Complex Contagion and Neural Networks

Centola and Macy's (2007) description of complex contagion sounds strikingly similar to a description of the workings of neural networks. When a neuron is activated, it releases a neurotransmitter. This neurotransmitter can excite the neuron that receives it. As a neuron receives more stimulation from surrounding activated neurons, it too becomes activated after reaching a threshold, and then fires, stimulating other neurons that it is connected to, spreading activation throughout the network. To me, this sounds strikingly similar to a description of complex contagion, in which nodes only become active after being in contact with a certain number of other activated nodes.

Neurons can be suppressed just as much as they can be activated, just as Centola and Macy discuss for nodes in social networks. Some neurons, when activated, release a neurotransmitter that suppresses activation in other neurons. Suppression in neurons seems similar to suppression in social networks, though it might work in a different way. In social networks, if one node is not doing something, it suppresses another from doing that same behavior. However, in neural networks it is the opposite. When one neuron is activated and firing, it keeps another neuron from doing the same. The same neurotransmitter might suppress one neuron and excite another. This difference in reaction to a specific neurotransmitter from one neuron to another might parallel different reactions that people have to the same behavior, e.g. deciding to copy or not copy the behavior.

In addition, neurons naturally fire randomly. I'm not sure what this might parallel, but perhaps this would parallel a node thinking about adopting a behavior? Since neurons fire randomly when not activated and nearby neurons are not necessarily randomly firing at the same time, they do not activate other neurons they are connected to. This could also perhaps represent random variation in some way? Or maybe this has no relation at all to complex contagion and the behavior of nodes in a social network.