We always if we were smarter than chimps (or at least baboons).
Here is clinical proof as to why the human brain has a better handle on complexity.
This article profiles a whole new dimension of evolutionary complexity has now emerged from a cross-species study led by Dr. Seth Grant at the Sanger Institute in England.
Evolution’s recipe for making a brain more complex has long seemed simple enough. Just increase the number of nerve cells, or neurons, and the interconnections between them. A human brain, for instance, is three times the volume of a chimpanzee’s.
The computing capabilities of the human brain may lie not so much in its neuronal network as in the complex calculations that its synapses perform, Dr. Grant said. Vertebrate synapses have about 1,000 different proteins, assembled into 13 molecular machines, one of which is built from 183 different proteins. These synapses are not standard throughout the brain, Dr. Grant’s group has found; each region uses different combinations of the 1,000 proteins to fashion its own custom-made synapses. Each synapse can presumably make sophisticated calculations based on messages reaching it from other neurons. The human brain has about 100 billion neurons, interconnected at 100 trillion synapses.
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Labels: brain research, complexity, neuroscience
The power and flexibility of a network--whether a simple group of casual neighbors or a complex
next generation communication network--depends not just on the number of connections, but on the quality of the nodes, and more important, the type of nodes. Below is a fantastic intro to the concept of
graphs and networks. It helps in understanding the a
social graph and how it differs from a social network.
In Mathematics, a Graph is an abstraction for modeling relationships between things. It is no different from a Network, which is a more common term for describing the same thing. Graphs consists of nodes and edges, or things and the ways that things relate to each other. As it turns out, Graphs are very powerful modeling tools for modeling natural and man-made systems. Diverse things like the Web, power grids, economies and even cells can be represented and analyzed as networks.
Note: Images above are from the Visual Complexity Gallery What is also remarkable is that a lot can be said about a graph by looking at its structure; and the evolution of the structure. For example, epidemiologists use graph structures to predict the spread of an epidemic. The very same model can be used to understand how wild fire spreads, as well as how to engineer a viral marketing campaign. The better we understand the structure of a system's graph, the more we can control it, predict it and analyze it. |
Labels: community, complexity, information graphics, social media
