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There are at least two problems with this model. For one, some neurons, when activated, don't just send a signal to specific other neurons, but instead release a chemical in an area, that affects the activation chances of other nearby neurons. I believe there are also other modes of activation, and other consequences of neuron activation, that make the brain far more complex. It should be remembered that the brain can also activate other glands in the body, which in turn change how the brain works - e.g. when releasing adrenaline, testosterone, oxytocin etc. For another, as far as we know right now, each neuron itself is deciding whether to fire or not based on much more sophisticated logic than "sum(input*weight) > threshold". In fact, it seems that computation happens quite a bit in individual neurons, not only at the NN level. At the very least, the neuron activation function is not fixed, like in an ANN after training, it changes constantly for various reasons. |
I will say that my mental model does hinge on the idea that the action of a single neuron at a single point in time in a single context can actually be equated to "sum(input*weight) > threshold". Doing the actual computation to figure out a principled measure of weight (and input, context, and maybe even time for that matter) is way outside our ability, but it seems like something that could be approximated in a simple experimental model!