From NeuroLogica:
http://www.theness.com/neurologicablog/?p=500
QuoteThe 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.
Dr. Steven Novella, Discordian Saint :fnord:
All hail emergence. :mittens:
Hail!
That was entirely awesome.
A dynamic system balanced between order and chaos... and people say the PD is full of shit ;-)
None of this is particularly new, but kudos to this dude for using the term "chaos" -- the only other guy I've seen who describes it that way without pointing out the differences in the definition of chaos between mathematics, complexity science, and the colloquial is Tim Leary, and he was talking about something on the software end, not in the firmware self-modification algorithm :P