that are in them.

No. Skin cells, which later become neurons.

Okay, they become neurons. Adam, do you know any of this stuff? I’m over here like a… Imbecile.

Kind of. I mean, what we know about human anatomy and why there is so much curiosity, and why she’s studying neuroscience, and how this science fiction era is just simply that our brains can compute so well, with such little power requirements… That’s why there’s the lore.

Twenty watts is what I read. Twenty watts to power the human brain…

Right.

That’s nothing.

Very little, comparative to ChatGPT, or something like it.

Yes.

Dramatically different.

And to simulate a human brain, you would need a little nuclear plant. So…

So you don’t need the full cognitive brain… So – here’s what I understand ,about the brain at least. Tell me if this even maps to the science that you’re doing to discover this stuff… It’s that you’ve got this humanity, which is your frontal lobe; that’s what helps you have rationale, reasoning etc. If I don’t have my frontal lobe, I’m angry Adam. I’m not nice Adam. I don’t make good choices, I make very poor choices. How do you get to this level of compute without the full brain? How are these cells able to do so much without what I would typically call like the human brain, I guess?

Well, so the human brain is actually for many things, not only thinking. It also runs all our body, it controls everything… So that’s not always necessary for the computer. What is the most interesting for us is indeed this cortex part, which is responsible for thinking, for processing some abstract information. So we are most interested in this.

So we would like to, in the future, process information through the neurons. Information only. So we don’t try, for example, to control a human body, or stuff like this. So there are a lot of things in the brain which are not really related to the biocomputing project.

So you’re effectively using the neurons just as logic gates; you’re just doing ones and zeros at the end of the day. They’re not doing –

Well, yes. We would like to try to play, to reproduce the logic gates. However, neurons work totally different. And that’s why it’s so difficult actually to build the computers, because indeed, in the computer you have zero and ones. And this is one of the reasons why actually they use so much energy. But the brain is encoding information totally differently in space and time… So when we have neurons in our head, it matters when and where (exact location) they are active

And this is information. So this is a totally different type of encoding. So no zero/ones, actually, but there are a lot of ways how we can look at the activity of the brain. For example, how often you have spikes, or what are the time in between the spikes… So this electrical activity of the neurons. So we know for sure it’s totally different. And that’s why this project is so difficult, because we have to figure out a totally new way of programming, a totally new approach. It’s the same actually as in quantum computing. It’s the same situation, that you have totally different hardware, which is working differently… So it’s necessary to figure out a new way of writing algorithms. And that’s why it’s so difficult, because actually, someone has to come up with some idea which would be totally, totally different.

But indeed, at the moment, when we do research on living neurons or sometimes on some simulations of neurons in silico, people usually try to follow the rules of digital computers, like try to reproduce logic gates… Which is actually not really correct, but that’s the best what we can do at the moment.

[00:12:09.14] Let me see if I understand this… I’m grokking some of the stuff from what you’re sharing, and then also from your very awesome website, finalspark.com. It says they transform stem cells into mini brains, that learn and adapt, growing neurons in an orbital shaker - which I have no idea what that is. It sounds so cool…

It sounds cool, yeah.

…over a three-month period. And these mini brains, organoids - I’m not sure if that’s a term you all came up with or not, but that sounds cool, too…

It sounds [unintelligible 00:12:37.09]

… of 0.5 millimeters in size, with about 10,000 neurons that function as real brain tissue. So you’ve found a way to take stem cells, grow them over a three-month period… They used to have a half-life that was even shorter, like a few hours. Now you can actually – they’ll live 100 days. And they get connected to this neural platform with 24-7 access. So essentially the same way we treat a CPU in AWS, you’re doing with stem cells, turned neurons, turned mini brains, turned organoids… That can be compute platform.

Yes, absolutely. Although now it’s for experiments, so we cannot really process information the same way as in digital. But yes, it’s available remotely, and we imagine that actually in the future our lab or our biocomputer will be available remotely as a cloud service today.

Right. Not quite AWS yet, but working your way there.

Yes, absolutely.

How did you get involved in this? Where are you coming from?

So I come from Poland. I was always on the medical side, let’s say. I studied pharmacy and biotechnology, and I always wanted to be a scientist. I enjoy a lot working in the lab. I was very fascinated by cracking my brain, teasing my brain with some ideas and challenges… So I always wanted to be a scientist, and I realized at some point that the brain is the most interesting part to study… So I did a PhD in neuroscience, I was working on brain imaging… And later, when I moved to industry - because I always wanted also to see what is outside Academia, outside this academic world… I started to work with startups on actually – initially on imaging. And then there is a lot of AI in the medical imaging, in industry, so this is how I learned about AI. And then I became fascinated by that, and I started to discover which opportunities it brings, beyond imaging. So I started to work on commercial applications of artificial intelligence, and after, I started to work on the next frontier of AI, which is actually closely related to neuroscience, so on biocomputers.

Can you talk about the imaging? I think you mean – when you say imaging, you’re probably referring to like MRIs, like brain scans… Is that right?

Yes. So actually, I did my research on positron emission tomography. This is something you do using radioactivity. You put some radioactive substance in the body, and this substance goes to some specific places in the body, and you can detect this non-invasively. So you can get the picture, for example, of the brain, which parts are active, for example… Or you can visualize some receptors without opening the brain.

Right.

But it’s actually similar to MRI. MRI is just a little bit different… So you don’t use the radioactivity, and you can see a little bit different things. But the idea is always the same, to look inside without opening the body.

[00:15:58.06] Right. Yeah, the MRIs are a little different… Which one’s more accurate? Is the imaging or the MRI more accurate? Does it matter?

Always imaging. I’m not sure it’s a good comparison, because I think it really depends on the protocol… Because there are different types of MRI, and also there are different types of PET, so it really depends. It depends on the parameters. And also, they measure different things. Because PET is always functional. So when you use the radioactivity, there is always some chemical substance, like even glucose. Everything is actually a chemical substance. Everything that is flowing in the body. So you always observe some process, a biological process. And in MRI, it’s not always like this. Sometimes you just observe the tissue, when you have different tissues, and it’s static. It’s not always functional. It’s not always [unintelligible 00:16:54.09]

Right. This imaging – that’s why I asked this question, because this imaging is really kind of like the rage, I would say…

And my version of the rage may be way different than your scientific version of the rage… But what I mean by that is that there’s a lot of study around mental health, ADHD, ADD, trauma - you name it - that folks are trying to image brains in these scenarios. Is that kind of what got you into this curiosity of like how the brain operates from different trauma levels, or different prescriptions, or descriptions of health concerns, mental health concerns, whatever it might be? Is that what got you interested in this imaging process to understand more clearly how the brain reacts to, I suppose, life?

Well, actually, I was working on something a bit different to what you’re talking about… Because you say about different activities of the brain, during different maybe diseases, or maybe different tasks, cognitive tasks… But I was actually working more on inflammation. So I try to visualize microglia. Microglia are a type of cells which are around neurons in the brain. So they actually take care of the neurons. And sometimes they become very activated, which means inflammation… And it’s believed that this process is actually involved in many neurodegenerative diseases, and also depression. So when you have an inflamed brain, you can develop some disease.

Yeah. Alzheimer’s… That’s interesting. Yeah. Inflammation is like the number one issue for most people.

Yes.

They get inflamed everywhere.

Yes, my research was actually about the effect of nutrition on inflammation… And it’s funny, because I started to put attention on everything, what I eat, after I started to do this research. It’s really interesting, because I started to look totally different on my groceries… Because actually, diet can be pro-inflammatory or anti-inflammatory. It’s very important what you eat. And it can affect also your brain health.

And I think now, after – it’s maybe almost 10 years since I did these studies… Now I see that there is more and more – more and more people are talking about this. About an anti-inflammatory diet, about how much is important, what you eat, for also your brain health.

How did that lead you into discovering – I mean, it kind of seems obvious, but how did that lead into AI and your discovery there? Were you leveraging trained models? Express how you got curious about AI.

Actually, I started my first job in industry. I started in the company which was doing medical imaging. And actually, it was a very good start, because there was at least one thing which I understood at the time… Because I had absolutely no idea about how companies work, and anything about this industry world. So there was at least one topic which I understood well, which was medical imaging.

[00:19:56.24] And this company was doing a service of analyzing images from different medical studies, and they talked a lot about AI. Because when you have imaging data, when you have a high number of imaging data, you can analyze them automatically. In some way, you can use AI for that. And actually, that’s the way how I learned about artificial intelligence. Because actually, when you go for different industry events, you see people talking constantly about AI… And I was very lucky also because at that time I was in London; this is a very good place for learning new stuff and for networking. So I could get a lot of exposure and to see what people are doing with AI. And I could discover that there is much more beyond imaging. So that’s why I started to be interested with anything, what you can do with AI.

Well, being able to scan a lot more - no pun intended really, but just grasp a lot more of these imagings that you’re doing, to see the anomalies and see the connection points that you can’t really see individually… I mean, that totally maps to me, because the more you can see across different scans is good.

Yeah, this is a general thing about AI, because it can see much more than us, and can scan a lot in a very short time.

So how long have you been working on this problem?

On FinalSpark I met the founders in 2019, at the conference in London, so I started to work with them, initially on some other projects… So actually, on FinalSpark I could say I’m working like three years, around.

Okay. And there’s a platform right now for experiments… You’re hoping to get to compute down the road, and a service for that… Is there a straightforward path towards that, or are there like breakthroughs that still need to happen to get from where you are right now to where you guys want to go?

No, it’s a very difficult, very challenging project. That’s why we expected to build these real computers in around 10 years. So it’s a bit of a challenge when we talk with potential investors… Because it’s quite a long-term project, and it’s very, very difficult. Because nobody knows how really neurons encode information. So this is the biggest challenge. So we know that neurons are active electrically, we know they are spiking… Spike means that there is electrical activity. And we know quite a lot about this, how it happens. However, we cannot really translate this into some specific information. So for example, you have text or image… Maybe about imaging there is some understanding already in neuroscience, but for example when you have words, text, it’s hard to say how some word can translate to specific activity of a neuron.

So at the moment, as I said, many people do a lot of random experiments. Also us, we do a lot of trial and error. So this is why actually we built an automated laboratory. Initially, the idea was to just be able to do as many experiments as we can. And also, a lot of research on neurons are often inspired by what happens in the digital world, which is not really correct, because neurons are working totally differently… But it’s still, at the moment, the best you can do. So this is the biggest challenge, that we don’t really know what activity of neurons mean.

And also, another thing very important is that brain, or neurons, any kind of form of - also our neurons in the lab - they are not stable systems. So a computer you can consider as a stable system. It’s a dead matter, so it works today in some way. Tomorrow it will work the same way. But the living tissue is not like this. It can change, so the dynamic inside can change.

[00:23:54.04] So for example, today we do some experiments, we send electrical signals to neurons and they can react in one way, and tomorrow they can react to the same signal totally differently. So that’s also a big challenge. The fact that living matter is plastic, so it changes behavior, actually also like us and our brains. We also change. During time we can be completely different people.

Can you talk about how you get them to compute?

This is actually a challenge. So what we do - we try to send them electrical signals. Because neurons are placed on the electrodes - you can see this on our website, finalspark.com. There is a section Live, you can see the readout. So we send them electrical signals and we measure how they respond. So how they change the activity. How they change electrical activity. Another thing how we try to compute neurons is also by sending them some chemical signals. At the moment, we can send them dopamine or serotonin. So programmatically, we can program in Python that neurons will get dopamine at some point. So at the moment, this programming is not really to do some specific task as with computers, but actually to change the behavior of neurons. So this is actually the first step. So we want to be able to consistently control how neurons behave. So how is the electrical activity of neurons.

So you may have one lazy organoid and one very non-lazy organoid.

Productive. Yeah.

Yes, absolutely. It is biological tissue. Sometimes it can vary. Sometimes they can just die. So we are still learning… And yes, so there is also variability. Yes, absolutely. A lot of things. It can be also a lazy organoid.

Yeah. Or depending on the day. Like, yesterday it was really productive and then today it’s lazy.

Yes. As I said, every day is dynamic. It’s not a stable system. However, we have some success. It’s not only so bad. We were able to store one bit of information. So just to give you an idea about the stage at which we are - we stored one bit of information in neurons. That was quite consistent. And we were able to reproduce this many times… So we are happy there is some kind of progress. But yes, it’s very challenging to get something.

So when you store a bit of information…

Yes.

How do you read it back out again? Or how do you get it back? Or how do you know that it’s stored?

Yes. So actually, that’s quite technical, because I can tell you every blob of cells - because there are such a blobs, these neurospheres, organoid, they are 3D structures… And each is placed on the eight electrodes. And all these eight electrodes, they measure activity from neurons. And depending on how strong the activity is at each electrode, you can mathematically calculate something, what is called center of activity. So this is quite a hard science approach… And we were able to shift the center of activity. So yes/no. That was one bit of information. And this is quite complex, as you can see… But yes, we were able to have this consistently, this kind of results.

Consistently across different neurons and across different times?

Yes. And different days. Because that’s always – in general, in bioscience, every time when you work with biological tissue, this is important, that you have to be able to repeat. Because many things work once or twice, but it’s important to be able to repeat your results. Yes, on different days, on different neurons.

Mm-hm. And is the process slow?

[00:27:59.25] Well, to be honest, I cannot tell you how much time it took… No, actually, I don’t know. But I guess it’s in seconds or milliseconds. But I don’t know. But I can say that, in general, neurons are slow, and in general, neurons will be good for tasks which don’t have to be fast. Because also, when we look at the human brain, and we look at computers, we can see that computers are very good in speed. In doing repetitive things - very, very fast. And we will never be able to compete with digital on that. However, the brain is better in complex tasks, because we can solve complex problems using very little energy. So that’s where is our strength. But definitely, speed or, for example, memory is not something where neurons are better. Because also, when we look at our brain, they are very limited, actually. A computer can remember 20 books very easily, and for us, it would be difficult to remember every word in 20 books. But for the computer it’s easy.

What does this wetware look like? I’m programming my Python experiment, and I’m sending it over the internet, I suppose, or some sort of VPN connection to you guys…

Absolutely.

And so, of course, it’s going over copper wires, and Wi-Fi, and whatever backbones, and then back into your interface, which eventually translates it into… I’m imagining there’s a needle at the end of a thing that like sprays some dopamine… I don’t know what happens. What happens at the end, the last mile of this API?

Yes. So when you send an electrical signal, you have a digital to analog converter. So you have to translate the things from digital world to analog world, because neurons are analog.

Right.

So you have this digital to analog converter, and it is translated into electrical signals, which goes through the electrodes. So basically, the stream of electrons are going, flowing through the electrodes. And when you want to send a signal with dopamine or serotonin, then it’s connected to the lamp. So we have an UV lamp.

Okay.

And when the UV light is open, the dopamine is released… Because dopamine is closed chemically. It’s encaged in the chemical, so that it’s not active. But when it sees the UV light, then it’s released.

Oh…

So it’s a way of releasing very, very quickly dopamine to the neurons. So this is how it works. So then it’s connected to some controllers, which are connected to the lamp, and then the lamp switch on.

Okay. Same thing for serotonin?

Yes. But for serotonin, we have different wavelengths. I don’t remember which one. It’s not UV. But then we have different wavelengths, yes… So that they don’t overlap. So you can have both in the medium. And of course, we plan to have more of this, and of course, much more neurotransmitters… But we started with dopamine, and now we added serotonin.

Are those hormones? Are those chemicals? What is the proper terminology to call dopamine and serotonin?

No, they’re neurotransmitters. So you have several in the brain, and they affect learning. And actually, the whole idea of using them is because we use them for feedback. Because, actually, the way how humans are learning is by feedback. So you have interaction with the environment, you get feedback. So things are going good or bad, and then you learn if you should do this or not. And the same way, actually, neurons are learning in vitro, on the very basic level. Because for example, when something good happens, then there is a dopamine release, and that reinforces the connection between neurons. So if they have done something good, then it kind of reinforces this behavior.

[00:32:02.02] So the idea - at least our idea here, because it’s a bit complicated… And actually, there are different opinions about how to give punishment or reward to neurons… But our idea is to give dopamine as a reward. And no dopamine as a punishment. So that’s used for the feedback loop. So for example, you stimulate neurons with some electrical signals, you measure the behavior… For example, you want that they increase activity. So if they do this, you give them dopamine. If they don’t do this, you do nothing. And then you send an electrical signal again, and there is such a loop, over and over, and you see if they’re learning.

And these neurons learn.

Well, that’s the problem - sometimes they learn, sometimes they don’t. This is still a challenge, you know. This is still a challenge, learning. So learning for neurons is changing the connections, changing the behavior of neurons, so behavior will be electrical activity… And this is still a challenge. It doesn’t always work.

Break: [00:33:04.00]

Remind me… Dopamine is positive, so that’s used for rewards… What is serotonin used for? What do the two levers do?

No, actually, that would also be for the –

Two different versions of reward.

Yes. However, if we have to go to the details, it’s a little bit tricky… So we are still –

Okay… Let’s go into the details. Let’s get tricky.

[00:36:11.12] Yes. Because actually, dopamine is – it also depends when it is given, and also there are different receptors. So receptors are on the surface of the cells. So if they have dopamine receptors, that means they can recognize dopamine, actually, because they need always a receptor to recognize a neurotransmitter… So it’s a little bit tricky, because there are different types of receptors, and different timing… Sometimes it’s milliseconds, or microseconds… So the timing also is important for cells. But we assume that dopamine is a reward.

So it’s not stable yet.

No.

Some days they do, and some days they don’t… And you’re learning. So this is 10 years before it’s usable in production. This is total lab learning… What is it that – and maybe you’re still early and you can’t answer this question, but what is it that makes it such a variable? Is it just because it’s bio, and we don’t know what we don’t know?

Yes, because it’s bio. Because first – there are two reasons, I would say, main. First is because it’s bio, so it’s unstable. Second is because nobody knows yet how neurons encode information. This is totally different than digital. So this is such a challenge, because you have to understand a new way of programming.

But you do have some indicators that they at least generally work the same.

Well, you mean neurons?

Yeah. Well, if each neuron was like a snowflake - you know, every snowflake is unique, and it melts, so it changes… Then there really would be no – like, 10 years, 100 years, a billion years, there would be no getting there, because there’s no determinism at all. Because everyone could just work completely different every time you prod it, you could never get information – but you’ve actually gotten a bit back out again, so you have proven…

You know, I think it is deterministic. It’s just that we don’t know the rules yet.

Right. That’s why I’m saying, you do have an indicator that they do kind of work the same generally, though… At least for this one thing.

Yes. But the indicator is our brains, actually… Because we have no doubts that neurons can process information very well. This is why we can talk.

True.

So we can say nature is a proof that neurons are working.

That’s fair.

There is no doubt about this.

We think differently, but we all think, I guess…

We just have to learn how to program them.

Have you tried telling them to ultrathink? I’m sorry, that was a joke…

[laughs] Good throwback.

That was from a previous show.

I have been using that, by the way… I’ve now said – sorry for a slight aside… “Triple check and ultrathink.” That’s my new phrase.

That’s the key word, Ewelina, for certain AIs…

Triple check that stuff and ultrathink.

When they’re not thinking best… But of course, with a neuron, maybe you just keep it analog and just whisper to it. “Ultrathink…!” Just walk up to it and whisper.

Yes, you can whisper, but they don’t have ears, so they can only understand –

Well, you need some ear cells. Get some ear cells going.

You have to translate, because usually our ears also translate to electrical signals.

True.

That’s why our neurons in the head can understand. So you have to learn. That’s the whole point - how to encode information so that they can understand.

Yeah, so you guys are just running – I imagine you’re just running experiments nonstop, right? Because you’re trying to figure out how these things work.

Yes, absolutely. And we are actually constantly building, because we have done huge progress since we started. We built a whole laboratory, a very stable system for working on neurons. And also, now it’s available remotely, so we are also busy with many users from all over the world… So we invited nine universities from different countries to work with us. They have access to our lab for free, to study also neurons. And we also have first industry clients who pay us to get access to our lab. So we are also busy with this.

[00:40:13.22] Really?

Yeah. We didn’t plan for this, but people started to write to us that they would like to try, that they would like to get access to the lab… And yes, now we have two types of subscription, and we have users who are coming to us and testing neurons.

Is this the Betamax versus VHS all over again, in terms of quantum computing versus - would you call this bioprocessors? How would you frame this? Because it seems like you’re both trying to solve a similar problem.

Bioprocessor, very good. Or biocomputing. We call it biocomputing, bioprocessor… So no, I wouldn’t say it’s in competition, because this is a totally different mechanism, different things. So we know quantum computing actually is very fast, and it can maybe be good for encryption of information… So it is a totally different type of task. So I’m not sure it will be in competition, but…

Okay. I was thinking more like one may win, or one may actually prove to be fruitful in terms of viability… That’s kind of what I was thinking like.

Yeah, actually, I think that the future will be that we will have very different type of hardware, because generally, you can see this kind of direction. It’s not only quantum, not only biocomputing. People are also working on many specific chips, also digital, which are optimized for some specific tasks… So I believe that we will have variety. So today we have mostly CPU, GPU, and in the future we will have hundreds maybe of different chips, I believe so, which will be optimized on some specific task.

Jerod, I think this was on a #define where we talked about this, but do you recall talking about slime molds, and subway systems?

Yeah, like a couple of years ago.

Yeah, just this really – it was recent. I want to say in the last year… We were talking about the concept of slime molds being very sophisticated.

They use slime to design the subway systems, or something?

Right. Routing, essentially. Like, efficient pathways to X. And they compare that to subway systems, and the way we route, which is more like cause and effect, really. We’re very reactive… But it’s very similar in terms of like bio. You’ve got this intelligence of sorts. Not intelligence like it’s got a body and it can come fight you. Slime is not gonna do that. But it can do its own growth mechanisms… And I’m not a slime expert, so I’m not trying to pretend… But just being enamored by the fact that there’s some level of intelligence in slime that can predict maps. Just this idea of bioinformatics, biointelligence that can supply this… In this case it’s obviously a neuron that can provide feedback, and computing, and stuff like that, but very similar in nature in terms of trying to leverage intelligence built into nature, the world around us.

Yeah, I think people are also inspired by insects or different biological things…

Yeah, why not.

…in computation. Yes, there are such projects also.

Ewelina, what industries are interested in this? You said you have these users all of a sudden… Which industries want this as a thing?

I would say we have three types of users. Individuals/fascinated engineers, or small startups. Some of them want to do something related to biocomputing… That’s why they want to use our platform. And big companies, which have R&D teams; very large companies which have R&D teams which want to do some project on cutting edge technology. The same actually how people do with quantum. They know that it doesn’t work yet, but they want to know what is going on, they want to know how it works, because they believe it will work in the future.

[00:44:16.01] Okay, so that’s cool… What kind of stuff are they trying to do? You don’t have to give specific examples or anything like that, but…

No, actually this is confidential. What our clients are doing is confidential.

Sure.

But we have universities which are using our lab for free, and they are going to publish. So actually, that’s why we chose them. We chose those who have the highest chance to publish.

Sure, that makes sense.

And actually, there will be some papers coming for what people are doing, so I hope everyone will be able to see.

That’s cool.

And we will be promoting this, for sure.

What’s at the other end of my Python API call? So we talked about what was at the neuron platform end… Like, a UV light turns on, or some sort of electrode electrolyzes… What do I get back? Like, I make a call… Is it like a one/zero? Is it like a success/fail? Is there more information coming back to me? Like, what do I get back at the other side so I can actually start mapping results or trying to make sense of it?

Yes. So what you get in response is the electrical activity of neurons. So this is what you can see also in our website, on the live section. So the way how you can measure activity of neurons is a few different ways. You can get a yes/no response. So this is spike trains. This kind of data you get just a dot, and you know this was spike. Every time there was a spike, you get a dot. And there is already quite a lot… You can analyze the patterns, you can see if they’re more active or less active… This is actually the most common way how you collect the data. And it’s quite efficient also, because you just have one dot, one point for each occurrence of the spike. And very often it can be enough. But if you want to be more specific, you can also measure the shape of the spike. Because spike means that the neuron will change the charge, and this will always have a shape. And you can also analyze the shape of the spikes. So this is much more heavy data, but you can also get this.

And of course then you can have – you know, people try to have different ways of interpreting the data. This is actually – it’s a big room for creativity. For the moment, we look for example how late, what was the delay before we saw the signal… Or we can see the distance between the signal, for example, how often a neuron is active. So we try to characterize all these patterns on how they are active. So that’s what you get… And then you can – yeah, there is a lot of signal processing, a lot of analysis of the data.

Sure.

And a lot of data.

Can you target a specific neuron or organoid, to like make sure that your call goes to the same place every time, or no?

No. Actually, you have eight electrodes. So every electrode is in a little bit different place of the organoid. And then you can target specific electrodes. You can, for example, use only a few of them, or you can use, for example, four of them for sending signals, and four of them for receiving signal… Or some other combination. So that gives you some room for playing.

[00:47:57.25] And what is also interesting is not every electrode is always active, because sometimes you might have less signal, or no signal at some of the electrodes. So it’s really complicated. It’s very difficult to work with a living tissue. Sometimes they are just not active also.

So they have – how do you know when they’re about to die? You mentioned inefficiency, you mentioned one day doesn’t work the same as the next… We know earlier in your research they would die in hours, now they die in hundreds of days, I think… Help me understand terminology, is that right, and how do you know the inefficiencies aren’t because they’re about to die? Again, I don’t know if that’s the right terminology to use or not.

So there is at least one thing which is easy… So this is easy, to see if they die or not, because they are not active. So living neurons - they are spontaneously active electrically, so they will always produce some spikes, and you will see them on the electrodes, on the measurements.

Okay.

So this is quite easy to say that they are dead. If there is no activity, you assume they are dead.

Okay.

And actually, you are right, because also batches are different. And this is also what you can see on our website… Because a few of our neurospheres are monitored there. You can see that the activity is not always the same. Sometimes it’s active, sometimes less active… So all this you can see very easily on the electrodes, when you measure the activity.

Yeah. One thing I think is interesting too is the environment it has to live in, which - we talked a little bit about quantum computing, and then compared it to biocomputing… That there has to be a sterile environment, no viruses. Can you talk – I know you are in a lab, or at least early days of research and stuff, but what is the environment? And how will that potentially scale to usable product at the long tail of usage? What is the environment these things live in?

Yes, so environment is very important. Neurons are very fragile. And the environment has to be physiological, so the same as in our bodies. So there has to be physiological temperature, there has to be, of course, always liquid around… Neurons are in the medium, so this is water with the different substances which keep them alive, which also feed them… And all this is very, very important. pH, temperature… Everything. Even small vibration. Everything is really important for the neurons to be stable. And it has to be very strict, otherwise the activity can change or they can die.

And this is why also we believe in these bioservers, in the central servers idea, because we think that it will be easier to control these conditions of the neurons when they will be in a central server. So that’s also the reason why we –

So not likely to have a home version of this in the early stages of this. Like, you want to centralize it at some sort of data center, or a space where the environment can be better controlled.

Yes, absolutely. And we imagine we have the same what we have today, but much bigger.

How big is what you have today?

Well, now we have two rooms for the laboratory, so we are growing… We started with one, a little lab. And our neurospheres are a few millimeters diameter. 10,000 neurons each. So they are very, very small, but for experiments it’s enough. And in the future we imagine to have huge structures, even 100 meters long of neurons. So that’s how we imagine the future. It’ll be much, much bigger.

Yeah. I have so many questions about the details of that, but you can’t really ask them until you guys know how they work exactly… Because I think a lot of the decisions will be based on how they work. Like, how many neurons will I need to do a thing? And it’s like “Well, we don’t know, because we don’t know how they work exactly yet.”

Yes.

[00:52:17.13] It seems like it’s going to be exotic use cases. And I imagine, as somebody who’s been at the doctorate level, the PhD level of this, from neuroscience to this laboratory stage, that you probably see at least some very exotic use cases. It has to have a unique environment, you plan to centralize it to offset that… But I’m sure there’s unique scenarios where like this may be finely tuned or very specific to a certain type of task, versus general computing. It’s not going to be in my iPhone. Maybe at that point it will be an iPhone, a literal iPhone… Anyways. Are there any unique, exotic scenarios or use cases that you already see, even though you’re in the science stage, where this may apply?

Actually, we aim for general computing. But of course, not everything. By looking at the human brain and also thinking about what is done now in digital, we think that every – maybe not every, but many tasks which are done by artificial neural networks will be much better to be done on biological neural networks. For example generative AI we believe could be better on the real neurons.

Really? Okay. So maybe that’s the first place where – because we’ve started off talking about our energy crisis that is obvious; that seems to be the obvious reason why biocomputing is the platform… It’s potentially a lot less required energy usage.

Absolutely.

And so you’re going general.

Yes, yes. We go for general computing, which will be much, much cheaper, and very competitive to digital.

Are any of your customers well-capitalized evil geniuses who just want to electrocute some neurons because they’re just enjoying…

Gru. Maybe Gru is –

Like a Doofenshmirtz, or maybe like a Moriarty… Any Moriartys? I’m just messing with you, Ewelina. I’ve just run out of actual questions… Adam, anything else for her? I mean, this is interesting stuff. I think we’re definitely – a lot of work to be done.

Yeah, I think it’s really about the – I was just thinking like where could it be used, where do you see it being used… I’m surprised at general, because it seems like it’s – that’s the long road. Like, the short-term road would be specialized use cases, where you can control the environment, have potentially really rich clients that have blank checks that can give you four rooms versus two, kind of thing… I’m thinking like that versus general computing, but I guess I was wrong.

No. Actually, we aim for general computing. We think general computing could be a real revolution.

Post-silicon, Jerod. Post-silicon.

Biocomputing human neurons.

Wow. I’m looking forward to it. I can’t wait to see what happens next. I’m so surprised by what’s happening today…

Yeah, I had no idea.

…let alone what the future may hold from this. So cool.

Thanks, Ewelina. Thanks for coming on the show and telling us all about it.

Thank you so much for the nice questions and nice discussion.