Neurons in our brains do not seem to follow a pattern of orderly behavior. In fact, the activity of individual neurons rarely show a regular pattern in their impulses. Then, how can be explained the so marked cycles that govern the functioning of our brain, especially in periods such as sleeping? Researchers think that the so-called "background noise", the set of random signals that commonly occur in any system and that are usually considered undesirable, is responsible for that. Imagine a neuronal response, that reaches a certain threshold and which depends on the magnitude of the signal. Perhaps the signal alone, albeit cyclic and regular, does not reach the threshold required to trigger the response. However, if we add the background noise to this cyclical signal, the value of the signal increases and it is able to overcome the necessary threshold that will lead to the answer.
This mechanism has been described previously in other systems. Earth glaciations, for example, there have been fairly regular over millions of years. However, the axis of rotation of the Earth is not enough to explain these periodic glaciations. It is because of the stochastic resonance: they are random fluctuations (noise) which are added to the weak swing axis of rotation and give a regular pattern to glaciations. The phenomenon has also been described in biological systems. Paddlefish detects its food, plankton, through weak electrical oscillations that it emits a regular basis. A study at the University of Missouri shows that adding background noise (random electrical oscillations) to the system, the paddlefish more easily detects plankton and therefore eat more.
The scientific team of the UPF and the IDIBAPS has studied the cerebral cortex in situations that simulate the state of deep sleep, and has described, for the first time, this stochastic coherence in the brain. To do it so, they control the noise level by varying the cortex excitability, and have observed that the slow waves characteristics of deep sleep become more regular when the excitability, and therefore randomness, increases. Thus, they have detected that there is a noise level that is optimal for maximum regularity, from which noise dominates the order. They have shown that the background noise, comparable to the black and white dots that were once saw on an untuned TV, is what allows that the irregular signals generated by neurons are converted into oscillations which often show the regularity of a clock.
Article reference:
Collective stochastic coherence in recurrent neuronal networks
Bele´n Sancristo´bal, Beatriz Rebollo, Pol Boada, Maria V. Sanchez-Vives, Jordi Garcia-Ojalvo.
Nature Physics, Maig 2016. DOI: 10.1038/NPHYS3739