Is the brain uncontrollable, like the weather?

The brain may be chaotic. Does that mean our efforts to control it are doomed?

Illustration of a meteorologist pointing to an aerial view of clouds swirling over a brain-shaped land mass.
Illustration by Kouzou Sakai

Complex systems operate in ways that are hard to predict from their parts alone, because their behavior is influenced by how their parts interact. As the famous saying goes, more is not just more; “more is different.” Brain researchers are increasingly turning to the idea that complex systems support many of the brain’s functions, from spatial navigation to memory function. Likewise, they are beginning to realize that many types of brain dysfunction reflect a complex system gone awry, such as when the epileptic brain enters a seizure.

Creating a seizure in a computer simulation is trivial; it’s what generally happens when you incorporate excitatory feedback loops with no inhibitory force to counter them. It’s vastly more challenging, however, to create a model that approaches the complexity of the brain that isn’t seizing. Something akin to this delicate balance is thought to stabilize a number of brain systems, including those that maintain sanity (versus psychosis) and mood stability (versus mania or depression). Indeed, that the brain somehow exists in an exquisite equilibrium the vast majority of the time seems like nothing short of a miracle — given that it relies on numerous giant amplifying feedback loops, offering many avenues to disruption.

Beyond the brain, many other complex systems live in a similarly delicate balance. Ecosystems can break into toxic blooms. Snow packs can break into avalanches. Weather can break into hurricanes and tornados. These systems, however, are not just complex but chaotic, meaning that they are subject to the butterfly effect, by which even tiny perturbations can sometimes push the system out of whack. (The phenomenon gets its name from the way that Edward Lorenz first described it: as if a butterfly flapping its wings over Brazil could cause a tornado in Texas.)

The butterfly effect explains why weather forecasts are much more accurate for the next few days than for the next few weeks. We can measure current conditions with only a certain degree of accuracy, and those small errors in our measurements of what’s happening now turn into big errors in our model predictions later.

For the same reason, chaotic systems are exceedingly hard to control, which naturally leads to a question: If the brain is chaotic like the weather — if seizures, depression and psychosis are the analogs of hurricanes — is there any hope of bringing it back to a healthy state via a brain-based intervention, such as a drug or brain stimulation? Or are our efforts to control the brain’s complex systems doomed from the outset? In this essay, I contemplate that question. I also asked 14 experts in complex systems to chime in.


reakthroughs in weather research date back to the early 1900s, when researchers began to formulate the types of weather forecasting models that we use today. Often forgotten is that the explicit goal of early weather research was not just to predict the weather but also control it — both to head off disasters and to weaponize it. Indeed, weather control was the explicit goal behind “the Meteorology Project,” organized by Princeton University mathematician John von Neumann and industrial researcher Vladimir Zworykin, the latter of whom contributed to developing the television. As the Second World War began to ramp down in the mid-1940s, the pair approached government officials in Washington, D.C., to request funding for their two-step plan to create a new computing infrastructure to predict the weather (the outcome of which is reflected in today’s computers as the von Neumann architecture) and to control the weather using those predictions. As described in their proposal, “Only with exact scientific weather knowledge will effective weather control be possible.”

Over the next few decades, other researchers around the globe sought to control the weather. In the United States, a government effort called Project Cirrus, for example, focused on disabling hurricanes. In 1947, the team attempted to dissipate a hurricane, conveniently forecast to remain at sea, by dropping 80 kilograms of dry ice on it from a B-17 bomber. The intent was to disrupt the hurricane’s internal structure, but instead the worst possible thing happened: The hurricane’s trajectory shifted 130 degrees, and it landed in Georgia. Project Stormfury resuscitated the idea in 1962 and lasted a few decades but never achieved any success. In short, 75 years after the Meteorology Project, we’ve achieved von Neumann and Zworykin’s first goal, forecasting, but weather control hasn’t really panned out. Today, weather control happens in subtle ways, such as when China precipitated rain during the 2008 Olympics to ensure that it did not happen at an inopportune time. But because of chaos, we still cannot influence the paths of hurricanes in any predictable way.

The chaos in our brain is a feature we can control and not a maladaptive ‘bug’ we need to quell.

Kanaka Rajan

For brain researchers, the history of weather control should give us pause. In our own attempts at controlling the brain, how likely is it that we’ll face the same difficulties that foiled weather researchers? The answer depends greatly on what type of thing the brain is, and that’s still a bit difficult to say. One theory suggests that the brain exists on the knife edge between order and disorder, in a state called “criticality.”

This critical brain hypothesis builds on work from physics focused on how phase transitions happen, such as when water changes to steam at a high temperature or carbon changes to diamond at high pressure. In these cases, the large-scale collective property of the system changes when a single parameter, such as temperature or pressure, crosses a critical point. But in other cases, this is not quite the right way to think about it — control derives not from an external parameter such as temperature, but rather from within the system itself. Grains of sand dropped onto a sandpile, for example, elicit avalanches that are just large enough to keep it at the boundary between piling and flattening. Birds in a flock move collectively, but individuals can affect the group’s behavior, which is crucial for responding to predators. The system organizes itself in a way that maintains it at that critical boundary of a phase transition.

The critical brain hypothesis accounts for a similar boundary. The gist is that if the brain is too disordered, it can’t do anything very useful, akin to being sedated. If it’s too ordered, it also can’t do anything, akin to being in a seizure. But at the edge of order and disorder, it’s optimally positioned to do all the many things it needs to do. The idea follows from studies of criticality in artificial recurrent neural networks, which perform optimally when positioned at the critical boundary. In such networks, the strength of recurrent interactions between model neurons controls where on the spectrum the network sits. If neurons are too interconnected, a small input will trigger every neuron to fire; if neurons aren’t connected enough, even a giant input will peter out before it makes its way through the network. But if neurons are connected by just the right amount, an input can and will be processed in a sensible way by a subset of model neurons.

Maintaining the brain at the critical boundary between order and disorder requires some type of exquisite regulation. In artificial neural networks, the balance of excitatory and inhibitory connections maintains criticality — the same may be true in the brain. Along time scales of hours and days, plasticity and other forms of homeostatic regulation, which refers to neurons’ capacity to regulate their own excitability relative to total network activity, could help maintain this balance. Along time scales of seconds to minutes, firing-rate adaptation could play this role. Conversely, anything that upsets these mechanisms, including mutated ion channels, broken plasticity mechanisms or aberrant neurotransmission, could throw the brain into a perpetually or partially disordered state.

Although it is a compelling idea, it has been very difficult to test hypotheses of brain criticality. Ideally, we would do things like study how the brain evolves after it is reset to similar initial conditions, and that’s just not possible. Instead, most attempts seek to identify the types of signatures typical of systems in a critical state. Phenomena akin to avalanches have been observed in neurons’ spiking patterns, for example: bursts of activity in cell cultures that occur with a power law distribution, where small bursts are much more likely than large ones. Another measure relies on the reverberation expected to be triggered in a system, which creates long-range correlations across time. To date, the evidence supports the critical brain hypothesis, but it’s far from definitive. We just don’t yet know.


hould the brain prove to be chaotic — or close to the critical boundary — what are the implications? Does it mean all hope of control, and therefore treatment, is lost, as is the case for the weather? Or is that the wrong way to think about it? We might consider a few possibilities.

First, some haven’t given up on the idea that chaotic systems can in fact be controlled with targeted perturbations. Researchers have figured out ways to control chaotic systems, in theory, via approaches such as the continuous injection of a signal, based on model predictions, as well as perturbations among attractor states. (Attractor states are patterns of activity that a network relaxes into, a bit like a ball rolling into a valley.) But for such an approach to be helpful, researchers would first have to create very extremely precise models of the brain. They would also have to develop ways to control the human brain with much more precision than is typically available today. Under the assumption that this approach would take the form of manipulating either genetic expression or brain activity, it would likely require the control of many genes or stimulation sites.

Second, insofar as disordered states such as seizures are signs of the brain entering subcritical or supercritical states, the brain appears to have internal mechanisms for restoring normal function. Under severe conditions, seizures can continue for hours — illustrating that the brain is physically capable of it — but typically last just minutes. Likewise, people often enter depressive and psychotic episodes and then exit them days or weeks later. Unfortunately, we don’t really understand the mechanisms by which the brain self-organizes and renormalizes. A better understanding of those mechanisms could lead to better treatments or preventions, akin to the fences used to prevent avalanches.

Of course, the answer may be the one we wish were not true: It may be that in some cases, we simply cannot control the brain — at least not in the ways we would need to treat some types of dysfunction, such as epilepsy, psychosis and depression.

What do researchers predict?

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