Effective Dimensionality Reduction for Visualizing Neural Dynamics by Laplacian Eigenmaps

Author:

Sun Guanghao1,Zhang Shaomin1,Zhang Yiwei1,Xu Kedi1,Zhang Qiaosheng1,Zhao Ting2,Zheng Xiaoxiang1

Affiliation:

1. Qiushi Academy for Advanced Studies, Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, and Department of Biomedical Engineering, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou 310027, China

2. Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, U.S.A.

Abstract

With the development of neural recording technology, it has become possible to collect activities from hundreds or even thousands of neurons simultaneously. Visualization of neural population dynamics can help neuroscientists analyze large-scale neural activities efficiently. In this letter, Laplacian eigenmaps is applied to this task for the first time, and the experimental results show that the proposed method significantly outperforms the commonly used methods. This finding was confirmed by the systematic evaluation using nonhuman primate data, which contained the complex dynamics well suited for testing. According to our results, Laplacian eigenmaps is better than the other methods in various ways and can clearly visualize interesting biological phenomena related to neural dynamics.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

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