Analysis of Regularized Poisson GLM Spike-Train Modeling

This paper introduces a method for modeling and analyzing neural impulse sequences. In this paper, we define the response value of a scale-independent neuron and construct the correlation graph of the neuron under the response value. The minimum cut algorithm is applied continuously to obtain the ma...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 2173; no. 1; pp. 12019 - 12026
Main Authors Fan, Yile, Li, Yuanpeng, Xue, Naiyang, Ding, Dan
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.01.2022
Subjects
Algorithms
Modelling
Neurons
Physics
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ISSN1742-6588
1742-6596
1742-6596
DOI10.1088/1742-6596/2173/1/012019

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Summary:This paper introduces a method for modeling and analyzing neural impulse sequences. In this paper, we define the response value of a scale-independent neuron and construct the correlation graph of the neuron under the response value. The minimum cut algorithm is applied continuously to obtain the maximum group of neurons. According to the characteristics of the firing of neurons, a Poisson-process based model is proposed to mathematically model the neural coding, and the gradient descent method is used to optimize it. Through the modeling analysis method, information such as maximum neuron group and Inter-spike-Interval (ISI) can be effectively analyzed according to neuron impulse sequence.
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ISSN:1742-6588
1742-6596
1742-6596
DOI:10.1088/1742-6596/2173/1/012019