The name is absent

46

to be performed using oscillatory currents, such as

ʃ(t) = 7_o(0.5 + 0.5sin(2πω(t - £)/1000)),                (2.43)

where ∕₀ is the peak amplitude, t is the time of stimulus onset (in ms), and ω is the
frequency (in Hz).

Our reduced model is useful here because we can use the ZAP current to get a
band in which the resonant frequency ω_r may be and then run a brute-force search to
pinpoint this value, all in much less time than the full system requires. For example,
using the ZAP current in (2.42) with parameters a = 50, b = IO^-6, and c = 3, we
find that the forked neuron with uniform channels has a definite peak near 65 Hz (see
Figure 2.11 A). Refining this estimate via a brute-force search using (2.43) in a small
interval around this peak reveals that ω_r increases linearly as distance from the soma
increases.

2.6 IRKA Model Reduction Results

With the BT method as a benchmark, we test the model reduction technique
based on using IRKA. First we demonstrate typical convergence of IRKA for our
problems, and then we show that cells of much larger dimension can be tackled.

Consider neuron AR-1-20-04-A from Figure 2.6B. We compute the maximum ab-
solute error in the soma voltage between the quasi-active and reduced systems using

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