The situation is common. Let’s say you want to know if a drug increases sleep duration. You grab 20 mice, you give 10 of them the drug, and 10 of them vehicle. You measure their sleep durations and you do a t-test, and get p < 0.05. You conclude that the drug increases sleep time. Then you realize you did the experiment on all male mice, so you grab a cohort of 20 female mice, give 10 the drug and 10 placebo. You measure how long they sleep and you do a t-test and get p > 0.05. You conclude that the drug does not increase sleep time in female mice. Thus, you conclude that the drug has different effects in males and females.

Seems reasonable right? Well it’s not reasonable. In fact, it’s even less reasonable than I thought.

Some of you may have seen this graph. It was tweeted out, non-ironically by an economist from a prestigious US university, and at first glace it seems ridiculous:

I’d like to talk about this graph, data, regression, causation and data analysis.

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.

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