日期 影响因子
日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:
–

Spiking neural networks provide accurate and time-efficient models for whisker stimulus classification of the awake mouse

脉冲神经网络为清醒小鼠的胡须刺激分类提供了准确且高效的模型。

期刊:Frontiers in Neuroscience
影响因子:3.2
doi:10.3389/fnins.2026.1605209
Albrecht, Steffen; Vandevelde, Jens R; Vecchi, Edoardo; Berra, Gabriele; Bassetti, Davide; Stüttgen, Maik C; Luhmann, Heiko J; Horenko, Illia
发表日期：2026
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On cheap entropy-sparsified regression learning

低成本熵稀疏回归学习

期刊:Proceedings of the National Academy of Sciences of the United States of America
影响因子:9.1
doi:10.1073/pnas.2214972120
Horenko, Illia; Vecchi, Edoardo; Kardoš, Juraj; Wächter, Andreas; Schenk, Olaf; O'Kane, Terence J; Gagliardini, Patrick; Gerber, Susanne
发表日期：2023
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Cheap robust learning of data anomalies with analytically solvable entropic outlier sparsification

利用解析可解的熵异常值稀疏化方法，以低成本、稳健的方式学习数据异常。

期刊:Proceedings of the National Academy of Sciences of the United States of America
影响因子:9.1
doi:10.1073/pnas.2119659119
Horenko, Illia
发表日期：2022
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A Resilience Related Glial-Neurovascular Network Is Transcriptionally Activated after Chronic Social Defeat in Male Mice

雄性小鼠在经历慢性社会挫败后，与韧性相关的神经胶质血管网络在转录水平上被激活

期刊:Cells
影响因子:5.2
doi:10.3390/cells11213405
Vennin, Constance; Hewel, Charlotte; Todorov, Hristo; Wendelmuth, Marlon; Radyushkin, Konstantin; Heimbach, André; Horenko, Illia; Ayash, Sarah; Müller, Marianne B; Schweiger, Susann; Gerber, Susanne; Lutz, Beat
神经科学
发表日期：2022
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Co-Inference of Data Mislabelings Reveals Improved Models in Genomics and Breast Cancer Diagnostics

数据错误标签的共同推断揭示了基因组学和乳腺癌诊断中模型的改进

期刊:Frontiers in Artificial Intelligence
影响因子:4.7
doi:10.3389/frai.2021.739432
Gerber, Susanne; Pospisil, Lukas; Sys, Stanislav; Hewel, Charlotte; Torkamani, Ali; Horenko, Illia
肿瘤
肿瘤免疫
乳腺癌
发表日期：2021
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A deeper look into natural sciences with physics-based and data-driven measures

运用基于物理学和数据驱动的方法来更深入地研究自然科学。

期刊:iScience
影响因子:4.1
doi:10.1016/j.isci.2021.102171
Rodrigues, Davi Röhe; Everschor-Sitte, Karin; Gerber, Susanne; Horenko, Illia
发表日期：2021
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Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo

通过聚类和控制适应性，揭示了活体皮层网络中时间扭曲的脉冲时间模式。

期刊:Scientific Reports
影响因子:3.9
doi:10.1038/s41598-021-94002-0
Isbister, James B; Reyes-Puerta, Vicente; Sun, Jyh-Jang; Horenko, Illia; Luhmann, Heiko J
发表日期：2021
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Low-cost scalable discretization, prediction, and feature selection for complex systems

复杂系统的低成本可扩展离散化、预测和特征选择

期刊:Science Advances
影响因子:12.5
doi:10.1126/sciadv.aaw0961
Gerber, S; Pospisil, L; Navandar, M; Horenko, I
发表日期：2020
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A scalable approach to the computation of invariant measures for high-dimensional Markovian systems

一种计算高维马尔可夫系统不变测度的可扩展方法

期刊:Scientific Reports
影响因子:3.9
doi:10.1038/s41598-018-19863-4
Gerber, Susanne; Olsson, Simon; Noé, Frank; Horenko, Illia
发表日期：2018
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Toward a direct and scalable identification of reduced models for categorical processes

面向分类过程的直接且可扩展的简化模型识别

期刊:Proceedings of the National Academy of Sciences of the United States of America
影响因子:9.1
doi:10.1073/pnas.1612619114
Gerber, Susanne; Horenko, Illia
发表日期：2017
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