I am about to simulate a neuron activity with the "Leaky Integrate and Fire" neuron model. But for that I need the membrane resistance. I was really looking a lot online, but I just cant find a value. Does anybody know the measured resistance value, or the value usually used in this model?

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    $\begingroup$ Welcome to cogsci.SE! More information, about both your question and what you've done to try to answer it, would help people answer you--simulate using what tools and for what goal? where did you look? $\endgroup$
    – Krysta
    Jul 6, 2016 at 17:42
  • $\begingroup$ I am doing it for a university project, and I am simulating it with my own C++ program via the Runge Kutta 4 Algorithm. $\endgroup$ Jul 6, 2016 at 17:44
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    $\begingroup$ Membrane resistance depends on the state of the neuron (in rest-high; excited-low; inhibited-very high?) - it's a dynamic thing, i.e., there does not exist something as the resistance $\endgroup$
    – AliceD
    Jul 6, 2016 at 22:00
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    $\begingroup$ But in this Leaky Integration Model you assume the resistance as a constant (it is a very strongly simplified Model). So what is commonly used there? Probably something like the average resistance when the Neuron is not firing. Does somebody have a value for that? Or at least a order of magnitude? $\endgroup$ Jul 7, 2016 at 5:55
  • $\begingroup$ It depends on the neuron you want to model. It varies enormously depending on the neuron. $\endgroup$ Jul 9, 2016 at 19:44

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If there is literature that you are building off of or comparing your approach to, I would look in their papers to see what values they used and use the same. This is the standard approach, as it yields easily comparable experimental results.

If you are just looking for some ballpark numbers for less formal research, these guys used 1 mΩ, these guys did the same, and these guys say they use 10 MΩ (I think they might mean mΩ). If you want, you can also tune the model by altering this parameter until it yields expected behaviors you are looking for under conditions where you know how the model performs. Different kinds of neurons have different resistance. It's hard to be more specific without knowing more details about what you want to do.


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