<p>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.</p>

<p>These are our guidelines:</p>

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

<p>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:</p>

<p><em>In what situation would such behaviour be useful?</em>   </p>

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

<p>.</p>

<p>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.</p>

<p><a href="https://i.sstatic.net/1sAxk.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/1sAxk.png" alt="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"></a></p>

CC BY-SA 3.0
2018-01-07T00:48:39.053Z
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?
Seanny123
CC BY-SA 3.0
2018-01-07T03:17:37.847Z
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.
got trolled too much this week
CC BY-SA 3.0
2018-01-07T13:04:07.257Z
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)
Sharonneke95
CC BY-SA 3.0
2018-01-07T13:10:46.160Z
Also, I looked up dynamic field theory and it seems to be exactly that :)
Sharonneke95
CC BY-SA 3.0
2018-01-07T13:25:41.493Z
@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?
Seanny123
CC BY-SA 3.0
2018-01-07T14:34:33.280Z
I'm not sure what kind of behaviour you're looking for, so I hope this will do. There seems to be a linear relationship between the resting potential and the amplitude of a single input that is needed to get an output with height 10. We found the value of centre-excitation (type of interaction kernel)  for which the memorisation kicks in. A second signal at remote distance of certain amplitude can make a memorised signal disappear, but this doesn't happen if the global inhibition is set to 0; then it can memorise two bumps. And for two near peaks (<15 degrees) the output bumps merge into one.
Sharonneke95
<neuroscience><model><homework>
Neural field application question
Sharonneke95
# Neural field application question

(Asked by: username_0 on 2018-01-06)

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](https://i.sstatic.net/1sAxk.png)](https://i.sstatic.net/1sAxk.png)

**Tags:** neuroscience, model, homework

> **Comments:**
> **username_1**: 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?  
> **username_2**: 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.  
> **username_0**: 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)  
> **username_0**: Also, I looked up dynamic field theory and it seems to be exactly that :)  
> **username_1**: @username_0 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?  
> **username_0**: I'm not sure what kind of behaviour you're looking for, so I hope this will do. There seems to be a linear relationship between the resting potential and the amplitude of a single input that is needed to get an output with height 10. We found the value of centre-excitation (type of interaction kernel)  for which the memorisation kicks in. A second signal at remote distance of certain amplitude can make a memorised signal disappear, but this doesn't happen if the global inhibition is set to 0; then it can memorise two bumps. And for two near peaks (<15 degrees) the output bumps merge into one.  

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## 0 answers

