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Incorporating Task-Related Information in Dimensionality Reduction of Neural Population Using Autoencoders

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Human Brain and Artificial Intelligence (HBAI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1369))

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Abstract

Dimensionality reduction plays an important role in neural signal analysis. Most dimensionality reduction methods can effectively describe the majority of the variance of the data, such as principal component analysis (PCA) and locally linear embedding (LLE). However, they may not be able to capture useful information given a specific task, since these approaches are unsupervised. This study proposes an autoencoder-based approach that incorporates task-related information as strong guidance to the dimensionality reduction process, such that the low dimensional representations can better reflect information directly related to the task. Experimental results show that the proposed method is capable of finding task-related features of the neural population effectively.

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Acknowledgments

This work was partly supported by the grants from National Key R&D Program of China (2018YFA0701400), National Natural Science Foundation of China (No. 61673340), Zhejiang Provincial Natural Science Foundation of China (LZ17F030001), Fundamental Research Funds for the Central Universities (2020FZZX001-05), and the Zhejiang Lab (2019KE0AD01).

Author information

Authors and Affiliations

  1. Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China

    Qi Lian & Yu Zhao

  2. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Qi Lian, Yu Zhao & Yu Qi

  3. Information Technology Research Center, China Electronics Standardization Institute, Beijing, China

    Yunzhu Liu

  4. China National Information Technology Standardization Technical Committee Secretariat, Beijing, China

    Yunzhu Liu

  5. Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, Hangzhou, China

    Yu Qi

  6. Zhejiang Lab, Hangzhou, China

    Yu Qi

Authors
  1. Qi Lian
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  2. Yunzhu Liu
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  3. Yu Zhao
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  4. Yu Qi
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Corresponding author

Correspondence to Yu Qi .

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Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Yueming Wang

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Lian, Q., Liu, Y., Zhao, Y., Qi, Y. (2021). Incorporating Task-Related Information in Dimensionality Reduction of Neural Population Using Autoencoders. In: Wang, Y. (eds) Human Brain and Artificial Intelligence. HBAI 2021. Communications in Computer and Information Science, vol 1369. Springer, Singapore. https://doi.org/10.1007/978-981-16-1288-6_4

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  • DOI: https://doi.org/10.1007/978-981-16-1288-6_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1287-9

  • Online ISBN: 978-981-16-1288-6

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