Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-02-23 DOI:10.3389/fncom.2024.1363514
Oleg Maslennikov, Matjaž Perc, Vladimir Nekorkin
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Abstract

In this study, we focus on training recurrent spiking neural networks to generate spatiotemporal patterns in the form of closed two-dimensional trajectories. Spike trains in the trained networks are examined in terms of their dissimilarity using the Victor–Purpura distance. We apply algebraic topology methods to the matrices obtained by rank-ordering the entries of the distance matrices, specifically calculating the persistence barcodes and Betti curves. By comparing the features of different types of output patterns, we uncover the complex relations between low-dimensional target signals and the underlying multidimensional spike trains.
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训练生成时空模式的递归尖峰神经网络中尖峰列车的拓扑特征
在这项研究中,我们重点训练递归尖峰神经网络,以生成封闭二维轨迹形式的时空模式。我们使用维克多-普普拉距离(Victor-Purpura distance)来检验训练网络中的尖峰列车的不相似性。我们将代数拓扑方法应用于通过对距离矩阵的条目进行排序而得到的矩阵,特别是计算持久性条形码和贝蒂曲线。通过比较不同类型输出模式的特征,我们揭示了低维目标信号与底层多维尖峰列车之间的复杂关系。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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