Coherence resonance in neural networks: Theory and experiments

The paper is devoted to the review of the coherence resonance phenomenon in excitable neural networks. In particular, we explain how coherence can be measured and how noise affects neural activity. According to our research, intrinsic brain noise, which affects neural activity at the microscopic level, has a positive effect at the macroscopic level related to brain connectivity. Namely, it coordinates responses of different brain areas and forces their interaction to efficiently process sensory information. We find that brain noise can be altered as a result of attention and cognitive training to optimize the efficiency of information processing. Numerous experimental and theoretical studies provide substantial evidence for beneficial effects of internal brain noise on cognitive performance. Furthermore, coherence resonance in the brain response to a cognitive task not only increases neural activity in certain brain areas, but also provides pathways for neural communication between distant brain areas. In addition, the study of coherent resonance allows finding optimal parameters for better performance and efficient control of brain–computer interfaces.