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  Hands-On Parameter Search for Neural Simulations by a MIDI-Controller

Eichner, H., & Borst, A. (2011). Hands-On Parameter Search for Neural Simulations by a MIDI-Controller. PLoS One, 6(10): e27013, pp. [1]-[4]. doi:10.1371/ journal.pone.0027013.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0012-2DFF-D Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0012-2E02-C
Genre: Journal Article

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Eichner_Borst_2011.pdf (Any fulltext), 351KB
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Eichner_Borst_2011.pdf
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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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 Creators:
Eichner, Hubert1, Author              
Borst, Alexander1, Author              
Affiliations:
1Department: Systems and Computational Neurobiology / Borst, MPI of Neurobiology, Max Planck Society, ou_1113548              

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Language(s): eng - English
 Dates: 2011-10-31
 Publication Status: Published online
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 Identifiers: DOI: 10.1371/ journal.pone.0027013
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Sciene
Pages: 4 Volume / Issue: 6 (10) Sequence Number: e27013 Start / End Page: [1] - [4] Identifier: ISSN: 1932-6203
CoNE: /journals/resource/1000000000277850