10
$\begingroup$

I've found plenty of resources on Hopfield networks that use either discrete variables for both activation level and time or continuous variables for both activation level and time. Is it possible to construct a Hopfield neural network that uses a continuous variable for activation level and a discrete variable for time? If it is possible, can anyone offer resources and/or tips on how to construct one in software?

$\endgroup$
5
$\begingroup$

After doing some additional research, I think the answer is yes. It just means using a fixed timestep for the continuous-time activation equation (as described here). Since this is a differential equation, implementing it in software requires implementing a numerical integration method. I recommend the Exponential Euler Method as a starting point, because it's relatively simple and it's designed for differential equations of the sort that's used for the continuous Hopfield network.

| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.