The Spike: An Epic Journey Through the Brain in 2.1 Seconds

by Mark Humphries

The math is fierce, but the idea is simple. We’ve got our pretend neurons, most excitatory and the rest inhibitory, and we’ve randomly wired them together. Then all we need to do is guarantee that the input to each neuron is more than the neuron needs to make a spike. For this then creates a web of negative feedback loops, of neurons trying to make a spike, but being held back. It works like this. Say some of the excitatory neurons send a lot of spikes. This will drive inhibitory neurons to make spikes—that are fed back to those excitatory neurons and turn down their spikes. But they can’t turn all the excitatory neurons down too much, because then the inhibitory neurons themselves will lose their input and stop firing. But they have to be firing, because the excitatory neurons are firing. This paradox implies there is a self-consistent state for the whole network, where the inhibitory and excitatory neurons are sending just the right amount of spikes each, so the push-pull of the excitation and inhibition in the network is balanced. And we all know by now what balanced excitatory and inhibitory inputs means: irregular spikes. Better still, this theory showed balanced networks are robust old things: you don’t need fine-tuning of the exact strengths of inhibitory and excitatory inputs; nor do you need to fiddle with the details of how the neurons make spikes. Just make the total input to each neuron a lot, add a dash of feedback, and voilà: balance.