NDLI: Computation of reduced energy input current stimuli for neuron phase models

Content Provider	IEEE Xplore Digital Library
Author	Anyalebechi, J. Koelling, M.E. Miller, D.A.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Western Michigan Univ., Kalamazoo, MI, USA (Anyalebechi, J.; Miller, D.A.) \|\| Dept. of Math., Western Michigan Univ., Kalamazoo, MI, USA (Koelling, M.E.)
Abstract	A regularly spiking neuron can be studied using a phase model. The effect of an input stimulus current on the phase time derivative is captured by a phase response curve. This paper adapts a technique that was previously applied to conductance-based models to discover optimal input stimulus currents for phase models. First, the neuron phase response θ(t) due to an input stimulus current i(t) is computed using a phase model. The resulting θ(t) is taken to be a reference phase r(t). Second, an optimal input stimulus current $i^{}(t)$ is computed to minimize a weighted sum of the square-integral `energy' of $i^{}(t)$ and the tracking error between the reference phase r(t) and the phase response due to $i^{}(t).$ The balance between the conflicting requirements of energy and tracking error minimization is controlled by a single parameter. The generated optimal current $i^{}t)$ is then compared to the input current i(t) which was used to generate the reference phase r(t). This technique was applied to two neuron phase models; in each case, the current $i^{}(t)$ generates a phase response similar to the reference phase r(t), and the optimal current $i^{}(t)$ has a lower `energy' than the square-integral of i(t). For constant i(t), the optimal current $i^{}(t)$ need not be constant in time. In fact, $i^{}(t)$ is large (possibly even larger than i(t)) for regions where the phase response curve indicates a stronger sensitivity to the input stimulus current, and smaller in regions of reduced sensitivity.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	4847
Ending Page	4851
File Size	891873
Page Count	5
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944709
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Computational modeling Adaptation models Optimal control Biological system modeling Cost function Mathematical model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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