CHAOS IS GOOD FOR US
Netflix’s new series, ‘Eternally confused and Eager for love’, shows the struggle of a young man, Ray, whom an imaginary wizard, Wiz, guides. An utter state of disorder and confusion can sum up Ray’s journey through the quest of finding his girlfriend with Wiz’s help. The story resonates with most of us as the imaginary wizard is none other than our brain. Despite being a simple system composed of just neurons and glia, the brain can produce a complex looking behaviour that often appears chaotic, yet it is not entirely random. In the late 1900s, Poincare put forward chaos theory. This theory states that deterministic laws can govern the random and unpredictable behaviour in the system. For example, the synchronization of different neuronal networks is often a result of a non-periodic oscillatory activity generated by a timely fashioned firing of the neurons. Systems in chaos theory are represented by a phase space, which refers to n-dimensional space. Each axis of this space corresponds to one of the variables on which this system depends. In the brain’s context, these axes can be represented by neuronal conductances, firing rates, synapses, etc. Now the state of this system evolves from one point to another to achieve stability, called the attractor point. This evolution captures the dynamics of the system. Small perturbations cannot change the state of this system like the times our brain fogs but still plunges back, much like the minor embarrassments that our protagonist Ray faces daily because of Wiz’s ideas. However, suppose the attractor point changes because of a significant change. In that case, it is an unstable system, and the corresponding point from where it changes its trajectory is called a bifurcation point. Recent studies have indicated that disorders like epilepsy, schizophrenia and loss of consciousness can result from such bifurcations in the attractor state of the brain, indicating that the brain exists at the border of this chaos. Therefore, predicting these bifurcation points can be crucial to understanding system dynamics. If Ray had predicted this point in the reel world, he would not have been so terrified at the end of the first season. Nevertheless, coming back to the real world, Yakovleva et al. (2020) predicted seizure onset in epileptic patients by studying their EEG signals under the purview of chaos theory.
Therefore, a systematic assessment of differences between attractor dynamics of healthy subjects and patients can help identify better prognostic and diagnostic methods. And not just this, maybe these dynamics can also help us decode other cognitive aspects of the healthy brain, if not Ray’s fate in next season.