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We develop an experimental approach for stimulating neuronal activity in mouse hippocampal slices using an artificial neuron controlled by various types of predicted signals. These signals are generated by a long short-term memory (LSTM) neural network trained on an array of neuronal data to produce an output response in the hippocampus, specifically, local field potentials. This method is implemented in in vitro neurophysiological experiments using the Arduino UNO hardware platform. Our approach represents a foundational step towards the intellectual restoration of neuronal activity in the entire brain of experimental animals, with the ultimate goal of memory function restoration. Addressing this significant challenge necessitates a comprehensive investigation of these processes at the cellular-network level.

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Neuronal Activity Stimulation in Mouse Hippocampal Slice for Memory Restoration Using LSTM-Predicted Signal

  • 会议论文

  • 发表日期：

    2025年

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  • Alexander V. Naumov,
  • Ivan M. Kipelkin,
  • Margarita I. Samburova,
  • Vyacheslav V. Razin,
  • Nikolay V. Gromov,
  • Maria I. Yashanova,
  • Svetlana A. Gerasimova,
  • Tatiana A. Levanova,
  • Lev A. Smirnov,
  • Albina V. Lebedeva,
  • Alexander N. Pisarchik

摘要

We develop an experimental approach for stimulating neuronal activity in mouse hippocampal slices using an artificial neuron controlled by various types of predicted signals. These signals are generated by a long short-term memory (LSTM) neural network trained on an array of neuronal data to produce an output response in the hippocampus, specifically, local field potentials. This method is implemented in in vitro neurophysiological experiments using the Arduino UNO hardware platform. Our approach represents a foundational step towards the intellectual restoration of neuronal activity in the entire brain of experimental animals, with the ultimate goal of memory function restoration. Addressing this significant challenge necessitates a comprehensive investigation of these processes at the cellular-network level.