A commonly shared image online (not attached here) compares the number of parameters in a large language model to the number of neurons in the human brain, i.e. 86 billion. The intent is probably to illustrate scale, but the analogy is broken at the root, let me tell you why
A parameter, which is a connection weight is a static number, frozen the moment inference from a model begins while a neuron is a computational device in itself, connected to thousands of other neurons. Each neuron integrates all its inputs, computes across time, has memory and contains dendrites that act as sub-processors. So the map isn’t just off by a scaling factor, it’s broken at the root as I said earlier, because it compares a living i.e. changing thing to a fixed one. A more careful version of the comparison would map parameters to synapses instead, and even that fails, because a biological synapse is plastic and dynamic. It strengthens, weakens and rewires as the brain computes. A parameter? Just a number at inference
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