1
$\begingroup$

I am following a class where we have just learned about so-called neural fields (the prof called them recurrent neural networks and said they are used to model large populations of real neurons). Now we received homework to get to know the method using a simulation, but we got stuck at the last question, about the usefulness of a certain behaviour.

These are our guidelines:

Reset the field; make sure it can memorise one bump. Give two equal inputs < 15 degrees apart. Reduce until the activity level forms one concave signal (so not like in the picture below).

This all works. As soon as the difference goes below 10, the bump changes from the signal below to a concave one, just as the input signal does. However, then we get the question:

In what situation would such behaviour be useful?

And we're sort of stuck there. Is there someone who could point us into the right direction?

.

Picture of neural field. The green line is the input signal, the red line is the output, the blue line is the neural field activity. The x-axis is the position in degrees.

Picture of neural field. The green line is the input signal, the red line is the output, the blue line is the neural field activity. The x-axis is the position in degrees

$\endgroup$
  • $\begingroup$ What is a neural field? Is it related to dynamic field theory? What is the x axis in the plot you've shown? What type of description of behaviour are they looking for? $\endgroup$ – Seanny123 Jan 7 '18 at 0:48
  • 1
    $\begingroup$ I suspect there's not enough detail in your question for anyone to answer it here. You're clearly talking about some experiment (simulation?) but we have no idea what that is. $\endgroup$ – SX welcomes ageist gossip Jan 7 '18 at 3:17
  • $\begingroup$ Sorry, yes. It's a simulation. The x-axis is the position in degrees. A neural field is basically a recurrent neural network, to model 'large populations of real neurons' (prof's words). The way it was explained it seemed like something that was used a lot, same as like neural networks, but then more specific. Sorry if I assumed that wrong! (I added it into the main question now) $\endgroup$ – Sharonneke95 Jan 7 '18 at 13:04
  • $\begingroup$ Also, I looked up dynamic field theory and it seems to be exactly that :) $\endgroup$ – Sharonneke95 Jan 7 '18 at 13:10
  • $\begingroup$ @Sharonneke95 it's also hard to answer this question without knowing what behaviour you saw in the previous simulations. Would you mind also adding this information to your question? $\endgroup$ – Seanny123 Jan 7 '18 at 13:25

Your Answer

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

Browse other questions tagged or ask your own question.