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Eichner_Borst_2011.pdf (Any fulltext), 351KB
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Eichner_Borst_2011.pdf
Description:
Computational neuroscientists frequently encounter the challenge of parameter fitting – exploring a usually high
dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is
using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer
several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error
function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a
mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and
time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the
simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The
model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short
simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter
sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good
intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes.
In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore
complex models such as Hodgkin-Huxley or dynamical systems.
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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
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