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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          | Published in | Journal of physics. Conference series Vol. 2173; no. 1; pp. 12019 - 12026 | 
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| Main Authors | , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Bristol
          IOP Publishing
    
        01.01.2022
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| Subjects | |
| Online Access | Get full text | 
| ISSN | 1742-6588 1742-6596 1742-6596  | 
| DOI | 10.1088/1742-6596/2173/1/012019 | 
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| Abstract | 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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| AbstractList | 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. | 
    
| Author | Fan, Yile Li, Yuanpeng Ding, Dan Xue, Naiyang  | 
    
| Author_xml | – sequence: 1 givenname: Yile surname: Fan fullname: Fan, Yile organization: Graduate School, Aerospace Engineering University , China – sequence: 2 givenname: Yuanpeng surname: Li fullname: Li, Yuanpeng organization: Graduate School, Aerospace Engineering University , China – sequence: 3 givenname: Naiyang surname: Xue fullname: Xue, Naiyang organization: Graduate School, Aerospace Engineering University , China – sequence: 4 givenname: Dan surname: Ding fullname: Ding, Dan organization: Department of Electronics and Optical Engineering, Aerospace Engineering University , China  | 
    
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| Cites_doi | 10.1109/TAMD.2010.2053365 10.1162/neco_a_01021 10.1126/science.1100265 10.1038/nature07140 10.1109/TBME.2007.891948 10.1152/jn.00697.2004 10.1109/TAMD.2012.2211101 10.1088/0954-898X_15_4_002 10.1109/TAMD.2011.2138705 10.1088/1741-2560/9/2/026004 10.1016/j.jneumeth.2010.03.024 10.1146/annurev-statistics-041715-033733 10.1038/nature06996 10.1038/nn.3800 10.1113/jphysiol.1952.sp004764  | 
    
| ContentType | Journal Article | 
    
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| SubjectTerms | Algorithms Modelling Neurons Physics  | 
    
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| Title | Analysis of Regularized Poisson GLM Spike-Train Modeling | 
    
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