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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Decoding the application of deep learning in neuroscience: a bibliometric analysis.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computational Biology

Background:

  • Deep learning (DL) offers powerful tools for neuroscience.
  • Understanding brain dynamics and neurological disorders is a key challenge.
  • Bibliometric analysis can reveal trends in DL applications in neuroscience.

Purpose of the Study:

  • To analyze the integration and evolutionary trends of deep learning in neuroscience from 2012 to 2023.
  • To identify key research hotspots and thematic shifts in this interdisciplinary field.
  • To provide a roadmap for future research directions in deep learning for neuroscience.

Main Methods:

  • Bibliometric analysis of 421 research articles published between 2012 and 2023.
  • Examination of the application of deep learning techniques, algorithms, models, and neural networks.
  • Thematic analysis to track the evolution of research methodologies and focus areas.

Main Results:

  • Significant growth in interdisciplinary research applying DL to neuroscience.
  • Classification algorithms, models, and neural networks are pivotal for data interpretation and simulation.
  • A thematic evolution from foundational methods to specialized approaches like EEG analysis and convolutional neural networks.

Conclusions:

  • Deep learning is transforming neuroscience, aiding in understanding neural mechanisms and neurological disorders.
  • Interdisciplinary collaboration and adoption of advanced technologies are crucial for innovation.
  • The study highlights key areas for future breakthroughs and practical applications in decoding the brain.