The McCulloch Pitts neuron was described in 1943. It consists of a bunch of inputs (dendrites) some excitatory, some inhibitory, which are just summed (integrated) the results determining the output (whether the axon of the neuron fired or didn’t). Hooking them together could instantiate a variety of boolean functions and ultimately a Turing machine.
The McCulloch Pitts neuron really isn’t that far from the ‘neurons’ in neural nets which underlie the spectacular achievements of artificial intelligence (ChatGTP etc. etc.) The neuron of the neural net is nothing more than a set of inputs, a set of weights, and an activation function. The neuron translates these inputs into a single output, which can then be picked up as input for another layer of neurons later on.
The major difference between the computation a linked bunch of neurons in the two models (McCulloch Pitts and neural net) is that given the same set of inputs in McCulloch Pitts you always get the same output, while in neural nets you don’t. The difference is that the set of weights on the inputs to each neuron in the net which can be and are adjusted which depends on how close the output of the net is to the target (which in the case of ChatGTP is how accurately it predicts the next word in a sample of text).
There is a huge debate going on as to whether ChatGTP and similar neural nets understand what they are doing and whether they are/will become conscious.
So does ChatGTP explain how our brains do what they do? Not at all. Our neurons are doing far more than integrating input and firing. This was brought home in a paper focused on something entirely different, the gamma oscillations of brain electrical activity (Neuron vol. 111 pp. 936 – 953 ’23). People have been studying brain rhythms since Hans Berger discovered alpha rhythm just shy of a century ago. The electroencephalogram (EEG) measures the various rhythms as they occur over the brain. Back in the day when I was starting out in neurology (1967), it was one of the few diagnostic tools we had. It wasn’t very good, and a cynical attending described it as useless but not worthless (because you could charge for it).
The gray matter of the surface of our brains (cerebral cortex) is gray because it is packed with the cell bodies of neurons — some 100,000 under each square millimeter of cortex. Somehow they are wired together so that they can produce coherent rhythmic electrical activity as they fire.
The best place to study how a bunch neurons produce rhythms is the hippocampus, an area crucial in forming memories and one of the earliest places the senile plaques of Alzheimer’s disease show up.
Unlike the jumble of neurons in the cortex, the large neurons of the hippocampus are all lined up and oriented the same way like trees in a forest. All the cell bodies lie in roughly the same layer, with the major dendrite (apical dendrite) going up like the trunk of a tree, and the ones near the cell body spreading out like the roots of a tree.
Technology has marched on, and it is now possible to fashion electrodes, which can measure neuronal electrical activity along the trunk, and watch it in real time.
Figure 2b p. 941 shows that different parts of the trunk of the hippocampal neurons show rhythmic activity at different frequencies at any given time. Not only that, but as time passes each area of the trunk (apical dendrite) changes the frequency of its rhythmic activity. This is light years away from the integrate and fire model of McCulloch Pitts, or the adjustment of weights on the inputs to the neurons of the neuronal net.
It shows that each of these neurons is a complex processor of information (a computer if you will). Even though artificial intelligence has made great strides, it really isn’t telling us how the brain does what it does.
Finally if you want to see what genius looks like, check out the life of Walter Pitts — https://en.wikipedia.org/wiki/Walter_Pitts — corresponding with Bertrand Russell about Principia Mathematica at age 12, studying with Carnap at the University of Chicago at 15, all while he was homeless.