Neuronal Modelling – The very basics. Part 2: Hodgkin and Huxley.

In part 1 of this post, I discussed the very basics of neuronal modelling. We discussed the fundamental equations that explain how ion channels create current and how current changes the membrane potential. With that knowledge, we created a simple one-compartment model that had capacitance, and a leak ion channel. But we didn’t have any action potentials. In order to model action potentials, we need to insert some mechanism to generate them. There are several ways of doing this, but the most common is the Hodgkin and Huxley (HH) model. I’m going to dive straight in to understanding the HH model, and as usual, I’m going to start from the ground floor.

\mathbf{\overset{n}{Open}} \overset{\beta}{\rightarrow} \mathbf{\overset{1-n}{Closed}}

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Neuronal Modelling – The very basics. Part 1.

I think a lot of people are confused about neuronal modelling. I think a lot of people think it is more complex than it is. I think too many people think you have to be a mathematical or computational wizard to understand it, and I think that leads to a lot of good modelling being discounted and a lot of bad modelling being let through peer-review. I’m here to tell you that biophysical models on neurons don’t have to be hard to implement, or understand. I’m going to start you off on the ground floor, in fact, below the ground floor, this is the basement level. All you need to know is a little coding (I’m going to do both Matlab and Python to start). But I should temper your expectations. When we are done, you’re not going to be ready to publish fully fledged multi-compartment models of neurons, but at least you will understand the fundamental principles of what is happening. And the most fundamental of all is this…

\frac{\mathrm{d}v}{\mathrm{d}t} = \frac{i}{C}

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