Effective Dimensionality Reduction for Visualizing Neural Dynamics by Laplacian Eigenmaps

DOI10.1162/neco_a_01203zbMath1429.92050OpenAlexW2945537329WikidataQ92194279 ScholiaQ92194279MaRDI QIDQ5214350

Guanghao Sun, Ke-Di Xu, Yiwei Zhang, Xiao-Xiang Zheng, Ting Zhao, Shaomin Zhang, Qiaosheng Zhang

Publication date: 7 February 2020

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_01203

zbMATH Keywords

Laplacian eigenmapsvisualization of neural population dynamics

Mathematics Subject Classification ID

Neural biology (92C20) Biomedical imaging and signal processing (92C55)

Related Items (1)

Neural manifold analysis of brain circuit dynamics in health and disease

Cites Work

• Spike-train spectra and network response functions for nonlinear integrate-and-fire neurons
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• t-SNE Visualization of Large-Scale Neural Recordings
• Population Coding and the Labeling Problem: Extrinsic Versus Intrinsic Representations
• Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron and Ensemble Data Analysis

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