Two Universes, Same Structure
This image is not of a neuron.
This image is of the other universe; the one outside our heads.
It depicts the “evolution of the matter distribution in a cubic region of the Universe over 2 billion light-years”, as computed by the Millennium Simulation. (Click the image above for a better view.)
The next image, of a neuron, is included for comparison.
It is tempting to wax philosophical on this structure equivalence. How is it that both the external and internal universes can have such similar structure, and at such vastly different physical scales?
If we choose to go philosophical, we may as well ponder something even more fundamental: Why is it that all complex systems seem to have a similar underlying network-like structure?
For illustration of this point, just take a glance at the front page of VisualComplexity.com (partially reproduced below).
These neural-network-like visual images represent complex systems and relations for domains as diverse as academic citations, the blogosphere, scientific knowledge, genealogy, iTunes music collections, and Italian wine production.
Does this imply some deep equivalence exists between all complex systems? Is it the nature of complex systems to be network-like?
Alternatively, this is perhaps simply how we, as neural networks, are able to conceptualize the external universe. Could it be that the external universe is vastly different in form from our internal universe, but we simply perceive that which happens to be compatible with our neural network knowledge structure?
It seems there are some situations where we have trouble representing reality for this reason. However, evolutionary pressures for survival likely drove the human brain to represent the world as accurately as possible. (Otherwise, e.g. our ancestors may have believed lions disappeared when hiding behind bushes; an obviously maladaptive representation of reality.) This suggests that even though our brains don’t represent the world with complete accuracy, it is nonetheless quite accurate in most cases.
Ultimately I think the equivalence between complex systems is due to the underlying nature of such systems. They must all involve massive integrated differentiation. In other words, there must be many different things (nodes), with many different relations among them (links) for a system to be complex. Thus integrated differentiation, the very basis of complexity, is inherently network-like (i.e., has the equivalent of nodes and links).
It is compelling to consider if neural systems, with their numerous nodes (neurons) and links (synapses) providing integrated differentiation, might have evolved complexity in order to represent other complex systems. In other words, neural systems may have evolved in order to mirror the complexity presented by the external universe, which helped each organism adapt and survive in its environment.
Thus the similarity between the internal and external universes may not be due to coincidence, but design.