October 03

A critical point about the brain

Neuroscience is one of the most fascinating fields of study, spanning science, art and philosophy. Okay, I may be biased here, but if you really think about it, we are trying to understand emotion, motivation, intelligence, and consciousness. We are trying to discover what sets humans apart, what unifies animals, what causes neurological disease, all using the very organ we are trying to understand. Neuroscientists attempt to do this using many different approaches. Some of us study molecular pathways and proteins in the nervous system, some of us study circuits that connect different brain regions, and some of us use computational approaches to do this. These computational approaches often involve developing mathematical models and then trying to fit existing, observed phenomena to those models. These models are predicated on known physical properties and molded to fit the data we collect. We can then use these models to drive forward our understanding of how the brain may be working and predict future discoveries. One such model is called the critical brain hypothesis. It was first put forward in the 1950s and has been debated by neuroscientists and physicists alike ever since.

A Goldilocks situation

A diagram depicting a magnetic material. Each arrow represents the orientation of the charge distribution of each atom. Each arrow is lined up and pointing in the same direction, giving a net direction to the whole magnet.

Before we dive into what this model is suggesting, let’s talk about the concept of criticality. Criticality, in physics, is the exact point at which a system is transitioning between two states. Take, for example, the magnet. Physical materials, such as iron, peanut butter and jelly sandwiches, and you, are made up of atoms. These atoms are composed of negatively and positively charged subatomic particles. These bits of negative and positive stuff can create what is called a magnetic moment where the negative stuff is on one side and the positive stuff is on the other side (kind of like a battery). You are familiar with magnets, right? Magnets exist because the individual atoms that make up the material have a magnetic moment and these magnetic moments are all aligned.  Each magnetic moment influences the magnetic moments around it to also align in the same direction.  This alignment creates a net positive and net negative directionality of the charge of the material. Alright, so now we have our material with all these little magnetic moments pointing in the same direction, which we call a magnet.

Imagine we begin heating up this magnet to really high temperatures. By adding heat, we will create disorder in the material, and at a hot enough temperature the magnetic moments will no longer be perfectly lined up with one another. They will all point in random, unorganized directions and, therefore, the magnet will cease to be magnetic. So on one end of the spectrum, at normal temperatures, the material is in one state (i.e. it’s magnetic) and on the other end, at high temperatures, it is in a different state (it’s not magnetic). The critical point for this system is the exact temperature at which the system transitions from being magnetic to not being magnetic. At this point of criticality, it takes on an entirely unique set of properties. If you add just the right amount of heat to get to the critical temperature, then the tendency for the atomic orientations to align is exactly counterbalanced by the disorder caused by the heat. Now instead of uniform order there will be local regions where a group of atom orientations are pointed in the same direction. At any given time you may see clusters of magnetic regions within the material. The sizes of these regions vary so this state of criticality is an interesting mix of order and disorder, and is constantly changing over time.

A diagram of atomic orientations in a magnet transitioning into and out of criticality. (Left) At low temperature, nearest neighbor interactions dominate over temperature fluctuations. As a result, almost all the spins align in the same direction, producing a very ordered state. (Right) At high temperature, temperature fluctuations dominate over nearest neighbor interactions. As a result, the spins point in different directions, producing a very disordered state. (Center) At some critical temperature, nearest neighbor interactions and thermal fluctuations balance to produce a complex state [2].

How are brains like magnets?

Now how does this all relate to the brain? Like I said before, modelling takes observed data and seeks to fit them to known patterns. Many studies which have recorded neural activity across large spaces and over long time intervals have shown data that fit very well with the rules and characteristics of systems operating near a critical point. This does not necessarily prove the brain is in a state of criticality, but it suggests that it very well may be.  Without going into the rigorous (and rather boring) math, many experimental recordings of large groups of neurons have shown bursts of spontaneous activity. If you count the number of neurons active in each distinct burst, the burst sizes follow a particular pattern [1] (called a power law distribution), which are similarly seen in systems operating near a critical point. These events, called neuronal avalanches, have led to the hypothesis that the collective dynamics of large neuronal networks in the brain operate close to the critical point of a phase transition.  

(a) An example of neural activity, recorded via electroencephalograpy (EEG) showing these random activity bursts, with inter-burst intervals (IBIs) in between them. (b) The relationship between the length and duration of bursts follows a power law [3].

In this case, you can think of individual neuron activity like the orientations of the atoms in our magnet example. In a state like the low temperature, ordered magnet example, all the neurons in the brain would be active or inactive at the same time (i.e. they would all share the same properties). In a state like the high temperature, random magnet example, neurons would fire randomly and there would be no correlation between their activity. But in a critical state between these two scenarios, neurons would overall seem to fire somewhat randomly with various sized groups of neurons firing in a synchronous way, which is exactly what we observe experimentally. This all may seem very arbitrary and math-centric. So what if the brain is operating at a so called critical point? Some argue that this property of brain activity is the precise reason brains are able to do what they do. These arguments state that brain criticality is what gives the brain maximum adaptability, resulting in its capacity for information processing and allows the mind to function as it does. [4]; [5]; [6]; [7]; [8]; [9].

You may be wondering what the brain’s equivalent to “temperature” from the magnet example is. How exactly does the brain achieve and maintain this precise critical point of activity? That question is still up to speculation and debate. The brain’s characteristics which drive these patterns of activity are not fully understood. Some proponents of the critical brain hypothesis argue it is the precise and well-maintained wiring of the neurons in the brain. There is a great deal of both synapse creation and degradation that keeps the connections between neurons in the brain under tight regulation. The greater the degree of neural connections, the more likely neuronal activity is to propagate across the brain. The brain derives its properties from its ability to maintain just the right level of neural activity to achieve all its tasks without over-saturating the system.

When the brain tips out of balance

This perfect balance of neural activity is great and all, but what happens when it goes wrong? The issue for a system trying to maintain a precise state, such as the critical temperature in our magnet example, in order to maintain its properties is that the slightest fluctuation can throw it all out of whack. This may be exactly what happens in many neurological disorders. One example is in the case of epilepsy, where spontaneous neural activity cascades out of control, causing the aberrant over-activation of the brain, called a seizure. This would be equivalent to the temperature of the magnet being too low to maintain criticality causing an increased correlation between the states of atoms. Some have also argued that individuals with certain psychiatric disorders like schizophrenia encephalopathy, bipolar disorder and schizophrenia may have fluctuations from an optimal critical point in their brains [3].

Being critical of criticality

                While the notion that the brain exists in this perfectly balanced critical state is cool, it is by no means accepted by all in the field of computational neuroscience. Many people argue that just because some neural data follow the same math as critical systems, this does nothing to prove the brain is in criticality. There is a reason we still know so little about how the brain can accomplish what it does, despite the huge amount of time and resources that have been put into it: the brain is complicated. Trying to lump the brain in with a bunch of known physical systems like magnets will inherently create problems. Perhaps we will never settle on a grand unified theory of how the brain works, but the idea that the brain is perched on a very fine line between not enough and too much activity is an intriguing one. I find the notion of this very property allowing brains to become capable of thought and information processing so fascinating! But then again this is my brain talking. 


  1. Beggs, J. and Plenz, D. (2003). Neuronal Avalanches in Neocortical Circuits. The Journal of Neuroscience, 23(35), pp.11167-11177.
  2. Beggs, J. and Timme, N. (2012). Being Critical of Criticality in the Brain. Frontiers in Physiology, 3.
  3. Cocchi, L., Gollo, L., Zalesky, A. and Breakspear, M. (2017). Criticality in the brain: A synthesis of neurobiology, models and cognition. Progress in Neurobiology, 158, pp.132-152.
  4. Friston, K., Breakspear, M., Deco, G., (2012b). Perception and self-organized instability. Front Comput Neurosci 6.
  5. Friston, K.J., (2000). The labile brain. II. Transients, complexity and selection. Philosophical Transactions of the Royal Society of London B: Biological Sciences 355, 237-252.
  6. Gollo, L.L., Breakspear, M., (2014). The frustrated brain: from dynamics on motifs to communities and networks. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 369.
  7. Kastner, D.B., Baccus, S.A., Sharpee, T.O., (2015). Critical and maximally informative encoding between neural populations in the retina. Proceedings of the National Academy of Sciences 112, 2533-2538.
  8. Shew, W.L., Yang, H.D., Petermann, T., Roy, R., Plenz, D., (2009). Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality. Journal of Neuroscience 29, 15595-15600.
  9. Yang, H.D., Shew, W.L., Roy, R., Plenz, D., (2012). Maximal Variability of Phase Synchrony in Cortical Networks with Neuronal Avalanches. Journal of Neuroscience 32, 1061-1072.
  10. Featured image (skull animation) by Bill Domonkos