网络神经科学中的循环和统一分析
Mika Rubinov1,2
1Departments of Biomedical Engineering, Computer Science, and Psychology, Vanderbilt University, Nashville, United States.
eLife
|November 28, 2023
概括
许多系统神经科学研究错误地将现有知识作为新发现,可能会影响成千上万的网络神经科学出版物. 对经验事实进行严格的测试对于真正的科学进步至关重要.
科学领域:
- 系统神经科学 系统神经科学
- 网络神经科学 网络神经科学
- 认知科学 认知科学
背景情况:
- 系统神经科学中的许多分析都未能在已确定的实证数据上验证投机假设.
- 有时使用循环推理,将现有知识误解为新发现.
- 在过去的十年里,这个问题可能影响了3000多项网络神经科学研究.
研究的目的:
- 突出结合现有知识与系统神经科学新发现的问题.
- 估计这个问题在网络神经科学研究中的流行程度和影响.
- 提出提高现场科学发现严格性的策略.
主要方法:
- 系统神经科学研究方法的概念分析.
- 审查常见的分析陷,包括循环推理.
- 估计对已发表的网络神经科学研究的影响.
主要成果:
- 有相当数量的系统神经科学分析可能不代表真正的发现.
- 使用投机证据和循环推理可以掩盖真正的进步.
- 在过去十年中,估计会对3000多项网络神经科学研究产生影响.
结论:
- 未来的网络神经科学研究应该限制投机证据.
- 建议将现有知识整合到基准模型中.
- 严格测试拟议的发现与既有模型的严格测试对于科学完整性至关重要.
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