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

by Mark Humphries

These location, location, location dependencies of a single neuron have deep implications for artificial intelligence (AI). AI-brand neural networks are all constructed from the same kind of pretend neuron, a simple thing that just adds up its inputs from other pretend neurons. And once added up, an AI-brand pretend neuron checks if they sum greater than zero, and if so then sends that sum on to all its targets (or else sends zero). The deepest of deep networks are all constructed from millions of these elementary things. But I’ve just spent five thousand and more words telling you that a single neuron in your cortex does not just add up its inputs. How a neuron deals with its input depends on a plethora of things that all interact: the balance and synchrony of its inputs, where they land and how they bunch, whether they sum up wrong, what the voltage is when the inputs land, and what is on the path between the input and the neuron’s body. Current AI networks have barely tapped the surface of what brains do. Indeed, detailed pretend models of neurons have shown us that a single neuron can itself be a two-layer neural network.30 If each branch of the tree has the ability to do those supralinear sums, each branch acts as single AI-brand pretend unit. So their outputs (layer one of the network) converge at the neuron’s body (layer two of the network). And with this comes the ability for single neurons to compute alone many of the functions of logic.31 So each individual neuro...