A Hopfield network is a specific type of recurrent artificial neural network based on the research of John Hopfield in the 1980s on associative neural network models. Hopfield networks are associated with the concept of simulating human memory through pattern recognition and storage.
In order to understand Hopfield networks better, it is important to know about some of the general processes associated with recurrent neural network builds. In general, neurons get complicated inputs that often track back through the system to provide more sophisticated kinds of direction. Some experts talk about the “traveling salesman problem” as a type of hard problem addressed with Hopfield networks – in this particular case, the system is looking at time between destinations and working out high-level solutions by using the artificial neural structures that in some ways simulate human thought.
Experts also use the language of temperature to describe how Hopfield networks boil down complex data inputs into smart solutions, using terms like “thermal equilibrium” and “simulated annealing,” in which spiking or excitatory data inputs simulate some of the processes used in cooling hot metals. The idea is that data heats up or settles down according to the neural inputs and lateral communications between layers, and that forms the basis for a lot of this balancing of stored patterns and new input that allows Hopfield networks to be valuable in fields like image processing, speech processing and fault-tolerant computing.
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