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Updated: May 5, 2026

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
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Exploring synergies: Advancing neuroscience with machine learning.

Marzieh Ajirak1, Tülay Adali2, Saeid Sanei3

  • 1Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA.

Signal Processing
|August 22, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) advances neuroscience by offering new ways to analyze brain activity and connectivity. These methods provide interpretable, adaptive tools for personalized brain data analysis and interventions.

Keywords:
Adaptive beamformingBrain connectivityDiscrete representation learningEpilepsyGaussian processesIndependent vector analysisfMRI

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Machine learning (ML) offers powerful analytical tools for neuroscience.
  • Analyzing complex neural data, brain connectivity, and guiding interventions are key challenges.

Purpose of the Study:

  • To present core mathematical frameworks in ML for neuroscience.
  • To highlight ML applications in analyzing neural data and guiding interventions.

Main Methods:

  • State-space models for closed-loop neurostimulation.
  • Discrete representation learning for time-series analysis.
  • Gaussian processes for high-dimensional time series analysis.
  • Independent vector analysis for multi-subject neuroimaging.
  • Distributed beamforming for EEG source localization.

Main Results:

  • Extracted meaningful patterns from complex neural recordings.
  • Revealed inter-regional brain connectivity.
  • Identified shared patterns in multi-subject neuroimaging while preserving individual differences.
  • Localized seizure sources from EEG data for surgical planning.

Conclusions:

  • ML provides interpretable, adaptive, and personalized tools for neuroscience.
  • Methodological innovations demonstrate ML's growing role in analyzing brain activity.
  • ML supports data-driven interventions in neuroscience research and clinical applications.