Back up a bit please! Analog computing is a thing. And it isn't even new - not by a long shot.

There are good reasons why practically all computing today is the digital kind. But electronic 'equivalents' of neural nets is one area where analog might make sense. Adding inputs can be as simple as a bunch of resistors + a transistor. Even on modern silicon nodes, that might be a more efficient setup than digital inputs, N-bit adders/multipliers etc. Not saying that's the case, and AI hardware should be based on analog circuitry. But it could be, and perhaps found to be practical.

Artificial neurons are significantly more complex that single transistors, and even a minimal hardwired circuit to implement just one neuron requires quite a number of transistors.

It's the same thing as stimulus, response.

Unchanging in response to circumstances is static.

Changing in the absence of circumstances is randomness.

The conditional is all that remains. Changing in response to circumstances

(Arguably, unchanging in the absence of circumstances completes the truth table, but it's a whole lot of nothing)

There exist several equivalent sets of primitive operations. While the sets containing only NAND or only NOR, or both AND and NOT or both OR and NOT are more notorious, these logical operations are more abstract and they do not indicate precisely a hardware implementation, i.e. there are many distinct hardware methods to make such logical gates.

Other sets of primitive operations map directly to hardware devices, e.g. the sets of primitive operations composed of maximum and complement or of minimum and complement map directly to a hardware implementation using rectifier diodes and inverting amplifiers (which can be made with either semiconductor devices or with vacuum tubes, or also with pneumatic or hydraulic devices).

Other sets of primitive operations are obtained by replacing the maximum or minimum circuits with series or parallel connections of switches, like in the CMOS logic that is nowadays dominant.

The alternative IF expression corresponds in hardware to a 2-way multiplexer, which, together with the 2 constant functions "0" and "1" (a.k.a. "false" and "true" or "low voltage" and "high voltage"), is sufficient for a complete set of computational primitives.

Besides those mentioned above, the main remaining variant for a complete set of computational primitives consists of an analog (possibly weighted) adder and an analog comparator, which had been used in the so-called RTL circuits (resistor-transistor logic) and which also corresponds to perceptrons. RTL had been used in some early integrated circuits, before being replaced by DTL and TTL circuits (which are based on minimum and complement functions).

In hardware, e.g. in RTL circuits, a combined analog adder+comparator can be made with a single high-gain amplifying device, together with a set of weighting resistors and a bias resistor. RTL circuits can implement complex logic with fewer devices (e.g. they can implement a neural network in the analog domain), but they were replaced during the sixties of the past century with DTL, then TTL, because those were faster (in RTL, the resistors limit the charging currents for input capacitors and parasitic capacitors, which slows down the logical transitions) and the fact that they needed more devices was not important, due to the quick increase in circuit density.

A transistor (driven to saturation) is a much better model.