Version 1
: Received: 1 May 2024 / Approved: 3 May 2024 / Online: 3 May 2024 (11:22:09 CEST)
How to cite:
Lebedeva, A. V.; Samburova, M. I.; Razin, V. V.; Gromov, N. V.; Gerasimova, S. A.; Levanova, T. A.; Smirnov, L. A.; Pisarchik, A. N. Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications. Preprints2024, 2024050155. https://doi.org/10.20944/preprints202405.0155.v1
Lebedeva, A. V.; Samburova, M. I.; Razin, V. V.; Gromov, N. V.; Gerasimova, S. A.; Levanova, T. A.; Smirnov, L. A.; Pisarchik, A. N. Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications. Preprints 2024, 2024050155. https://doi.org/10.20944/preprints202405.0155.v1
Lebedeva, A. V.; Samburova, M. I.; Razin, V. V.; Gromov, N. V.; Gerasimova, S. A.; Levanova, T. A.; Smirnov, L. A.; Pisarchik, A. N. Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications. Preprints2024, 2024050155. https://doi.org/10.20944/preprints202405.0155.v1
APA Style
Lebedeva, A. V., Samburova, M. I., Razin, V. V., Gromov, N. V., Gerasimova, S. A., Levanova, T. A., Smirnov, L. A., & Pisarchik, A. N. (2024). Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications. Preprints. https://doi.org/10.20944/preprints202405.0155.v1
Chicago/Turabian Style
Lebedeva, A. V., Lev A. Smirnov and Alexander N. Pisarchik. 2024 "Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications" Preprints. https://doi.org/10.20944/preprints202405.0155.v1
Abstract
The increasing growth in knowledge about functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technologies development in recent decades, have led to the emergence of a large number of brain-computer interfaces and neuroprosthetics for the regenerative medicine tasks. Neurotechnologies have traditionally been developed for therapeutic purposes to help or replace motor, sensory or cognitive abilities damaged by injury or disease. They also have significant potential for memory enhancement. However, there are still no fully developed neurotechnologies and neural interfaces capable of restoring or expanding cognitive functions, in particular memory, in mammals or humans. In this regard, the search for new technologies in the field of restoration of cognitive functions is an urgent task of modern neurophysiology, neurotechnology and artificial intelligence. Hippocampus is an important brain structure connected to memory and information processing in the brain. The aim of this paper is to propose an approach based on deep neural networks for prediction of hippocampal signals in CA1 region based on received biological input in CA3 region. We compare results of prediction for two widely used deep architectures: reservoir computing (RC) and long short-term memory (LSTM) networks. Proposed study can be viewed as 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.
Keywords
Hippocampal signals; Deep neural networks; Neuroprosthetics; Reservoir computing; Long short-term memory (LSTM)
Subject
Biology and Life Sciences, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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