Neuromorphic system development based on adaptive neuronal network to modulate synaptic transmission in hippocampus

Лебедева, А.В.; Gerasimova, S. A.; Fedulina, Anastasiya; Mishchenko, Mikhail; Beltyukova, Anna; Matveeva, Mariya; Mikhaylov, A. N.; Pisarchik, Alexander N.; Kazantsev, Victor B.

doi:10.1109/cnn53494.2021.9580201

Cited by 6 publications

(3 citation statements)

References 10 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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…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%

“…Adding to this tapestry of innovation, our own contributions have illuminated novel avenues in memory restoration. Through the conceptual fusion of rodent hippocampal activity and memristive devices, we have unveiled a symbiotic relationship between living hippocampal cells and neuron-like oscillatory systems [37][38][39][40][41]. This interplay promises not only to redefine our understanding of memory mechanisms but also to inspire transformative interventions in the realm of cognitive enhancement.…”

Section: Introductionmentioning

confidence: 99%

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

Lebedeva,

Samburova,

Razin

et al. 2024

Algorithms

View full text Add to dashboard Cite

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.

show abstract

The Concept of Hippocampal Activity Restoration Using Artificial Intelligence Technologies

Beltyukova,

Razin,

Gromov

et al. 2024

Communications in Computer and Information Science

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Neuromorphic system development based on adaptive neuronal network to modulate synaptic transmission in hippocampus

Cited by 6 publications

References 10 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

The Concept of Hippocampal Activity Restoration Using Artificial Intelligence Technologies

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