在形神经元形态学分类:一个系统的审查
Fábio Lobato1, Jéssica Leite2, Antonio Jacob3
1Federal University of Western Pará, Institute of Engineering and Geosciences, Santarém, Pará, Brazil; State University of Maranhão, Department of Computer Engineering, São Luís, Maranhão, Brazil; University of São Paulo, Institute of Mathematics and Computer Sciences, São Carlos, São Paulo, Brazil.
本综述强调了人工神经网络作为使用显微镜图像分类神经元形态的主要方法. 准确性是这个快速发展的领域中最常见的评估指标.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 了解大脑功能依赖于连接学和神经元多样性.
- 分类神经元形态是至关重要的,但在计算上是复杂的.
- 需要进行系统审查,以绘制当前方法和未来趋势的地图.
研究的目的:
- 审查神经元形态分类的最先进的计算方法.
- 识别共同的数据表示和评估指标.
- 提供一个更新的视角,指导未来的神经科学研究.
主要方法:
- 从2018-2024年对同行评审的研究进行系统审查.
- 搜索了五个数据库,收集了840篇论文.
- 评估了35项研究的质量,方法和报告.
主要成果:
- 人工神经网络是主要的计算方法 (21篇论文).
- 显微镜图像是最常见的数据表示 (30篇论文).
- 准确性是主要的评估指标 (29篇论文).
结论:
- 人工神经网络和基于图像的特征是当前神经元分类的关键.
- 标准化的评估指标对于进步至关重要.
- 本综述为未来神经元形态分类研究提供了一份路线图.
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