Why Sleep?

Phys. Rev. Focus 21, 1
The brain may need sleep in order to concentrate on one activity at a time.
Getty Images
Concentrate on napping. Researchers aren’t sure why animals need to sleep, but a new study suggests that any system is more efficient when it focuses on one task at a time, rather than trying to multitask. With a sleep-wake cycle, the brain collects information during the day and processes it at night.

Why we sleep remains a mystery. Competing theories claim various “house-cleaning” brain activities occur during sleep, but they can’t say why we need to power down to accomplish them. A study in the January Physical Review E suggests that a sleep-wake cycle, allowing the brain to focus on one task at a time, may be the most efficient way to operate. The researcher shows mathematically that processing a continuously changing resource–sensory input, in the brain’s case–is best done “offline,” when there’s no input. This sort of analysis may lead to a more precise biological explanation for why sleep and other biological cycles evolved.

Humans spend a third of their lives asleep, and sleep is essential to our health. But scientists do not yet agree on its purpose. One theory is that the brain requires sleep to consolidate information collected during the day, while another theory says that the brain needs to sweep out harmful free radicals that build up during waking hours. But turning off the senses seems impractical, if not outright dangerous. It would seem better for an organism to perform sleep-related tasks in parallel with being awake.

To address this question, Emmanuel Tannenbaum of Ben Gurion University in Beer Sheva, Israel, proposes the concept of temporal differentiation, in which a system focuses on one task at a time, rather than trying to multitask. The advantages of a time-varying strategy have been studied in traffic control, computer programming, and operations research. But Tannenbaum believes he is the first to consider the brain as a “factory” for information processing, for which certain routines are more efficient than others.

In his paper, Tannenbaum analyzes two models. The first involves a tank with two pipes–one for filling and one for emptying–which can be opened one at a time. Assuming the incoming resource flow continuously switches between “on” and “off,” Tannenbaum proves mathematically that one way to maximize the flow through the tank is to fill whenever the resource is available and empty when it isn’t. The resource is analogous to sensory information that fills the brain and needs to be processed (emptied). Tannenbaum reasons that many animals can only receive visual information when there is light, so an efficient strategy, according to the tank model, is to be alert during daylight hours and devote all one’s time in darkness to processing. As a comparison, Tannenbaum calculates the productivity of alternating rapidly between filling and emptying (equivalent to being half-asleep and half-awake simultaneously) and finds this approach less efficient.

Certain sleep behaviors, like episodic REM sleep and nocturnal habits, do not fit this picture, so Tannenbaum formulated a more generic model, in which a resource supplied at a fixed rate is processed in three separate steps, such that the initial, intermediate, and final products are all present in varying concentrations. The model bears some resemblance to the cyclic reactions of circadian rhythm proteins, which keep many organisms on 24-hour clocks even in complete darkness. Tannenbaum finds that a temporally differentiated case, where the steps are performed separately, is 33 percent more efficient at producing the final product than an undifferentiated case, where all three steps run simultaneously. This result depends on the details of the model, but he believes that optimization through temporal differentiation might explain why certain cyclic behaviors evolved.

James Krueger, a sleep expert at Washington State University, says that this is definitely a new approach, but he thinks Tannenbaum ignores a host of sleep phenomena, such as the localization of sleep to specific areas of the brain and the fact that some sensory input continues during sleep. Still, he welcomes the effort and admits that “any new idea cannot address everything at once.”

–Michael Schirber

Michael Schirber is a Corresponding Editor for Physics Magazine based in Lyon, France.

Subject Areas

Nonlinear Dynamics

Related Articles

The Neuron vs the Synapse: Which One Is in the Driving Seat?
Complex Systems

The Neuron vs the Synapse: Which One Is in the Driving Seat?

A new theoretical framework for plastic neural networks predicts dynamical regimes where synapses rather than neurons primarily drive the network’s behavior, leading to an alternative candidate mechanism for working memory in the brain. Read More »

Nonreciprocal Frustration Meets Geometrical Frustration
Nonlinear Dynamics

Nonreciprocal Frustration Meets Geometrical Frustration

New theoretical work establishes an analogy between systems that are dynamically frustrated, such as glasses, and thermodynamic systems whose members have conflicting goals, such as predator–prey ecosystems. Read More »

Quasi-integrable Arrays: The Family Grows
Nonlinear Dynamics

Quasi-integrable Arrays: The Family Grows

A new approach to solving arrays of two-dimensional differential equations may allow researchers to go beyond the one-dimensional oscillator paradigm. Read More »

More Articles