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The Concept of Hippocampal Activity Restoration Using Artificial Intelligence Technologies

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Mathematical Modeling and Supercomputer Technologies (MMST 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1914))

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Abstract

Real-time processing and decoding of neural signals play a crucial role in biohybrid neuroprostheses, as they provide feedback to modulate or replace neural function. However, several technological challenges associated with this process remain unsolved. These challenges include the need for complex computation in real-time, handling large volumes of data from hundreds or thousands of channels, and extracting meaningful features to drive stimulation. To address these challenges, deep neural networks (DNN) integrated with biohybrid systems have emerged as a novel strategy. In this paper we propose an approach based on DNN for prediction of hippocampal signals based on received biological input. Proposed study is a first step in the complex task of the development of a neurohybrid chip, which allows one to restore memory functions in the damaged rodent hippocampus.

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Acknowledgements

Data collection and preprocessing was supported by Russian Science Foundation (Project No. 23-75-10099), numerical results (model training and testing) was supported by Ministry of Science and Education of Russian Federation (Contract FSWR-2021-0013).

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Authors and Affiliations

  1. Lobachevsky University, Nizhny Novgorod, 603022, Russia

    Anna V. Beltyukova, Vyacheslav V. Razin, Nikolay V. Gromov, Margarita I. Samburova, Mikhail A. Mishchenko, Ivan M. Kipelkin, Anton E. Malkov, Lev A. Smirnov, Tatiana A. Levanova, Svetlana A. Gerasimova & Albina V. Lebedeva

Authors
  1. Anna V. Beltyukova
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  2. Vyacheslav V. Razin
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  3. Nikolay V. Gromov
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  4. Margarita I. Samburova
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  5. Mikhail A. Mishchenko
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  6. Ivan M. Kipelkin
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  7. Anton E. Malkov
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  8. Lev A. Smirnov
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  9. Tatiana A. Levanova
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  10. Svetlana A. Gerasimova
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  11. Albina V. Lebedeva
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Corresponding author

Correspondence to Tatiana A. Levanova .

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Editors and Affiliations

  1. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia

    Dmitry Balandin

  2. Lobachevsky State University, Nizhny Novgorod, Russia

    Konstantin Barkalov

  3. Lobachevsky State University, Nizhny Novgorod, Russia

    Iosif Meyerov

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Beltyukova, A.V. et al. (2024). The Concept of Hippocampal Activity Restoration Using Artificial Intelligence Technologies. In: Balandin, D., Barkalov, K., Meyerov, I. (eds) Mathematical Modeling and Supercomputer Technologies. MMST 2023. Communications in Computer and Information Science, vol 1914. Springer, Cham. https://doi.org/10.1007/978-3-031-52470-7_19

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  • DOI: https://doi.org/10.1007/978-3-031-52470-7_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-52469-1

  • Online ISBN: 978-3-031-52470-7

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