"I am sorry, but your brain suffers from avalanches" is a diagnosis that should be a thing. The cure should involve a St. Bernard digging neurons out from under piles of neurotransmitters. Unfortunately, everyone's brain suffers from avalanches. Indeed, I can safely diagnose anyone who does not suffer from avalanches as dead. (And you thought the barriers to graduate school were intellectual?)
An avalanche in the brain is basically a small, generally inconspicuous event that triggers a massive cascade of neuronal activity. These are observed to occur without any external triggers.
So why do they occur? It has been thought that these avalanches should confer some sort of benefit, but new research suggests that it might just be a noisy accident.
Why do neurons cascade?
On one level, the explanation for brain avalanches is fairly simple and very unenlightening. The brain is not linear—if the right neuron fires at the right time, it can trigger a disproportionate response from the neurons that it is connected to. These too can trigger a large response, with a cascade that spreads fast, far, and wide. But, that explanation, while having the benefit of being correct, does not actually tell us much.
For instance, it does not tell us why these events are allowed to occur. If your computer did this, it would crash. And it's possible that parts of the brain do crash during a cascade. That sounds like a bad thing, so you might expect that avalanches provide some advantage. Perhaps they are required to enhance computation? Maybe they are the undesirable consequence of operating the brain at a point where learning is as fast as possible?
There have been two general approaches taken to figure out what's behind this phenomenon. There are people who actually know what a neuron looks like and can successfully distinguish brain tissue from burnt toast two times out of three. They investigate neuron cascades by examining how the brain actually functions. Physicists don't and can't, so they make mathematical models instead. These models are extensively tested under all sorts of conditions. (This sort of investigation represents a great savings in burnt toast.)
These models bear the same resemblance to the brain and the neuron as my bicycle does to an albatross. Everyone, including the physicists, knows this—so why do they think modeling will work? Well, they are trying to understand broader questions about the general behavior of the brain as a network. And these questions can be answered using models that are really shadows of the real thing.
Why do these models work
Our confidence in these relatively simple models goes back to solid state physics and the idea of a phase transition. Phase transitions are things like ice melting to liquid water, or a magnet losing its magnetism as it is heated. Phase transitions are, physically, all very different from each other. Yet the mathematics that describes the way the phase transition occurs has an uncanny similarity to those examples. Embedded in that is the idea of a critical point. On one side of the critical point, the material behaves one way; on the other side, the material behaves in another.
It turned out that this idea is much broader than this sort of transition: all sorts of things, like dripping taps, animal populations, chemical reactions, the behavior of markets, and many more, seemed to be amenable to the same analysis. And, yes the brain is included in that. Epilepsy, for instance, seems to be well-described by these models.
So let's bring this back to neural avalanches. This sort of behavior is very similar to the behavior of, for instance, the orientation of magnetic spins during a paramagnetic phase transition. But neuronal avalanches occur relatively frequently, which would mean that the brain operates near a critical point. Researchers have suggested that the computational efficiency of the brain is enhanced by operating near the critical point, which would mean that it's an adaptive feature.
But there is an idea in evolutionary theory that tells us that not all traits are around because they provide a benefit. Some traits end up in place due to drift and others because they're a side-effect of something useful. It turns out that this concept can both describe the utility of neural avalanches as well as how they ended up being a feature of brains in the first place.
Now, what follows is a physicist talking about evolution; those with an aversion to abuse-of-biology should look away now. It was quite shocking for me to learn that many traits are not selected for or against by natural selection but can arise inadvertently. While some mutations are advantageous, and some are plain bad, most don't cause a big enough change to be noticed—they are neutral. Neutral changes may spread in a population because they are accidentally linked to something that is selected for. A neutral trait can also end up spreading in the same way that an avalanche occurs: it happens to be in the right place at the right time. The point being that neutral traits in genetics exhibit population dynamics every bit as complex as critical point phenomena but without any critical points.
If we can get that sort of behavior in population genetics without being close to a critical point, maybe we are seeing exactly the same thing in the brain with neuron cascades?
Using a well-accepted model of a neuron, connected up as part of a neural network, researchers tried to determine if neuronal avalanches could be described by neutral behavior. And this is where the power of models comes into its own. The model parameters are well known, and the critical points can be discovered and accurately characterized. Then, it becomes possible to catalogue the different types of behavior seen, depending on how the neurons are connected and the threshold for when neurons will fire in response to other neurons. This is exactly the sort of experiment that is next to impossible when playing with real neurons.
Not all is well with models
In particular, the researchers chose settings for which it was known that the neural network had a discontinuous transition between a very active state (lots of neurons firing frequently) and a low activity state (lots of neurons firing infrequently). In the active state, cascades or avalanches had been observed by other researchers, and the network's activity is consistent with behavior observed in real brains.
This analysis used a trick that cannot be used in real observations. The researchers searched the computational data to identify the neuron that triggered the avalanche and, from there, traced the chain of activity to determine things like the duration and size of the avalanche. In a real measurement, you never know which neuron caused the avalanche—instead, you have to kind of group neuron firing by time and use temporal proximity to determine which neurons caused which to fire.
If you do this with the computational data, however, the avalanches of the model neurons do not resemble those of real brains. That seems kind of bad if you want to make predictions. So it's not clear whether the other information we're getting out of the computational model is relevant.
To go beyond that, the researchers developed a simplified model that had several desirable features. Depending on the parameters, it had a discontinuous phase transition (so a sudden jump from an active state to a quiescent state), and a continuous phase transition (the activity rate smoothly changes from one state to another, but the rate of change exhibits a sudden jump).
And, of course, close to the phase transitions, the neural network showed cascades. But, importantly, the network has cascades even when there is no phase transition nearby.
Considering the advantages that were ascribed to the brain operating near a critical point, does operating in neutral territory have any advantages? The researchers offer some speculation along these lines. For instance, the order and intensity of the neurons firing in a network can encode information. The researchers speculate that neutral avalanches, because they are causal (e.g., each neuron triggers others to fire after they have fired), could provide the source for the initial firing activity that can be associated with, and encode, information.
But, frankly, this is where it all goes wrong. The model, along with others like it, offers a great insight into the statistics of network behavior. Unfortunately, linking that to actual-factual activities, like recognizing a face or adding two numbers, is rather more tenuous. The strength here is that the research might suggest some new avenues to understanding how the brain learns. But even if it does, it will be difficult to link research in real neurons back to the models.
Physical Review X, 2017, DOI: 10.1103/PhysRevX.7.041071