More to do with neuroscience than you think. Fukushima took direct inspiration from Hubel & Wiesel's nobel prize in the 1960s when developing the neocognitron, which turned into convolutional neural networks. Hopfield networks are a model for associative memory. And, well, then there is the perceptron. There was always a link and mutual inspiration.

Recommended reading: Lindsay, G. W. (2021). Convolutional neural networks as a model of the visual system: Past, present, and future. Journal of cognitive neuroscience, 33(10), 2017-2031. https://direct.mit.edu/jocn/article-abstract/33/10/2017/9740...

As inspiration, yes. However, a neural network neuron and a biological neuron are, to modern understanding, entirely unrelated.

They're not identical but they are related. There's a series of approximations and simplifications you can go through to get from biological neurons to neural nets. Essentially the weights in the neural net end up corresponding to steady-state firing rates of populations of spiking neurons. See for example Chapter 7 of Dayan & Abbott's Theoretical Neuroscience.

Not to be dismissive, but through any “series of approximations and simplifications” you can go from anything to anything else.

Discussing the right levels of abstraction is a huge thing in computational biology. At what level is 'the algorithm' of natural computation implemented?