The brain is not a computer, as anyone reasonably familiar with both should know. There are many similarities – both store and process information. But the fundamental architecture and function of the silicon on your desktop and the meat inside your skull are very different. That is why computers which are merely scaled up in power and speed will not spontaneously become conscious.The computational paradigm offers some insights into how the brain works, but it is not enough. Neuroscientists are searching for deeper understanding of brain function, particularly how it relates to consciousness. For example, it is known that the brain is organized as a massively parallel processor. There is also the neural network model of brain organization which tries to understand the brain as a collection of overlapping patterns of connectedness (networks).
At the same time there is a modular model of the brain which tries to understand brain function as a collection of anatomically identifiable modules that each have a specific function and interact interact to create the net effect of both consciousness and subconscious processing. I think that the network model and modular model are not mutually exlusive but are each part of the picture.
Now a newly published paper in PLOS Computational Biology argues that the brain operates at a critical point between organization and chaos – a state previously described as self-organized criticality. This is more of a description of the dynamic function of the brain, rather than its organization, and again is complementary to the modular and network models.
The concept of self-organized criticality (SOC) emerged out of physics, mathematics, and efforts to understand complexity in nature. SOC explains how complexity can spontaneously emerge from simple interactions, such as individual cells interacting with each other. Such complexity would have various features. These include the property of being scale invariant – meaning that the overall structure of the complexity does not change significantly at different scales.
If that sounds familiar it’s because that is the defining feature of fractals described by Benoît Mandelbrot. As you scale up and down through a fractal pattern the amount of complexity remains the same.
Another feature of SOC is that it occurs at the critical point between ordered and random behavior, such as might exist between different phases of matter.
And a very important feature was described in 1987 by Bak, Tang and Wiesenfeld – that complexity in an SOC system emerges in a robust manner, which means it is not sensitively dependent on conditions. Therefore, the system can maintain its complexity even through great changes in the parameters of the system – the system does not have to be “finely tuned” in order for complexity to emerge.
What all this means is that a dynamic system, even one made of relatively simple parts with individual interactions that are also simple, can spontaneously generate complexity in a robust way – and exactly the kind of complexity we see in nature.
SOC and the Brain
Manfred Kitzbichler and his coauthors decided to look at brain function to see if it also has the features of self-organized criticality. They thought that SOC would be a good model for brain function because it optimizes information transfer, memory capacity, and sensitivity to external stimuli.
They looked specifically to see if brain complexity exhibits the feature of scale invariant complexity – if patterns in the brain are similar across scales of space and time. They examined a phenomenon known as phase coupling – essentially different parts of the brain firing in synchrony, presumably because they are part of a functional network – and measured how this coupling changed over time.
What they found is that this feature of brain activity does indeed have the signature features of self-organizing criticality.
Of course, this is an extremely complex topic and no one study such as this will be the final word. But they do appear to have provided the first direct evidence of SOC in the overall dynamic function of the brain.
If further investigations support this conclusion then this new way of looking at brain function is likely to deepen our understanding of what is perhaps the most complex system studied by science. This might also help us one day design computers that are more like human brains. Perhaps what this means is that such computers will have to be “grown” not built – they will also have to have complexity emerging out of self-organization, functioning at the edge of chaos.