Neuron Modeling: Estimating the Parameters of a Neuron Model From Neural Spiking Data

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2018

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Tubitak Scientific & Technological Research Council Turkey

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Electrical-Electronics Engineering
The Department of Electrical and Electronics Engineering covers communications, signal processing, high voltage, electrical machines, power distribution systems, radar and electronic warfare, RF, electromagnetic and photonics topics. Most of the theoretical courses in our department are supported by qualified laboratory facilities. Our department has been accredited by MÜDEK since 2013. Within the scope of joint training (COOP), in-company training opportunities are offered to our students. 9 different companies train our students for one semester within the scope of joint education and provide them with work experience. The number of students participating in joint education (COOP) is increasing every year. Our students successfully completed the joint education program that started in the 2019-2020 academic year and started work after graduation. Our department, which provides pre-graduation opportunities to its students with Erasmus, joint education (COOP) and undergraduate research projects, has made an agreement with Upper Austria University of Applied Sciences (Austria) starting from this year and offers its students undergraduate (Atılım University) and master's (Upper Austria) degrees with 3+2 education program. Our department, which has the only European Remote Radio Laboratory in Foundation Universities, has a pioneering position in research (publication, project, patent).

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Abstract

We present a modeling study aiming at the estimation of the parameters of a single neuron model from neural spiking data. The model receives a stimulus as input and provides the firing rate of the neuron as output. The neural spiking data will be obtained from point process simulation. The resultant data will be used in parameter estimation based on the inhomogeneous Poisson maximum likelihood method. The model will be stimulated by various forms of stimuli, which are modeled by a Fourier series (FS), exponential functions, and radial basis functions (RBFs). Tabulated results presenting cases with different sample sizes (# of repeated trials), stimulus component sizes (FS and RBF), amplitudes, and frequency ranges (FS) will be presented to validate the approach and provide a means of comparison. The results showed that regardless of the stimulus type, the most effective parameter on the estimation performance appears to be the sample size. In addition, the lowest variance of the estimates is obtained when a Fourier series stimulus is applied in the estimation.

Description

Doruk, Ozgur/0000-0002-9217-0845

Keywords

Neuron model, neural spiking, firing rate, inhomogeneous Poisson point processes, maximum likelihood estimation

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0

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Q4

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Q3

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Volume

26

Issue

5

Start Page

2301

End Page

2314

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