Using Complexity Theory To Understaind The Brain
Using self organized critical states models to understand brain behavior.
Article Introduction (Via NeuroNarrative)
What do our brains have in common with piles of sand, earthquakes, forest fires and avalanches? Each of those is a dynamic system in a self-organized critical state, and according to a new study in PloS Computational Biology, so is the brain.
Systems in a critical state are on the cusp of a transition between ordered and random behavior. Take a pile of sand for example: as grains of sand are added to the pile, they eventually form a slope. At a certain point, the sloping sand reaches a “critical state,” and at this point adding even a single grain can cause an avalanche that may be small or large. We can’t predict the moment or size of the avalanche, but we know that when the critical state is reached, there are several potential responses that may occur in the system (pile of sand).
Additional Article Excerpts (Via NeuroNarrative)
While self-organized critical state models have been used to model brain dynamics before (in simulated neural networks), this study took the additional step of linking modeling with neuroimaging to measure dynamic changes in the synchronization of activity between different regions of the brain’s network. After developing a profile of brain dynamics with neuroimaging, researchers compared the profile with synchronization of brain activity in critical-state computational models. They found that the computational model results exactly reflected the dynamic activity in the brain, which strongly suggests that the brain exists dynamically in a critical state.