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Survey details ML methods for neural activity dynamics

By PulseAugur Editorial · [2 sources] ·

This paper surveys machine learning methods for analyzing neural activity dynamics, focusing on Latent Variable Models (LVMs). It categorizes LVMs into single-region dynamics, multi-region communication, and behavior-aligned modeling. The survey also covers large-scale neural foundation models like Transformers and diffusion models, discussing current challenges and future research directions for interpretable brain dynamics and neural decoding. AI

IMPACT Provides a structured overview of ML techniques for neuroscience, potentially guiding future research in brain-computer interfaces and neural decoding.

RANK_REASON The cluster contains an academic paper published on arXiv.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Survey details ML methods for neural activity dynamics

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The cluster contains an academic paper published on arXiv.
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109 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Shufeng Kong, Fumei Deng, Xinyi Dong, Caihua Liu, Weiwei Chen, Yingheng Wang, Daniel Cao, Azahara Oliva, Antonio Fernandez-Ruiz, Carla Gomes ·

    Machine Learning Methods for Studying Latent Neural Activity Dynamics

    arXiv:2606.10530v1 Announce Type: cross Abstract: Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations of neurons. In this paper, we provide a comprehensive survey that outlines the tr…

  2. arXiv cs.AI TIER_1 English(EN) · Carla Gomes ·

    Machine Learning Methods for Studying Latent Neural Activity Dynamics

    Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations of neurons. In this paper, we provide a comprehensive survey that outlines the trajectory of Latent Variable Models (LVMs) from ear…

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