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 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:

[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: