Department of Biology, University of Washington, Seattle, WA 98195, USA; Institute for Neuroengineering, University of Washington, Seattle, WA 98195, USA; eScience Institute, University of Washington, Seattle, WA 98195, USA.
Institute for Neuroengineering, University of Washington, Seattle, WA 98195, USA; eScience Institute, University of Washington, Seattle, WA 98195, USA; Department of Psychology, University of Washington, Seattle, WA 98195, USA.
Curr Opin Neurobiol. 2019 Oct;58:21-29. doi: 10.1016/j.conb.2019.06.008. Epub 2019 Jul 17.
Discoveries in modern human neuroscience are increasingly driven by quantitative understanding of complex data. Data-intensive approaches to modeling have promise to dramatically advance our understanding of the brain and critically enable neuroengineering capabilities. In this review, we provide an accessible primer to modern modeling approaches and highlight recent data-driven discoveries in the domains of neuroimaging, single-neuron and neuronal population responses, and device neuroengineering. Further, we suggest that meaningful progress requires the community to tackle open challenges in the realms of model interpretability and generalizability, training pipelines of data-fluent human neuroscientists, and integrated consideration of data ethics.
现代人类神经科学的发现越来越依赖于对复杂数据的定量理解。数据密集型建模方法具有极大地提高我们对大脑的理解能力，并能显著提高神经工程能力的潜力。在这篇综述中，我们提供了一个现代建模方法的入门指南，并强调了神经影像学、单个神经元和神经元群体反应以及设备神经工程领域的数据驱动发现。此外，我们认为，要取得有意义的进展，就需要研究界解决模型可解释性和泛化、数据流畅的人类神经科学家的培训管道以及综合考虑数据伦理等领域的开放性挑战。
