Good morning. Thank you very much, Hans, for this most kind invitation. It's an honor to
be here. I'm just a biologist and a neuroscientist, so there's nothing I can tell you about supercomputers
or give you any lessons about computing in general. But what I can try to do is to give
you a use case, at least to tell you what we would like to see your technology do for
us. So I actually started in medicine and with a strong interest in brain diseases and
psychiatry. I very quickly ran out of medicine. I realized that it was just a cataloging system
where you just catalog diseases and then you sub-catalog which drug should be given
to the patient. And I thought that neuroscience was going to be the answer and I went into
neuroscience. This was while I was in South Africa at Cape Town University. And I've studied
the brain now for 25 years, digging, digging, very reductionist, gone from the whole brain
level down to the genes and back up and down, published, I don't know, lots of papers and managed
to get a nice position and I can keep doing that for the next 20 years, 30 years. I realized though
that it's not going to make any difference to this. This is a serious problem. I don't think
that most people even realize how serious this problem is and how serious it is going to get.
And very soon. I think that we have to stop saying let's study the fly brain for the next 10 years
or the bird's brain or the snail's brain. We do not have an option. We have to start addressing
this problem. So let's look at the problem. That is the problem. 560 clinically classified
diseases. It sounds scary, but that's all the places and the things that can go wrong in the
human brain. Now, the problem is that about 95% of the funding that goes to study these diseases
comes from the pharmaceutical industry. And the pharmaceutical industry, if you read the latest
news and speak to them, you will discover that they are pulling out. They're pulling out of brain
because it's too complex, it's too risky and it's too costly. It's already costing a billion euros
to bring one drug to the market. Now, that's not where the problem ends. The problem is that
pharmaceutical industry can only focus on a handful of these diseases, just a handful, because
that's where the money is. You can understand it. They have big risk to take. They have to go where
the money is. That means that most of the diseases, the 98% is left to academia, that is getting about
5% of the funding to study not only how the brain works, but to study all these different diseases.
So, how is academia addressing this problem? Very much the same way that I was, digging into a
particular part of the brain, understanding the structure and the molecules and the cells and so
on and so forth. And we're each digging in many, many different areas of the brain with many
different strategies and techniques. Now, we can do that for a very long time and I believe that
that's not going to help us understand the brain. In fact, there's about 5 million papers that are
relevant to understanding the brain already. We're producing the whole community, producing about a
hundred thousand papers a year on the brain already. Huge amount of data. High throughput
technologies in industry is already producing more data in a day or in a month than we've generated
in the entire 20th century. We are facing a massive tsunami of data. So, more data? Is that the
solution? I don't think so. I think we have to start getting organized. And I think this is
probably the first time that I'm going to give a presentation to a community that doesn't need to
be convinced about the solution or that actually already knows the solution. You already know the
solution. And the solution is integrate it all. Bring it all together using simulations, integrate
it into a unifying model. Now, all these different levels, do I have a pointer somewhere? All these
different levels we call omics levels of the brain. From the genes to the molecules to the neurons,
the synapses, microcircuits, meso-circuits, macro-circuits, whole brain. They're omics levels,
huge amount of information on every single level. And what we need to try to do is bring them all
together. There are powerful new informatic tools. We actually call it predictive reverse engineering,
where you can analyze and look for patterns within and across these different levels and find rules.
And we call it predictive reverse engineering because we discovered that if you combine,
and I think it's also not a discovery, but it's a discovery for us in life science, that when you
combine rules, you discover new rules. And it starts to accelerate and it helps you to start
integrating the information. Integrating the information into a biologically accurate model
is also a big challenge, but by applying these different rules, you can start constraining models
in such a way that you actually can get to very accurate representations. The most important
message or lesson that we've learned, and perhaps the message, is that actually if you follow biology
very closely, modeling becomes easy. And I'll try and show you some parts of that. So I'll try and
illustrate to you how we go about this problem. How do we start? Where do you begin? Well, I mean,
the brain is complicated. So we begin in the rodent brain and this is a huge number of experiments
that were performed over probably the past 15 or so years. And your first challenge is you've got
to go in and record from every single one of these neurons, fill it with the chemical dye, reconstruct
the morphologies in three dimensions and make a computer graphic model, a computational model
out of each of these neurons. We've classified almost all the different types of cells, but that
is one strategy. And that's just for a tiny part of the brain about the size of a pinhead. That's
not going to give you the solution either. That is one strategy that you have to begin with. What
we have to do, even in this particular project, is we have to clone the neurons. So morphologically,
we have to actually look at the statistics of all the branches and we have to copy and clone. And we
create in this way a massive database of as many of neurons as we want. But that's also not the
solution because in the end, if we want to get to the human brain or to a brain of another species,
is you've got to be able to computationally synthesize the neurons. You've got to grow them.
And currently, we use statistical properties of the branching as input parameters to a model
that actually synthesizes these neurons. And this is becoming more and more feasible to be
able to capture from pictures, to capture statistics, and to synthesize the different
types of neurons that even will be similar to the neurons in the human brain. But this is also an
intermediate step. We have to move towards where we can capture the genetic information because in
the end, we'll have much more genetic information about the human brain and different cells and use
the genetic information as input to the model and synthesize computationally synthesize the
neurons according to the genes that are building all the different types of neurons. Now, it's not
enough just to build the outside or the morphology of neurons. You have to build also the inside of
neurons. You have to build what we call the ultra structure or the internal geometry of
neurons. And neurons, inside neurons, is a whole world. It's a massive and very complex world.
But it is also not impossible to start synthesizing this internal structure. And what we do is we
mine electron microscopic blocks of tissue of the brain. And we extract statistics that help us
specify generic models of the internal organs of a single cell. This, of course, has to go further
and beyond that to where we can actually also use genetic information. A cell is expressing about
4,000 of its 30,000 genes or 20,000 genes. And if you know the combination of these
genes, the subset of genes that are expressed, you can start building individual, the internal
organs of all kinds of individual cells. Now, once you know the internal organs, you can start
looking at the proteins that these genes produce and start distributing them inside the cell.
You can start looking for how they interact with all kinds of other proteins. And you can characterize
computationally the different kinds of processes that will result from that.
Now, to illustrate here what we call what a powerful technique we think is going to make
this much easier than it would seem, because of course it is a very complex process. You probably
have about a billion proteins inside here that you have to account for and that you have to map
who they're interacting with. But if you didn't build those internal organs, then there could be
potentially millions of different types of reactions that you'd have to capture. But when
you put in the internal organs, this collapses into probably less than a thousand, probably a few
hundred types of reaction types that you have to capture. And what that illustrates again is that
the closer you follow biology, the more the parameter space constrains. So in physics,
when you talk in physics, you want to build a complex system, everybody knows the parameter
space explodes. In biology, it's the opposite. The parameter space collapses because you add
constraints. There are rules in biology, lots and lots of rules. And it stands to reason
that if we can get those biological rules about the brain, we can use the same rules to build
the model of the brain. So of course we're not going to be able to, and I would hope if you could
build a supercomputer that could, I'd be very happy. But you're not going to be able to build
a supercomputer that is going to be able to simulate the whole human brain, or any brain for
that matter, at least any mammalian brain, at the molecular level anytime soon. But what we can start
doing is start to do true multi-scale modeling where we can dynamically switch the scale of the
simulation so that as we zoom into a particular area, we can see it in much more detail with much
more biological precision. And this is one of the strategies that we are now trying to develop so
that you can both dynamically switch, perhaps depending on the activity of a particular part
of the brain, or depending on what part of the brain that you want to look at. So let's go back
now to the circuitry in order to be able to understand how we model an individual cell.
This is still what we call a cellular level. So we're not at the molecular level, at the cellular
level. Now when you excite, you know, neurons generate electricity, when you excite individual
cells, they produce electrical patterns. These are like binary spikes, 0.1 volt spikes, but every
neuron has a character, it has a personality. So when you excite it, it produces a train of these
spikes, a series of these spikes, and some of them are stuttering, some of them have a delay,
they think about it first, and then they react. Some of them are bursting cells, some of them are
very irregular type of firing cells, they like an Israeli type of character. Some of them are
Swiss clock type cells, they fire exactly at the same time, every time. So they all have a character,
and there are 16 types of characters. And what we had to do was to find out the next step, how do we
build a very accurate model of all these different electrical types of cells, and to do that you have
to be able to go inside an individual cell, and find out which genes are switched on, that are
building what are called ion channels. Ion channels are little proteins that are sitting on the
membrane, and when they open, they're voltage sensitive, so they have, you apply a voltage,
but I suppose a bit like a transistor, when you apply a voltage, they open, and they allow ions
to flow through, the electricity in the brain is carried by ions and not electrons. So, but there
are many different types, over 350 different types of these, and depending on the combination,
the color combination, you can build all these different types. But you have to not only know
the combination, you have to know how they're distributed on the branches, and you have to
know their biophysical characteristics, when are they sensitive to the voltage, and how fast do
they open, and how big do they open, and so on. These can be described by equations that we
invented some time ago, in fact Nobel Prize, two Nobel Prizes were awarded for this discovery.
So, it is possible to actually derive the combinations, get the distributions, and find
out the kinetics biologically through measurements. Now the next challenge that you have, is that you
need to know the relative concentrations, and this is a much more difficult data set to get.
And what we discovered is that, we can now collapse the fitting, we run a genetic algorithm,
that looks for the relative color combinations, and when it finds the right combination of types
of channels or colors, it actually matches some of these behaviors. We can recap, we can capture
all these different behaviors automatically. Before we started this project, it was literally
a three-year project of a PhD, to tweak these channels, and to make this cell behave in that
way. Today we can push a button, and we can generate as many of these cells as we want,
millions or billions if necessary, with very high precision of biological processing.
This is an example, we can just, the color code is voltage, and when a cell is quiet, it's sitting
at about minus 70 millivolts, and it collects some, gets some inputs, it rises to about minus 40
millivolts, and then it produces this spike of activity. So, it's technically possible to create
model neurons, that are capturing and behaving almost the same as biological neurons. The next
thing you have to do, if you want to build a circuit, is you need to know the recipe. How many
of each type of cell do you need to put in each layer? This is what we call a composition recipe,
and that composition recipe is then used in an application we call the distributor,
that runs through a series of steps, in order to distribute each of these different types of
neurons, into a particular volume of the brain, that we're trying to simulate, and that allows us
to basically recreate and specify the composition and the location. But that's not going to work,
and it's not going to be enough if we want to get to a human brain. There's a hundred billion of
these neurons, we haven't yet worked out all the different types, but once we understand the
genetic types of neurons, we will still face a problem. How do we then position all these
different neurons in the human brain? Now, there are new techniques today that are emerging, it's
called ultra microscopy, that is allowing you, and this is in a mouse brain, that is allowing you to,
within about an hour, to scan and map out the position of every single neuron in the brain.
So a mouse brain is about a hundred million neurons, you can take this microscope, if the
cells are expressing a little fluorescent protein inside them, you can map out all the different
positions of the cells. You can make these cells express this fluorescent protein for different
kinds of genes, and then you can get as many maps as you want, that tells you about the distributions
of different types of cells. Now, the secret in the end to be able to position them is to exactly
take into account all the multiomic levels, all the levels of organization. These are brain maps
of different genes that are expressed in the brain, and what we can do is we can use one map,
when we want to build a particular model with all the distributions, we say when we built it,
it must account for this map, and this map, and this map, and another 100, 200, 300 maps.
When you start adding maps, it collapses very quickly. There's not many options how you can
position these neurons, and that's the, again, the principle that from the macroscopic constraints,
you can actually specify microscopic detail, and this is going to make it faster and easier for
us to specify the specific detail in the cell. Let's go back. We don't have, we're not there yet.
This is what we are. We have the actual neurons already in 3D, not synthesized, and we can start
building a microcircuit. So here, we have, we're calling up the neurons from the database. We've
cloned as many of them as we need of each of the types, and we can load them within it, and you get
this reconstruction in the same volume, as accurately as you can for spatial distributions,
a piece of the neocortex of the rodent. Now, you face the next challenge. You have to start
connecting them. Now, your brain has about a thousand trillion synapses. In this part,
you have about 10 million or so synapses, and you have to find out where they are.
So, what we can do is look and see where these cells touch, but before that, we go back to the
data, and if you go to the data, what you, and this was a huge number of experiments,
you discover that you can find out where these cells all touch each other, and they form what
are called synapses. Now, it looks like a mess, and the first time you look at it, you think,
okay, that's it. Let's all go home. There's no way you'll ever understand this, but if you keep
doing many of these recordings, you discover there are patterns. There are statistical patterns of
where these synapses are going to be located. The question was, how are we going to capture
that in a model? And what we found in another set of experiments is that, actually, when neurons
are connected and are talking to each other, the fiber that's crossing, that is providing that
synapse, there's very little difference from a fiber that is not providing the synapse.
So, that led us to conclude that what we should try first is just look for where these fibers
are touching. So, we did that, and we built an algorithm together in our collaboration with IBM
that searched, it's called a collision detector, that searches, and it runs on a blue gene,
it searches for the app positions, how close these branches get. The first time we built,
ran the algorithm, took a couple of days. Today, it takes seconds because of optimization and
improvement. It finds about 100 million of these locations of where they touch. But then, we don't
know if they're correct. So, we analyzed the positions, the statistical positions, where all
these different types of synapses are, and we discovered that we didn't have to do anything.
We thought we were going to have to spin and rotate and juggle and fit these neurons together,
and we didn't have to do anything. They all, exactly, well, not exactly, about 98 percent
fitted the experimental data. So, biology had solved the magical solution for creating massive
connectivity by just randomly throwing these branches together. You just throw them in a
bucket, you look at where they touch, and that is what biology is actually doing anyway.
So, what you have to do is take from all these possible touches and down sample so that you can
find only those where they are going to form a synapse and talk to each other. And this is a
rule of synaptic selection or synaptic formation. And once they form these connections, then there
becomes a live contact. So, it's like you've got this massive connectivity in the brain,
and there's a down sampling where you get the specific functional connectivity that allows
the neurons to all start talking to each other, and that accounts for these thousand trillion
synapses. We haven't run this algorithm yet, but we now apply, in the meantime,
a connection probability between these cells, and what that gives you is basically all the synapses
that are connecting these neurons. There's about 10 million synapses. The red ones are excitatory
synapses. They excite the brain. The blue ones are the inhibitory ones, and they control the
excitation. They carve the excitation. And so, for the first time, we could actually computationally
derive the positions of synapses in the brain and solve, in some part, what we call the connectome
problem. And in this case, this is what I mean by predictive reverse engineering, where we apply
these rules that we've learned in biology to actually computationally map out and find out
where all the synapses are in the brain. You can accelerate this process computationally and
by building the model. Now, we discovered another amazing thing, which is very relevant to technology,
is that you can actually take these neurons and spin them and rotate them and put them in almost
any position. The map of where the synapses are doesn't change. It actually stays invariant,
and it's the secret to why the map of synapses in our brain actually can be the same,
even though the branches are different and the neurons individually are different.
More importantly, if you delete neurons, you wipe out neurons, wipe out half the neurons,
this map of where the synapses are doesn't change. So, in fact, in biology, it's discovered
an incredible secret. The way that it gets to an invariant, totally highly robust connectivity
is by making sure that every single neuron is slightly different. In technology, you spend
billions to make sure that every transistor is exactly the same. In the brain, it does exactly
the opposite in order to make sure that it's a highly robust system. Now, because it's so robust,
we can now take the circuits that we build, get the very detailed map, and collapse it into a
simple connectivity map. Once we have that simple connectivity map, we can export it to an
application, an artificial neural network. So, you know, DARPA and many others are hunting
to build neuromorphic processes and clever machines, and they're imagining this circuit.
Well, in this strategy, you just push a button and export the circuit, and you can implement
it onto a neuromorphic chip or into artificial neural networks, and we're currently exploring
this option together with our colleagues in Heidelberg that have already developed a lot
of this technology to implement and integrate these circuit designs. In fact, that's the one
key thing missing in neuromorphic engineering is the circuit designs. Let's continue. Next step,
once you've got your neurons, you've got your connections, you have to actually capture in a
mathematical model the communication between these neurons. Now, this synapse is probably the most
complex submicron device, I dare say, in the universe. It is a very complicated little machine,
but it's especially interesting because when one neuron talks to one neuron, it will behave in a
particular way, and if it talks to another neuron, it behaves in a different way. You can think of
synapses a bit like as filters. You have low-pass filters and band-pass filters and high-pass
filters, and you actually need to know all the different filters that are used between the
different cells so that you can capture the communication between the cells. So, in summary,
we've got a basic blueprint of the circuitry. We know what the equations are. We have the equations.
There's no new mathematics here that has to be invented. The equations to simulate the flow of
ions within cables, the way these ion channels open and close. We have the equations that describe
this dynamic synaptic transmission. So, we have to go to the next step and prepare for blue gene
simulations. And, in fact, I went to IBM when I was in about 2000, I think it was, when they were
just starting before it was sold, the blue gene series, already then, because it collected all
this data, and I said, okay, I mean, where is the machine? And it looked like that was going to be
the solution, and we went there and discussed. And, in fact, IBM doesn't know it. They think
they built it for something else, but actually it was designed for running simulations on
brain simulations. So, we, as you can imagine what we do, we throw the neurons into different
processes there. When the cell spikes, it's a very simple communication. Just spike times are
communicated between cores today. But before we can do anything with that, we need to build now
a new visualization. We have to visualize this. So, we had to build a mesh generator that will take
these biological structures and turn them into mesh models. And we had to also build a dedicated
real-time rendering machine. And we had to integrate that into an in silico virtual environment,
so that I, as a biologist, can sit there and play with it as a biologist, experiment with it. And so,
we built this what we call blue hub, which is an environment where we can actually design an
in silico virtualized experiment. And that's what you get. The first reconstruction and simulation
of the neocortical column, which is the heart of the mammalian brain. It's the heart of our
intelligence. This is the eye of the brain. You may think that your eye is the eye. It's not.
The eye just collects some fragments of information about the world, sends it in, and this is the
machine that generates perceptions. So, now we have a way to systematically start decomposing
how this machine is working and what are the principal mechanisms behind the molecules, the
synapses, the communication, and the computational strategies that it's using. Now, what we do when
we recreate this piece of the brain is we don't try to make it perform a particular function.
This is very important. We call it bottom-up building and top-down validation. So, the model
is not designed to do object recognition or face recognition or anything intelligent. The model is
built according to biological rules. And what we then do is we check to see how this model can
capture experimental data. We take data of other experiments. We mimic those experiments in the
model, and we see whether these emergent properties happen. They're not programmed to happen.
And all the papers that we found so far, this is another one, they can recreate the experimental
emergent properties that have been found. In this case, it's a paper that shows that the brain
actually wakes up at a certain frequency of stimulation. You stimulate, stimulate, and
suddenly it wakes up. And when we do that, you actually can recreate. You see the same phenomena
occurring. And not only does it occur, but if you actually analyze the spectral graph of the
oscillations or the way that the cells resonate with each other, we discover emergent properties
that were missed in experiments. And with these, and this is gamma oscillations, which is a very
important way that neurons can actually bind their activity together, we discovered gamma
oscillations can be generated within the same little piece of the brain. But the power of it is,
you know, you can get gamma oscillations with any simple model. But if you did that, you wouldn't
be able to trace back and say it's this cell and it's this molecule and it's this synapse.
With a detailed model, if you get this without having programmed it, you can then trace back
the causal mechanisms behind this. And this gives you a much more fundamental way to study
the mechanisms of the brain. We're now being able to, we can simulate the local fields that are
generated by these cells and you can combine them to look at the whole space or this 3D volume of
electrical fields generated, which is a precursor to doing
EEG. EEG is a way that you go to the clinic and they put these little electrodes on
and you can get maps. This is the step before EEG. So this can be also captured in simulations.
The important thing here is that this we can't do on the blue gene today,
because this now changes your mode of communication. This is going to explode the
bandwidth. Before we were just transmitting spikes between the cores. Now we have to
transmit a lot more information. So this is post-processing at this stage and we will have
to find other solutions to be able to do this for a whole brain. We take this further and
in order to study the emergent behaviors, not just the emergent properties, but the emergent
behaviors, couple this model hopefully as close to real time as possible. Depending on how much
you want to read, we're at about 100 times real time. Couple the emergent properties to
a virtual robot or a real robot. The value of this is that if, and we don't know when we haven't
done this experiment yet, we're setting up to do it, but with all the learning rules and all the
reward mechanisms inserted into this model, if this model can support another time to make
anything intelligent, a decision, remember which door has the reward, that very moment
you'll be able to trace back the causal chain of events that lead to complex behaviors. If you had
built a simple model, you will have a very intelligent robot, but you will not know what
caused it in the brain. If you have a model that is built from the bottom as detailed as possible
and it can support this behavior, you will have a systematic way to trace the causal
chain of events that lead to intelligence. So, of course, we're not staying at the
column level. We're going beyond the column and this is the way that we can do it. You can see
there's a lot of fibers that are extending out here. They're going to many other areas and we're
going to get to that challenge later, but now we can start combining columns together and connecting
them together to build a brain region and start mapping out the whole of the neocortex. And
this is the first simulations which we can now do. We do this on a blue gene
P, which is 16,000 plus cores. We can actually get to probably about a million neurons
with over a billion synapses. This is 360,000 neurons, but you see here we've already hit
the limitations of any visualization technology today. We cannot visualize all this,
so I can just show you the points, but those are really detailed neurons and what we need to be
able to do is to capture that in detail so that we can navigate inside there and see it.
So, in summary, there are many different, in summary for this part, there are many different
ways that we can get to the human brain and we have mapped out a chart where we need to synthesize
the cells. We need to synthesize the ultra structure. We need to map out the fibers because
the more macroscopic constraints you have, the more you'll be able to specify and the microscopic
details of the brain. So, this is more or less the roadmap that we have charted and I get into
a lot of trouble for saying 10 years. 10 years provided there's the funding and provided you
guys come up with the supercomputers, but that's happening. We could see that in the previous
presentation, but it's 10 years provided there's funding. This doesn't come from the air,
but if that does happen, we should be able to move along this roadmap. So, before we started,
it was basically possible to build one neuron. It was running on a single PC. By 2008, we had built
the first cortical column, 10,000 neurons. Today, we're at about 100x of that and we should be able
to reach that. In fact, this is technically possible, for example, on the eulach machine.
We don't have all the biological detail to specify it, but it's technically possible to
simulate 100 million neurons on eulach's blue gene machine. We'd need about an exascale to get to
the human brain at the cellular level, but again, what we're doing with the multi-scale strategies
that will be able to go as needed into deeper simulations that are much more precise.
Well, again, it's not just a model. It's not just a simulation. To do this, you have to do a global
integration. You have to integrate all the neuroscience from the genes all the way up to
the phenotype, the emergent behaviors. You have to database this information. We apply this
predictive reverse engineering to look for rules and patterns and to make predictions to help us
specify the details of the brain. You have mathematical abstractions that go across all
levels. You have to build models of all the different levels and you have to have the
capability to do virtual experimentation. There are multiple representations which
explodes the data space that you need. It is a massive data challenge. If you just look at
the spatial data, you're looking at full human brain, going to put some serious strain on
the amount of memory that would be needed. This is our current HPC environment,
which we have, but of course, this is going to have to expand significantly to be able to get
close to this roadmap. You also have to workflow almost everything. You have to create a way that
you can use this quite interactively and easily. In fact, the key is to put the scientist in the
loop. This has almost got to become a black box so that the scientist can specify details of the
brain according to either hypothesis of a disease or some new information about a new protein or
new cell or new synapse and put it in and it will compile a new model. We have built the first
facility. It's a prototype facility that allows us to do this. It's a huge software stack that
enables us today to build microcircuits. We can already build, actually this is an old slide,
we can already build small brain regions. In fact, it's a generic facility, so it's not a model.
It's a generic facility that allows us to build any microcircuit and provided you can specify
certain amounts of biological data. But what you really want in the end is to be able to do virtual
steering and interact with supercomputing. It's just not going to be possible to build a model
of the brain without physically interacting as you're building it and you're going to want to.
This is not a simulation. This is the data. It's like how you would want to fly through a model
and at any moment in time stop and analyze or at any moment in time stop and run a simulation,
a different kind of simulation or build a virtual instrument, a virtual microscope and map and measure
another feature. This of course is going to put a huge strain on the kind of software or perhaps
even the kind of hardware that is going to allow us to be able to do this in any reasonable
real-time mode. So that's why you run out of resources. We had to go for this big proposal
for what we call now the human brain project and in this project what we aim to do is to build two
cockpits, a simulation cockpit which will be the sort of display end point of all this
huge software stack that will allow us to integrate the biological information,
build models, simulate design experiments, design virtual instruments, virtual microscopes or virtual
imaging systems so that we can actually do what we do in the lab but on a model.
This will sit in Luzan and we also aim to build a cockpit for high-performance computing. I think
that when you get to this stage it's going to become very difficult to optimize these kinds of
exascale and beyond systems and a mission control center where you'll be able to look
and see bottlenecks and analyze them and have them indicated in as fast as possible during
a simulation because there are many different modes of simulation. You'd be able to improve
the rate of hardware-software co-design and hopefully come up with the kind of system
that is going to meet the specific needs of brain simulation. We're aiming for this to be
a generic system which means you can come in with your earth simulation application or software
stack and optimize the high-performance computing system to deal with that and the focus is not just
getting speed or memory but the whole end-to-end value chain. To take that further this is not a
project that sits there and serves a couple of scientists. The goal is to make this open to the
community worldwide and to allow everybody to start using this facility as a research instrument,
as a research tool. It's not about it solving anything, it is about us using a new research
tool or instrument to explore deeper and deeper into the brain and to allow us to investigate
any hypothesis about the brain. We aim to create this, I suppose you could call it an ecosystem,
that would allow this brain simulation to serve as a service. So in conclusion this is some of
the team, it's actually not all the team. The key people here of course are Felix Schumann, he's
also here in the audience, who has been the key guiding glue that has steered this team through
all the different technological barriers. Sean Hill who was originally from IBM but then he
defected and he came and worked on the project and he's now actually the director of what's called
the International Neuroinformatics Coordinating Facility but also still on the project.
And I'd just like to thank very much IBM for their amazing participation over many years,
David Cremies, Dave Turek, Bernie Mason. It's really an incredible company to work with and
the capabilities that we have got through many many years of discussion have been incredibly
valuable. And lastly but not leastly you can see Bob Bishop and I'm sure you all know Bob,
Bob sitting over there looking over sometimes in amusement I think at what we're trying to do and
sometimes I think feeling that he was a bit younger so that he can actually jump in and
help us and actually solve some of these key challenges. But Bob has been very important
in giving us advice and warnings about particular areas that we may or may not solve soon but we
intend solving them. And lastly it's not in my mind any longer an option to ask whether it's
possible to simulate the brain. The only question we have to ask now is what it's going to take to
make it possible. So thank you very much for your attention.