The document discusses how the neural mechanism of Spike Time Dependent Plasticity (STDP) could provide a model for how social media platforms like Twitter function. STDP strengthens connections between neurons that fire in rapid succession, with neurons firing first gaining influence. Similarly, on Twitter influential users who tweet news first could automatically gain increased influence over their followers. Over time, as followers converge on the same interesting content, the network could develop thoughts and ideas that propagate, representing a form of global consciousness emerging from the network.

1. Twitter and the Global Brain
© 2009 Dean Pomerleau
The prevailing model for many years of how synapses between neurons in the
brain are altered during learning has been Hebbian learning, which can be
summarized as "neurons that fire together, wire together". In other words, in
two neurons fire at the same time, the connection(s) between them will
strengthened.
But recent evidence in neuroscience shows the truth is actually an interested
twist on this idea - a twist that could have important implications as a model of
how global consciousness could emerge from real-time social media like Twitter.
In reality, synapses are modified according to a rule called Spike Time Dependent
Plasticity (STDP). In a nutshell, STDP says that if two neurons fire (= spike) in
rapid succession, the connection from the one that fires first to the one that fires
second will be strengthened.
In other words, if neuron A reliably fires shortly before neuron B, the connection
from A to B will get stronger, so that next time when neuron A fires, neuron B will
be more likely to fire too. And the opposite holds as well. In this example, since
the firing of neuron B lags behind neuron A, the strength of the connection in that
direction (from B to A), will be weakened. You could think of it as the neural
equivalent of the old saying 'the early bird catches the worm' - a neuron that fires
first gains increasing influence on its downstream neighbors.
STDP is a simple idea, but it has been shown to be a surprisingly powerful way
that the brain uses for rapid pattern recognition and classification [1][2]. It turns
out that using STDP, neurons naturally learn to specialize in detecting certain
patterns in their inputs, even in the presence of lots of noise.
So what in the world does this have to do with social networks? There is an
intriguing analogy between networks of neurons operating by the STDP rule and
the emerging structure and functioning of real-time social networks like Twitter.
Imagine a twitter user as a neuron. He/she makes the equivalent of a synapse
with each of his/her followers. When a twitter user sends out a tweet, it is the
equivalent of a neuron firing. Followers who receive the tweet decide whether to
propagate the activity by retweeting the message, in a sense by deciding whether
they too should fire in response to the tweet.
It isn't happening exactly this way yet, but STDP would enter the picture in the
following way. Suppose Bill is a follower of an influential person on Twitter like
Guy Kawasaki and Bill decides one of Guy's tweets is interesting enough to
retweet. This is a clear indication that Bill finds Guy's tweets interesting and
valuable. Based on this 'vote of confidence' for Guy's tweets, a yet-to-be-
implemented mechanism could automatically increase the weight that Guy's

2. 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.
[1] 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.

3. [2] 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:
http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.2207-09.2009