Integration technology for replacing damaged brain areas with artificial neuronal networks

Лебедева, А.В.; Mishchenko, Mikhail; Bardina, Polina; Fedulina, Anastasiya; Mironov, Andrey; Zhuravleva, Z.D.; Gerasimova, S. A.; Mikhaylo, Alexey; Pisarchik, Alexander N.; Kazantsev, Victor B.

doi:10.1109/dcnair50402.2020.9216941

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…We applied a 50-µs stimulation signal whose amplitude was increased from 100 to 1000 µA with a 100-µA step. A more detailed description of the experimental protocol can be found in our previous publications [37,39,40,48,49]. Data visualization and recording were performed using Patchmacter software.…”

Section: Data Collectionmentioning

confidence: 99%

Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications

Lebedeva,

Samburova,

Razin

et al. 2024

Preprint

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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.

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Section: Data Collectionmentioning

confidence: 99%

Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications

Lebedeva,

Samburova,

Razin

et al. 2024

Preprint

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“…The following shows the original trace of the fEPSP recorded in the dendrites of the CA3 and CA1 hippocampus areas. A similar protocol is presented in our previous works [37,39,40,48,49]. Data visualization and recording were performed using Patchmaster software.…”

Section: Data Collectionmentioning

confidence: 99%

Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications

Lebedeva,

Samburova,

Razin

et al. 2024

Algorithms

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The increasing growth in knowledge about the functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technology developments in recent decades, has led to the emergence of a large number of brain–computer interfaces and neuroprosthetics for 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 the restoration of cognitive functions is an urgent task of modern neurophysiology, neurotechnology and artificial intelligence. The 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 the prediction of hippocampal signals in the CA1 region based on received biological input in the CA3 region. We compare the results of prediction for two widely used deep architectures: reservoir computing (RC) and long short-term memory (LSTM) networks. The 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.

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“…Artificial neuronal network is a theorized mathematical model of the neural network of the human brain, an information processing system based on imitating the structure and function of the neural network of the brain [25,26]. It is an artificially constructed neural network capable of achieving certain functions based on the existing human understanding of the neural network of the brain, which absorbs many advantages of biological neural networks and thus has its special characteristics: (1) highly parallel computing and distributed storage functions: artificial neural networks are composed of many identical basic processing units grouped in parallel, and although the function of each unit is simple, both each Although the function of each unit is simple, both the small unit and the whole neural network have the dual capability of processing and storing information, and these two functions are naturally integrated in the same network, which makes its processing capability and effect on information amazing [27,28].…”

Section: Neural Network Development Of the Allometric Rating Scalementioning

confidence: 99%

Construction of an efficient evaluation model for athletic athletes' competitive ability based on deep neural network algorithm

Niu¹

2023

3C Empresa

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This paper analyzes the data of this year's athletes' physical fitness test scores and manages the classification of different physical qualities of the farmer. In order to reduce the manual calculation and increase the prediction efficiency, as well as to unify the scoring criteria of previous years, this paper proposes a comprehensive performance prediction model based on deep neural network algorithm. First, principal component analysis is used to transform multiple attributes with strong correlation into independent attributes that are not related to each other, and to reduce the time and space for model training by eliminating redundancy. Second, a back propagation (BP) neural network algorithm is used to build a physical fitness test prediction model, and the model is applied to the test dataset for model performance evaluation. Finally, the physical fitness test model was applied to other years for comprehensive performance prediction, and the differences between the model prediction results and the actual teachers' manual calculation results were observed. The results showed very good prediction results for 2021, in which 92.95% of the data had an absolute value of error less than 2 and only 0.06% had an absolute value of error greater than 4, which indicated that the prediction performance of the model was extremely significant. At the same time, a new athletic athletic scoring standard was also developed based on the neural network BP model to provide a more scientific theoretical basis and guidance for the evaluation of athletic ability of athletes.

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Integration technology for replacing damaged brain areas with artificial neuronal networks

Cited by 7 publications

References 28 publications

Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications

Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications

Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications

Construction of an efficient evaluation model for athletic athletes' competitive ability based on deep neural network algorithm

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