Vol. 4 No. 1 (2024): Issue 4
Articles

Simulation of Leaky Integrate-and-Fire with Random Forest Regression

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  • Budi Santoso,
  • Siti Nurhaliza
Budi Santoso
Computational Neuroscience Lab, Universitas Negeri Malang
Siti Nurhaliza
Institute of Data Science and Artificial Intelligence, Universitas Syiah Kuala
DOI: https://doi.org/10.71070/jcbm.v4i1.101

Published 2024-10-22

Keywords

  • Neural Networks,
  • Neuronal Spiking,
  • Leaky Integrate-and-Fire,
  • Random Forest Regression,
  • Spike Timing Precision

How to Cite

Santoso, B., & Nurhaliza, S. (2024). Simulation of Leaky Integrate-and-Fire with Random Forest Regression. Journal of Computational Biology and Medicine, 4(1). https://doi.org/10.71070/jcbm.v4i1.101

Abstract

Neural network models play a crucial role in understanding the complex dynamics of neuronal spiking activities in the brain. Among these models, the Leaky Integrate-and-Fire (LIF) neuron model has been widely used due to its simplicity and efficiency. However, accurately simulating the spiking behavior of LIF neurons remains a challenging task. Current research efforts often face limitations in accurately capturing the nonlinear dynamics and spike timing precision of LIF neurons. To address this issue, this paper proposes a novel approach that combines the LIF neuron model with Random Forest Regression. This innovative methodology aims to improve the accuracy and efficiency of simulating neuronal spiking activities. The incorporation of Random Forest Regression enables better prediction of the spiking behavior of LIF neurons, providing a more precise model for studying neural network dynamics.

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