A Hopfield network is a totally connected neural network often used as autoencoder. Like Boltzmann machine, the Hopfield network is related to {[spin glass models}} in physics. Hopfield networks differ from Boltzmann machines principally in the update model for node weights, notably, while Hopfield networks adopt a continuous learning mechanism with smooth thresholds, similar to that in backpropagation; Boltzmann machine use stochastic firing that effectively creates a Markov process, and are more similar to spiking neural networks.
Used in Chap. 12: page 196
Links:
neuronaldynamics.epfl.ch:
Neuronal Dynamics: Hopfield Model
Wikipedia:
Hopfield network
arXiv:
Hopfield Networks is All You Need