Brain waves are modeled after the rhythms of nature

Recent research by Jack Cowan of the University of Chicago sheds new light on the connection between brain waves and the rhythms of nature. At the annual meeting of the American Association for the Advancement of Science, Cowan presented findings on transitional phases in brain function, which may have significant implications for understanding human consciousness and its states. His work indicates that the principles of physics governing the behavior of molecules can be applied to the analysis of neuronal function, potentially opening up new avenues for neuroscience research.

The discovery you are about to read about opens up a completely serious scientific discussion, including on the impact on human consciousness and what its consequences may be.

The same physical principles that govern molecules that, for example, condense a gas or cause a liquid to turn into a solid also apply to models of how neurons in the human brain work. University of Chicago mathematician Jack Cowan proposed this and similar ideas at the annual meeting of the Association for Advanced Science in Boston.

"Structures with a lot of parts can exhibit abrupt transitions from one state to another, what physicists call transitions," said Cowan, a professor at the Institute of Mathematics and Neuroscience in Chicago. "Strange and interesting phenomena occur at transitions."

When fluids undergo phase transitions, they change to a gas or solid state. When the brain undergoes phase transitions, it moves from random to patterned activity. "The brain at rest exhibits random activity, or what physicists call Brownian motion," Cowan said.

Although much of his work involves deriving equations, Cowan's findings also align with laboratory data from studies of the cerebral cortex and electroencephalograms. His latest findings demonstrate that the same mathematical tools physicists use to describe the behavior of subatomic particles, and the dynamics of fluids and solids, can now be applied to better understand how the brain generates different rhythms.

These include delta waves generated during sleep, alpha waves of the visual brain, and gamma waves, discovered in the last decade, which appear to be linked to information processing. "Brain activity during the resting state seems to have a static structure that is characteristic of certain transitional phases," Cowan said. "The brain likes this state because information processing is optimized during this state."

At this stage of the research, Cowan says it's too early to try to determine how these transitional phases might be related to neurological conditions or states of human consciousness. "That will only be possible in the future," Cowan said.

Another component of his recent research is the closed relationship between spontaneous model generation and chemical reaction networks. In this study, Cowan shows how mathematics can help explain visual hallucinations and how the visual cortex acquires stripes that are visible to the naked eye after being removed from a dead body.

This line of research on model emergence may have been pioneered by Alan Turing, who also founded modern computational science," said Terrence Sejnowski of the Salk Institute for Biological Studies in La Jolla, California, a leading expert in computational neuroscience.

Cowan's quest to understand the brain using numerical methods spanned more than four decades. During that time, he collaborated with numerous philosophy doctoral students and colleagues in physics, mathematics, biology, and neuroscience.

In 1972, he and postdoctoral student Hugh Wilson, now at Canada's York University, formulated a set of equations that could describe the dynamics of neural networks. Now called the "Cowen-Wilsonian" equations, they became the foundation for studying neural networks. "But I always knew those equations were wrong, so I kept thinking about them," Cowan said.

Then, in 1985, he read an article in a Japanese journal that described a statistical physics approach to chemical reaction networks. "It took me years to understand how to apply this tool to biological networks. There are times when there is an analogy between the behavior of chemical reaction networks and the behavior of neural networks," Cowan said.

His research career began in 1962, when, as a graduate of an electronics technical school, he worked alongside the founders of neural network theory. Among them was Norbert Wiener, who died in 1964 before the team began tackling the problem Cowan continues to study today.

"I didn't really understand what he was saying until I started working on it myself. Wiener was one of the great mathematicians of the 20th century," Cowan said.