人工神经网络与生物神经元模型的共生:培训和测试
Tatyana Bogatenko1, Konstantin Sergeev1, Andrei Slepnev1
1Institute of Physics, Saratov State University, 83 Astrakhanskaya str., Saratov 410012, Russia.
Chaos (Woodbury, N.Y.)
|July 11, 2023
概括
这项研究证明了将生物神经元模型嵌入到人工神经网络 (ANN) 中,例如菲茨休-纳古莫系统. 这种整合提高了图像识别的准确性,为先进的模拟神经网络设计铺平了道路.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 生物物理学的生物物理.
背景情况:
- 人工神经网络 (ANN) 是由生物神经结构启发的计算模型.
- 菲茨休-纳古莫 (FHN) 系统作为神经元动作潜力的简化数学模型.
- 将生物神经元动态集成到ANN中可以增强它们的能力.
研究的目的:
- 研究使用生物神经元的数学模型创建ANN的可行性.
- 探索将FitzHugh-Nagumo系统纳入训练有素的ANN的影响.
- 通过使用生物启发的ANN来评估图像识别任务的性能改进.
主要方法:
- 在MNIST数据集上训练具有非线性神经元的ANN进行图像识别.
- 将FitzHugh-Nagumo (FHN) 系统引入训练有素的ANN架构中.
- 将混合ANN (与FHN系统) 的准确性与基线ANN进行比较.
主要成果:
- 用非线性神经元训练的ANN成功执行了图像识别.
- 与基线相比,将FHN系统集成到ANN中导致了更好的准确性.
- 混合ANN显示了增强的性能,验证了该方法.
结论:
- 人工神经网络可以成功地用生物神经元的数学模型来增强.
- 菲茨休-纳古莫系统集成为增强ANN性能提供了一条途径.
- 这项研究支持使用更具生物现实的神经元模型开发模拟神经网络.
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