Analysis of Regularized Poisson GLM Spike-Train Modeling. Issue 1 (1st January 2022)

• Record Type: Journal Article
• Authors: Fan, Yile; Li, Yuanpeng; Xue, Naiyang; Ding, Dan
• Is Part Of: Journal of physics. Volume 2173:Issue 1(2022)
• Publication Date: 2022-01-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2173/1/012019
• Languages: English
• ISSNs: 1742-6588

Abstract

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.