Problems Using FFT to Compute Impedance in a Model Neuron
I don't know how much background you have with FFT, so I apologize if I list a few things you already know.

In general, I think people have a tendency to throw an FFT at a problem and hope it works, but you may need to consider what the transform is really all about. First, when you take an FFT, it's implicit in the calculation that your signal is periodic. So, even though you're feeding it a finite number of samples with frequency content only between f0 and ff, the calculation makes the assumption that before time zero and after your samples, the waveform repeats forever. If your waveform's beginning and end don't match perfectly, there's going to be a lot of frequencies you wouldn't expect in your FFT. Basically, it's like there's a discontinuity. So, often, you'll window the signal to force it to zero at both ends so each periodic clone of your waveform meets the next smoothly. This obviously also has consequences for the frequency content of the FFT, so you need to do this carefully also.

Second, if your waveforms have any DC content (a constant offset, frequency = 0 Hz), it usually makes sense to remove it before your FFT unless DC is of interest to you. Often, low frequencies are huge and swamp the stuff you care about. Doing some high-pass filtering with a very low cutoff will help you see the interesting content of your plot more easily.

Third, when you view your plot on a linear frequency axis, you'll see that there are Fourier coefficients for both positive and negative frequencies, and the plot is symmetrical about 0 Hz. For qualitative assessments, you probably only need to look at the positive half.

Fourth, consider that you're taking the ratio of output over input, and anywhere your input FFT is near zero, that's going to blow up, and may make it hard to see what's going on.

So, I'd say, do some preprocessing of your signal to remove things you don't care about, and to prevent edge effects. Then post another plot and let's see how it looks. Since you're simulating the input and output here, there may not be a need, but in practice, I'll often use Welch's method to compute power spectral densities, and then compute an H1 or H2 estimation of the frequency response. Here, we can hopefully get a result with a simple analysis.