Decoding the application of deep learning in neuroscience: a bibliometric analysis.

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Computational Neuroscience Pub Date : 2024-10-04 eCollection Date: 2024-01-01 DOI:10.3389/fncom.2024.1402689
Yin Li, Zilong Zhong
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引用次数: 0

Abstract

The application of deep learning in neuroscience holds unprecedented potential for unraveling the complex dynamics of the brain. Our bibliometric analysis, spanning from 2012 to 2023, delves into the integration of deep learning in neuroscience, shedding light on the evolutionary trends and identifying pivotal research hotspots. Through the examination of 421 articles, this study unveils a significant growth in interdisciplinary research, marked by the burgeoning application of deep learning techniques in understanding neural mechanisms and addressing neurological disorders. Central to our findings is the critical role of classification algorithms, models, and neural networks in advancing neuroscience, highlighting their efficacy in interpreting complex neural data, simulating brain functions, and translating theoretical insights into practical diagnostics and therapeutic interventions. Additionally, our analysis delineates a thematic evolution, showcasing a shift from foundational methodologies toward more specialized and nuanced approaches, particularly in areas like EEG analysis and convolutional neural networks. This evolution reflects the field's maturation and its adaptation to technological advancements. The study further emphasizes the importance of interdisciplinary collaborations and the adoption of cutting-edge technologies to foster innovation in decoding the cerebral code. The current study provides a strategic roadmap for future explorations, urging the scientific community toward areas ripe for breakthrough discoveries and practical applications. This analysis not only charts the past and present landscape of deep learning in neuroscience but also illuminates pathways for future research, underscoring the transformative impact of deep learning on our understanding of the brain.

解码深度学习在神经科学中的应用:文献计量分析。
深度学习在神经科学中的应用为揭示大脑的复杂动力学提供了前所未有的潜力。我们的文献计量分析跨越 2012 年至 2023 年,深入探讨了深度学习与神经科学的结合,揭示了演变趋势,并确定了关键的研究热点。通过对 421 篇文章的研究,本研究揭示了跨学科研究的显著增长,其标志是深度学习技术在理解神经机制和解决神经系统疾病方面的蓬勃应用。我们研究结果的核心是分类算法、模型和神经网络在推动神经科学发展方面的关键作用,突出了它们在解释复杂神经数据、模拟大脑功能以及将理论见解转化为实际诊断和治疗干预措施方面的功效。此外,我们的分析还勾勒出一个主题演变过程,展示了从基础方法到更专业、更细致的方法的转变,尤其是在脑电图分析和卷积神经网络等领域。这种演变反映了该领域的成熟及其对技术进步的适应。研究进一步强调了跨学科合作和采用尖端技术的重要性,以促进解码大脑密码的创新。当前的研究为未来的探索提供了一个战略路线图,敦促科学界朝着突破性发现和实际应用成熟的领域迈进。这项分析不仅描绘了神经科学领域深度学习的过去和现在,还为未来研究指明了道路,强调了深度学习对我们理解大脑的变革性影响。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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