## Understanding the brain using topology: the Blue Brain project

ALERT ALERT! Applied topology has taken the world has by storm once more. This time techniques from algebraic topology are being applied to model networks of neurons in the brain, in particular with respect to the brain processing information when exposed to a stimulus. Ran Levi, one of the ‘co-senior authors’ of the recent paper published in Frontiers in Computational Neuroscience is based in Aberdeen and he was kind enough to let me show off their pictures in this post. The paper can be found here.

So what are they studying?

When a brain is exposed to a stimulus, neurons fire seemingly at random. We can detect this firing and create a ‘movie’ to study. The firing rate increases towards peak activity, after which it rapidly decreases. In the case of chemical synapses, synaptic communication flows from one neuron to another and you can view this information by drawing a picture with neurons as dots and possible flows between neurons as lines, as shown below. In this image more recent flows show up as brighter.

Numerous studies have been conducted to better understand the pattern of this build up and rapid decrease in neuron spikes and this study contains significant new findings as to how neural networks are built up and decay throughout the process, both at a local and global scale. This new approach could provide substantial insights into how the brain processes and transfers information. The brain is one of the main mysteries of medical science so this is huge! For me the most exciting part of this is that the researchers build their theory through the lens of Algebraic Topology and I will try to explain the main players in their game here.

Topological players: cliques and cavities

The study used a digitally constructed model of a rats brain, which reproduced neuron activity from experiments in which the rats were exposed to stimuli. From this model ‘movies’ of neural activity could be extracted and analysed. The study then compared their findings to real data and found that the same phenomenon occurred.

Neural networks have been previously studied using graphs, in which the neurons are represented by vertices and possible synaptic connections between neurons by edges. This throws away quite a lot of information since during chemical synapses the synaptic communication flows, over a miniscule time period, from one neuron to another. The study takes this into account and uses directed graphs, in which an edge has a direction emulating the synaptic flow. This is the structural graph of the network that they study. They also study functional graphs, which are subgraphs of the structural graph. These contain only the connections that fire within a certain ‘time bin’. You can think of these as synaptic connections that occur in a ‘scene’ of the whole ‘movie’. There is one graph for each scene and this research studies how these graphs change throughout the movie.

The main structural objects discovered and consequentially studied in these movies are subgraphs called directed cliques. These are graphs for which every vertex is connected to every other vertex. There is a source neuron from which all edges are directed away, and a sink neuron for which all edges are directed towards. In this sense the flow of information has a natural direction. Directed cliques consisting of n neurons are called simplices of dimension (n-1). Certain sub-simplices of a directed clique for their own directed cliques, when the vertices in the sub-simplices contain their own source and sink neuron, called sub-cliques. Below are some examples of the directed clique simplices.

And the images below show these simplices occurring naturally in the neural network.

The researchers found that over time, simplices of higher and higher dimension were born in abundance, as synaptic communication increased and information flowed between neurons. Then suddenly all cliques vanished, the brain had finished processing the new information. This relates the neural activity to an underlying structure which we can now study in more detail. It is a very local structure, simplices of up to 7 dimensions were detected, a clique of 8 neurons in a microcircuit containing tens of thousands. It was the pure abundance of this local structure that made it significant, where in this setting local means concerning a small number of vertices in the structural graph.

As well as considering this local structure, the researchers also identified a global structure in the form of cavities. Cavities are formed when cliques share neurons, but not enough neurons to form a larger clique. An example of this sharing is shown below, though please note that this is not yet an example of a cavity. When many cliques together bound a hollow space, this forms a cavity. Cavities represent homology classes, and you can read my post on introducing homology here. An example of a 2 dimensional cavity is also shown below.

The graph below shows the formation of cavities over time. The x-axis corresponds to the first Betti number, which gives an indication of the number of 1 dimensional cavities, and the y-axis similarly gives an indication of the number of 3 dimensional cavities, via the third Betti number. The spiral is drawn out over time as indicated by the text specifying milliseconds on the curve. We see that at the beginning there is an increase in the first Betti number, before an increase in the third alongside a decrease in the first, and finally a sharp decrease to no cavities at all. Considering the neural movie, we view this as an initial appearance of many 1 dimensional simplices, creating 1 dimensional cavities. Over time, the number of 2 and 3 dimensional simplices increases, by filling in extra connections between 1 dimensional simplices, so the lower dimensional cavities are replaced with higher dimensional ones. When the number of higher dimensional cavities is maximal, the whole thing collapses. The brain has finished processing the information!

The time dependent formation of the cliques and cavities in this model was interpreted to try and measure both local information flow, influenced by the cliques, and global flow across the whole network, influenced by cavities.

So why is topology important?

These topological players provide a strong mathematical framework for measuring the activity of a neural network, and the process a brain undergoes when exposed to stimuli. The framework works without parameters (for example there is no measurement of distance between neurons in the model) and one can study the local structure by considering cliques, or how they bind together to form a global structure with cavities. By continuing to study the topological properties of these emerging and disappearing structures alongside neuroscientists we could come closer to understanding our own brains! I will leave you with a beautiful artistic impression of what is happening.

There is a great video of Kathryn Hess (EPFL) speaking about the project, watch it here.

For those of you who want to read more, check out the following blog and news articles (I’m sure there will be more to come and I will try to update the list)

Frontiers blog

Wired article

Newsweek article

## Combing braids

I’m going to a conference next week, and it’s all about braids! So I thought I would write a wee post on combing, a technique which dates back to Artin in the 1940s. In fact the paper where he introduces the concept of combing finishes with the following amusing warning:

“Although it has been proved that every braid can be deformed into a similar normal form the writer is convinced that any attempt to carry this out on a living person would only lead to violent protests and discrimination against mathematics. He would therefore discourage such an experiment.” – Artin 1946

but I really don’t see it as so bad!

Combing is a technique for starting with any braid (see my introductory post on braids here) and ending up with a braid in which first the leftmost strand moves and the others stay put, then the next strand moves while the rest stay put etc etc. It’s much nicer to show this in pictures.

and transform it into a braid where the strands move one at a time, like the following one. I’ve coloured the strands here so you can see that, reading the braid from top to bottom, first the red strand moves (i.e. all crossing involve the red strand, until it is finished), and then the green, and then the blue.

For convenience I’ll only look at braids called pure braids, where each strand starts and ends at the same position. You can easily comb non-pure braids, you just need to add an appropriate twist right at the end to make them finish in the correct positions.

So how do we do this? Consider the first strand, I’ve coloured it red to make it clear. We want all the crossings between red and black strands to happen before (higher up than) a crossing of two black strands. So in this case the crossing circled in yellow are okay, because they happen lower down than any crossing involving the red strand. The crossings circled in blue and green need to be changed.

We can slide some crossings of black strands down past the red and black crossings, as they don’t interfere. Here we can do it with the crossing circled in blue, as shown:

We can start to do it with the crossing circled in green, but we encounter a problem as it wont simply slide past the red strand crossing below it. Moving this crossing down requires using some of the braid relations (see braid post) to replace a few crossings with an equivalent section in which the red strand moves first, as follows:

Even though this braid looks different than the previous one they are in fact the same (you can always test this with string!). Now we have a braid in which the first strand moves before any others. Since all the first stand action is now at the top of the braid, we can now ignore the first strand all together, and consider the rest of the braid, as show below:

we only need to consider the following section now, and again we can put this into a form where only the first strand moves.

In this case using braid relations gives us the following:

And we can now ignore the green strand!

Colouring the first strand in this final section gives us no crossing that don’t involve the first strand:

and we colour the last strand yellow for fun!

Remembering all the pieces we have ignored gives us the full combed braid, where we focus on the leftmost strand until it ‘runs out of moves’ before looking to the next one.

And this is exactly the same as the original braid, which looks a lot messier when coloured:

Why might we want to do this? In some cases it makes mathematical proofs a lot easier. For me, recently I have been focusing only on what the first strand is doing, and so I want a technique to push the other strands down and away!

## Mapping class groups and curves in surfaces

Firstly, thanks to Rachael for inviting me to write this post after meeting me at the ECSTATIC conference at Imperial College London, and to her and Anna for creating such a great blog!

My research is all about surfaces. One of the simplest examples of a surface is a sphere. We are all familiar with this – think of a globe or a beach ball. Really we should think of this beach ball as having no thickness at all, in other words it is 2-dimensional. We are allowed to stretch and squeeze it so that it doesn’t look round, but we can’t make every surface in this way. The next distinct surface we come to is the torus. Instead of a beach ball, this is like an inflatable ring (see this post by Rachael). We say that the genus of the torus is 1 because it has one “hole” in it. If we have $g$ of these holes then the surface has genus $g$. The sphere doesn’t have any holes so has genus 0. We can also alter a surface by cutting out a disc. This creates an edge called a boundary component. If we were to try to pass the edge on the surface, we would fall off. Here are a few examples of surfaces.

As with the sphere, topology allows us to deform these surfaces in certain ways without them being considered to be different. The classification of surfaces tells us that if two surfaces have the same genus and the same number of boundary components then they are topologically the same, or homeomorphic.

Now that we have a surface, we can start to think about its properties. A recurring theme across mathematics is the idea of symmetries. In topology, the symmetries we have are called self-homeomorphisms. Strictly speaking, all of the self-homeomorphisms we will consider will be orientation-preserving.

Let’s think about some symmetries of the genus 3 surface.

Here is a rotation which has order 2, that is, if we apply it twice, we get back to what we started with.

Here is another order 2 rotation.

And here is a rotation of order 3. Remember that we are allowed to deform the surface so that it looks a bit different to the pictures above but still has genus 3.

However, not all symmetries of a surface have finite order. Let’s look at a Dehn twist. The picture (for the genus 2 surface) shows the three stages – first we cut along a loop in the surface, then we rotate the part of the surface on just one side of this loop by one full turn, then we stick it back together.

A Dehn twist has infinite order, that is, if we keep on applying it again and again, we never get back to what we started with.

If we compose two homeomorphisms (that is, apply one after the other) then we get another homeomorphism. The self-homeomorphisms also satisfy some other properties which mean that they form a group under composition. However, this group is very big and quite nasty to study, so we usually consider two homeomorphisms to be the same if they are isotopic. This is quite a natural relationship between two homeomorphisms and roughly means that there is a nice continuous way of deforming one into the other. Now we have the set of all isotopy classes of orientation-preserving self-homeomorphisms of the surface, which we call mapping classes. These still form a group under composition – the mapping class group. This group is much nicer. It still (usually) has infinitely many elements, but now we can find a finite list of elements which form a generating set for the group. This means that every element of the group can be made by composing elements from this list. Groups with finite generating sets are often easier to study than groups which don’t have one.

An example of a mapping class group appears in Rachael’s post below. The braid group on $n$ strands is the mapping class group of the disc with $n$ punctures (where all homeomorphisms fix the boundary pointwise). Punctures are places where a point is removed from the surface. In some ways punctures are similar to boundary components, where an open disc is removed, but a mapping class can exchange punctures with other punctures.

So how can we study what a mapping class does? Rachael described in her post how we can study the braid group by looking at arcs on the punctured disc. Similarly, in the pictures above of examples of self-homeomorphisms the effect of the homeomorphism is indicated by a few coloured curves. More precisely, these are simple closed curves, which means they are loops which join up without any self-intersections. Suppose we are given a mapping class for a surface but not told which one it is. If we are told that it takes a certain curve to a certain other curve then we can start to narrow it down. If we get information about other curves we can narrow it down even more until eventually we know exactly what the mapping class is.

Now I can tell you a little about what I mainly think about in my research: the curve graph. In topology, a graph consists of a set of points – the vertices – with some pairs of vertices joined by edges.

Each vertex in the curve graph represents an isotopy class of curves. As in the case of homeomorphisms, isotopy is a natural relationship between two curves, which more or less corresponds to pushing and pulling a curve into another curve without cutting it open. For example, the two green curves in the picture are isotopic, as are the two blue curves, but green and blue are not isotopic to each other.

Also, we don’t quite want to use every isotopy class of curves. Curves that can be squashed down to a point (inessential) or into a boundary component (peripheral) don’t tell us very much, so we will ignore them. Here are a few examples of inessential and peripheral curves.

We now have infinitely many vertices, one for every isotopy class of essential, non-peripheral curves, and it is time to add edges. We put an edge between two vertices if they have representative curves which do not intersect. So if two curves from these isotopy classes cross each other we can pull one off the other by an isotopy. Here’s an example of some edges in the curve graph of the genus 2 surface. In the picture, all of the curves are intersecting minimally, so if they intersect here they cannot be isotoped to be disjoint.

I should emphasise that this is only a small subgraph of the curve graph of the genus 2 surface. Not only does the curve graph have infinitely many vertices, but it is also locally infinite – at each vertex, there are infinitely many edges going out! This isn’t too hard to see – if we take any vertex, this represents some curve (up to isotopy). If we cut along this curve we get either one or two smaller surfaces. These contain infinitely many isotopy classes of curves, none of which intersects the original curve.

So why is this graph useful? Well, as we noted above, we can record the effect of a mapping class by what it does to curves. Importantly, the property of whether two curves are disjoint is preserved by a mapping class. So not only does a mapping class take vertices of the curve graph (curves) to vertices, but it preserves whether or not two vertices are connected by an edge. Thus a mapping class gives us a map from the curve graph back to itself, where the vertices may be moved around but, if we ignore the labels, the graph is left looking the same. We say that the mapping class group has an isometric action on the curve graph, so to every element of the group we associate an isometry of the graph, which is a map which preserves distances between elements. The distance between two points in the graph is just the smallest number of edges we need to pass along to get from one to the other. When we have an isometric action of a group on a space, this is really useful for studying the geometry of the group, but that would be another story.

## Defining topology through interviews. Interview seven with Jeremy Mann.

The final interview (*cry*) in the Defining topology through interviews series is with Jeremy Mann, who is a PhD student in mathematics at the University of  Notre Dame, studying geometry and topology.

1. What would your own personal description of  “topology” be?

Topology studies features we call “qualitative”: ones that don’t change if the system is gently* disturbed. In some sense, we created topology in order to give precise answers to qualitative questions. In my day to day life, I reason qualitatively. I rarely wonder “Will the temperature outside be greater than 23 degrees?” I ask: “Is it warm outside?” I would call the first question quantitative, and my second one topological. In other words, topology is created to give precise answers to the types of questions we, as humans, are naturally interested in.

* What one means by “gently” depends enormously on the context, and one has a lot of freedom in choosing what that means. For these reasons, despite being wonderfully vivid, topology is at times unavoidably abstract.

2. What do you say when trying to explain your work to non-mathematicians?

I fudge the details and I lie. If a careful mathematician were listening, they might interject with a few “well, actually—”s. But the details can obscure content, and people enjoy fiction, so I try not to lose sleep over it. That being said, I might tell a story like this:
By the age of three, I could pick two peaches out of a bag without knowing the first thing about the symbol “2.” A number was something like a bunch of stuff contained within a box. A number could bounce around and bruise. I could hold it in my hands.
If I had a sack of plums and a sack of peaches, I could add them together by pouring them both into a bigger sack.
But these terms didn’t help me add the grains of sand in a bucket, or the stars spread before my eyes. So I dropped this way of adding, in favor of an algebra with lots of symbols like “2,” and “376,” and eventually “x.”
Since then, I’ve made another shift. These days, my conceptualization of arithmetic is a lot closer to a child’s. This approach has many names, but my favorite is Factorization Algebras. I see a number as a collection of objects contained within a region of space.
But now, my numbers can interact. Symbols are no longer rich enough to capture their structure. Sometimes my numbers feel like exotic creatures. They can circle each other suspiciously.

Symbols see this as “2=2=2=2,” but this picture shows us there’s a lot more going on.

“1 + 1 = 2”.
Two numbers can be enemies. When I add them together, they remove each other from existence. “1 + (-1) = 0”. Sometimes, I play this in reverse, watching two enemies spontaneously born from empty tranquility.
I guess I’m interested in more than just writing down the final answer. I want to see their costumes. I want to know how they come together. I want to feel the content in their choreography. My work helps me do this.

3. How does your work relate, if at all, to the Nobel prize work?

The Nobel Prize was awarded for insights into the behavior of certain forms of matter at very low energy, where their behavior becomes “topological.” Strictly speaking, the structures I consider are not “topological” — despite “being a topologist,” my work does in fact know the difference between a coffee cup and a donut. It’s much higher-energy.*
Many physicists are interested in a material’s low energy behavior because these conditions contain a huge amount of information about a material’s possible phases. This even includes more “exotic” phases of matter, some with potential applications to quantum computers.
I’d like to point out the following: often, “exotic” means “outside of one’s comfort zone.” So, when physicists say “exotic topological phases of matter,” I suspect they are expressing how the low energy behaviors of certain materials are outside the comfort of zone of many members of the physics (and mathematics) community. This “exotic” behavior defies common intuition. However, when a material behaves in this manner, to a topologist, it enters very familiar territory. The topological is not exotic to a topologist.

** hotter, but certainly not sexier.

## Defining topology through interviews. Interview six with Cécile Repellin.

The penultimate interview in this Defining topology through interviews series is with Cécile Repellin, who is a postdoc at the Max Planck Institute for the physics of complex systems, Dresden and works on condensed matter theory.

1. What do you say when trying to explain your work to non-mathematicians?

Since I’m not a mathematician myself, let’s rather pretend that I’m trying to explain my work to a non-physicist.
Understanding the different phases of matter is one of the most important goals of my field, condensed matter physics. Sometimes we can intuitively grasp the difference between various phases: you can think of water, which can appear in liquid form, but also as solid ice, or as steam, which is in gas form. Sometimes, it is more subtle, like the difference between a material that can carry an electrical current — a metal — and an insulator. Quantum mechanics leads to phases of matter even less intuitive than this, with electrical properties that are neither those of an electrical conductor like copper, nor those of a simple insulator, or even semiconductor like silicium. In these materials, everything happens as if the electrons carrying the electrical current were split into three or more parts. This phenomenon arises from the collective behavior and interactions (electric and magnetic) between the electrons.
My work consists in finding new phases of (quantum) matter, and more specifically new ways that electrons could split up. Among the many forces competing at the microscopic level, I try to figure out which ones are essential, to help predict in which materials these phases might appear.

2. What would your own personal description of  “topology” be?

One often gives the example of a mug and a donut to explain the concept of topology. Imagine that you have a mug made of an extremely elastic material. By stretching it, you can transform your mug into an object with the shape of a donut. Had you started from a bun, it would have been necessary to pierce a hole to achieve the same result. The number of holes is a global property of an object, or topological invariant: if you stand too close, you can’t tell how many holes there are. You need to take a step back and look at the whole object. On the other hand, the details of the mug do not matter to determine the number of holes. In the context of physics, you would not be talking about the number of holes, but about something that you can measure in an experiment, like the conductivity or resistivity. In a quantum Hall experiment, a thin layer of semiconductor is sandwiched between two thicker layers, and subjected to a large magnetic field. If you apply a voltage on either side, you will observe the apparition of a voltage in the opposite direction. Another way of saying this is that the transverse resistance (or Hall resistance) is finite. If the temperature is low enough (around -273C), this resistance evolves step by step by forming plateaus as you tune the magnetic field. There is something very special about the value of the Hall resistance on the plateaus: it does not depend on the sample that you are looking at, nor does it depend on the material. It is in fact related by a simple proportionality rule to physical constants: the Planck constant h and the charge of the electron e, or rather the ratio e^2/h. This property is very unique and is so robust that it is used in metrology to define the ratio e^2/h. The robustness is a consequence of the Hall resistance being a topological invariant, much like the number of holes in an object.

3. How does your work relate, if at all, to the Nobel prize work?

A lot of my research relates to the work of David Thouless and Duncan Haldane, two of this year’s Nobel prize winners in physics. In 1988, Haldane proposed a lattice model where the quantum Hall effect could be realized in the absence of a magnetic field. The first projects I worked on as a PhD student consisted in understanding the physics of this model (and other similar ones) when the electrons hopping on the lattice strongly repel one another. One way or another, my research interests are in large part related to topology in condensed matter physics. I was attending a conference on topological phases of matter when I heard about the Nobel prize, and it was very nice to share this moment with colleagues and see the community react and celebrate the great news.

## Defining topology through interviews. Interview four with Tom Hockenhull.

The next of my  Defining topology through interviews  series is with Tom Hockenhull. Tom is a PhD student in mathematics at Imperial College London and his research is in knot theory. You should also check out the cool topology themed comics he does for chalkdust magazine!

1. What do you say when trying to explain your work to non-mathematicians?

I’m in the odd position that my field gets more intelligible for non-mathematicians the more specific I am (well, to a point). It’s actually quite hard to capture the idea of ‘topology’ in enough generality, I find – but it’s rather easy to explain the subfield of topology I work in, which is knot theory. I usually talk about unknot recognition. If I get a piece of string, tie a knot in it, and then fuse the ends together, I get a knot which is trapped in the string — I can’t untie it without cutting the string again.

The question is, if you give me a big mess of string, can I tell easily whether I can just straighten it out into a big unknotted loop without cutting the string at all? People can understand quite easily that this is a hard question in general.

So how might I go about doing it? Well, one way to recognise if two things are different in general is to look at some easy to discern property of them and see if they’re different. For instance, if I’m trying to work out whether two insects are of the same species, I might look at how many legs they have.

If I have a big tangle of string, I might try and work out some property of it that is different from that of a plain loop of string – then I can tell that it’s a different knot altogether. What’s the analogous notion for knots? Well, this is sort of what my work is in: there are a whole bunch of different properties we might try and use to compare knots. One which is easy to understand is the property of being able to colour a picture of the knot using exactly three colours (no less!), so that at each crossing I have three different colours, or all the colours are the same.

You can see that I can’t colour a simple loop of string with three colours (only one) – but I can colour the picture of the knot below.

It follows that I can’t turn one into the other without cutting the string!

2. What would your own personal description of ‘topology’ be?

There’s the standard ‘rubber sheet geometry’ or ‘the study of spaces up to deformation’, which are probably the most generally accurate, although they don’t really tell you much about what doing topology looks like or feels like. I suppose, though, that the problem is that ‘topology’ now encompasses a whole bunch of different areas that are rooted in the same place but are vastly different in their techniques, language and flavour.

3. How does your work relate, if at all, to the Nobel Prize work?

I’m not aware of any direct relevance – although that could be down to my ignorance! The word ‘quantum’ tends to pop up in descriptions of the Nobel work and in relation to a number of things to do with my work, but in my experience the use of the word quantum in my area seems to carry little relevance to its meaning in the world of physics.

## Defining topology through interviews. Interview three with Ruben Verresen.

The third interview in my Defining topology through interviews series is with Ruben Verresen, who was a masters student alongside Anna and I. Ruben is now a PhD student at the Max Planck Institute for the Physics of Complex Systems and is one of two physicists I am thrilled to have participating in this series.

1. What do you say when trying to explain your work to non-mathematicians and non-physicists?

Traditionally theoretical physics follows the doctrine of ‘separate but equal’, where theories for different aspects of the universe are quite disjoint, with the smallest scales being described by particle physics and the largest by cosmology. (And perhaps not so ‘equal’ since particle physics is considered the building block for all the rest.) This has changed in recent years. The dusty 18th century divisive thinking is making way for a more thematic structure, as it is slowly becoming clear that various different fields give rise to similar concepts. Instead of defining a topic in terms of some mechanical parameters (e.g. ‘the size of an atom’), one can categorize physics in terms of relevant concepts (such as ‘topology’, more about this later). If you allow me one tacky metaphor, to me concepts are the jewels of physics, forged in the heat of discussion, mined out of theories and eventually polished until they reach their purest form. A successful theoretical framework you can forget after a year, but a beautiful and insightful concept, once germinated in the mind, will never leave you. So when people ask me what I do, I usually just end up talking about one of these concepts (which usually helps my understanding as well).

Three such concepts that I am actively exploring are emergence, quantum entanglement and topology. These three ideas turn out to run quite deeply and seem to be intimately connected—with the Nobel prize recognizing this. Emergence is the idea that the best way to understand something is not by thinking in terms of its constituents but rather by some new effective way of thinking—for example if we want to understand a hurricane, there is not much point in thinking about air molecules.

Entanglement is a less intuitive property about the universe: it says that any two objects—say you and the red eye of Saturn—can share an instantaneous connection, such that one can affect the other faster than the speed of light. Since its discovery 80 years ago, there have been strong disagreements about what this property actually means, but in the last few decades physicists have been discovering that no matter what it ‘is’, it is key to many new physical features, some of which may very well change our future (for example through quantum computing, teleportation, et cetera). And finally there is topology…

2. What would your own personal description of  “topology” be?

I really like this question. There are some standard go-to answers for the general public, but I don’t like them. The first common one is that topology has to do with holes, the typical example being that a coffee mug and a donut are topologically the same because they have the same amount of holes. A second—and closely related—common answer is that topology is about the properties of an object that don’t change if you stretch, squeeze and deform it—but you’re not allowed to tear it! Again, you can imagine a plasticine donut being reshaped into a coffee mug. Maybe I just don’t like these answers because I have heard them too many times before, but more importantly: although they seem easy to understand, I would say they are not. The issue is that they seem very arbitrary: what’s so special about holes? Or about tearing versus stretching? If that’s all you know about topology, there is no reason to think it would be interesting. But it is! Very much so. Aside from leading to beautiful mathematics, in recent years it has been rising to ever increasing prominence in almost all fields of physics. There has even been a feedback effect: studying the topology of quantum fields—a physical question—has given new fundamental insights into mathematics itself. There is no hint of this in the above. Indeed, my biggest peeve with those answers is that there is no fire in them: if I explain topology by comparing a donut to a coffee mug, I can just see my listener slowly turning off. This is an immense pity, since topology is currently one of the most exciting concepts around.

So, what then is topology? Imagine having an object, like a mountain lion, or the universe, or yourself. Now think of all the properties it might have, like height or color or holes. Some of these properties you can assign to arbitrarily small regions: if you know the color of the mountain lion at every small piece of its hide, you can say you know all about the colors of the cat as a whole. We say these properties are local. Topology is simply the study of the properties which are not local! If I look at my door frame, I can assign sizes and lengths to every small piece, and together they add up to for example the height of my door, so height is a local property. But the fact that my door (luckily) has a hole in it is not local: none of the four sides can be said to have a hole, it is only when I take them all at once that it is there. In that sense my (open) door is topologically different from my wall.

Given this characterization, it is then perhaps not surprising that topology enters in those fields of inquiry where we are learning that ‘the (w)hole is more than the sum of its parts’. It can be a fun game to go outside and try to identify ‘local’ versus ‘non-local’ properties. But maybe we don’t even need to look too far: perhaps the human mind cannot be understood by piecing together local properties of the brain, and somehow only emerges when everything is considered at once.

3. How does your work relate, if at all, to the Nobel prize work?

To better understand what this year’s Nobel Prize in physics is about, we just need to appreciate how and why these three concepts of emergence, entanglement and topology are so closely related. Luckily we are now in a position to do exactly that! To see this, let me just reword and recap:

• Emergence is the idea that a system consisting of many particles can have properties which cannot be assigned to any of its individual particles, but only emerge when the system is taken as a whole. (H20 molecules do not have ‘wetness’, but a glass of water does.)
• Quantum entanglement tells us that we cannot think of a ‘thing’ as merely being the sum of its parts. In a classical world we know that if we study each particle in the universe separately, then we know the state and motion of the complete universe. In quantum theory we would know almost nothing, since most of the information is stored in these instantaneous connections between different particles. This is sometimes called quantum non-locality—and it is worth pointing out that this has been experimentally verified!
• Finally, as explained above, topology is about those properties of a ‘thing’ that cannot be attributed to any arbitrarily small piece of it.

So we see that these three concepts are all about some form of ‘non-locality’. More concretely, the Nobel Prize went to physicists who vastly aided our understanding by theoretically investigating physical systems where (some or all of) these phenomena play a crucial role. Personally I am most excited about this on a conceptual level, as it feels that we are learning something new and deep about how the universe works. But it is also of great importance for the more practical-minded: learning to harness the power of ‘non-locality’ means we have exponentially more possibilities at our disposal. In particular, ‘non-locality’ is often associated to strong stability (because if information is not stored locally it is much harder to accidentally destroy it) and physicists hope it could lead to the development of quantum computing, which is a whole exciting topic in and of itself. But anyway, despite there being deep links between emergence, entanglement and topology, it is still a basic question as to how deep the similarities really run. I’m certainly trying to find out!