tweets are given for Bill, making Guy's tweets more likely to show up high on
Bill's Twitter 'dashboard'.
But what if Guy wasn't the first to tweet the news that Bill found so interesting?
The same automated mechanism could suggest to Bill that instead of (or in
addition to) following Guy, Bill might like to follow another sharp Twitter
personality (perhaps Nova Spivack) who beat Guy to the punch by being the first
to post the content Bill found interesting.
In this way, users could be automatically steered towards following folks who are
the first to post content that will interest them - towards those who are
considered the 'thought leaders' you might say. And content creators who work
hard to be the first to find and tweet interesting content will be rewarded
automatically with a growing list of followers, and eventually with monetary
reward if/when Scobleizer 'attention economy', or some other way to monetize
eyeballs, emerges on Twitter.
As an added benefit, the tweets Bill receives could be automatically sorted based
on how interesting they are likely to be for him. As a simple example, imagine
that several of the people Bill follows and has demonstrated an affinity for in the
past (by retweeting their posts) tweet about the same story. This convergence of
matching input from sources that Bill weights highly suggests that Bill will find
this to be very interesting content, so it should be automatically bubbled to the
top of Bill's prioritized list of tweets to read.
In this model, content generators on Twitter will compete to be the first to create
good content or break important news, just as neurons in the brain compete via
the STDP update rule to be the first to detect patterns in their input and shout out
about it by spiking. In both systems, 'the early bird catches the worm'.
Eventually, tools may even emerge that automatically retweet messages based
on a user's previously expressed preferences, to alert his followers of content he,
and therefore they, will likely consider interesting. At that point, the virtual
neurons formed by the combination of people and their automated agents on
Twitter will be influencing each other and firing automatically based on the inputs
they receive. On a macro scale, this will represent the equivalent of thoughts
emerging in the Global Brain, in the form of rapid, coordinated firing of millions of
these virtual neurons. These thoughts will propagate and potentially trigger other
thoughts in the network. This massive semi-autonomous reverberation in the
twittersphere could signal the emergence of a true global consciousness.
 Masquelier T, Guyonneau R, Thorpe SJ. Spike timing dependent plasticity
finds the start of repeating patterns in continuous spike trains. PloS one.
2008;3(1):e1377. Available at: http://www.ncbi.nlm.nih.gov/pubmed/18167538.
 1. Masquelier T, Hugues E, Deco G, Thorpe SJ. Oscillations, Phase-of-Firing
Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme.
Journal of Neuroscience. 2009;29(43):13484-13493. Available at: