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  1. 1861

    Incremental accumulation of linguistic context in artificial and biological neural networks by Refael Tikochinski, Ariel Goldstein, Yoav Meiri, Uri Hasson, Roi Reichart

    Published 2025-01-01
    “…Our findings reveal how the brain’s hierarchical temporal processing mechanisms enable the flexible integration of information over time, providing valuable insights for both cognitive neuroscience and AI development.…”
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    Article
  2. 1862

    Where Environment Meets Cognition: A Focus on Two Developmental Intellectual Disability Disorders by I. De Toma, L. Manubens Gil, S. Ossowski, M. Dierssen

    Published 2016-01-01
    “…One of the most challenging questions in neuroscience is to dissect how learning and memory, the foundational pillars of cognition, are grounded in stable, yet plastic, gene expression states. …”
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    Article
  3. 1863

    Persistent homology based topological analysis on the gestalt patterns during human brain cognition process by Zaisheng LIU, Fei NI, Rongpeng LI, Honggang ZHANG, Chang LIU, Jiefang ZHANG, Songyun XIE

    Published 2021-07-01
    “…The integrated development of information communication technology and neuroscience heralds the possibility and great potential of brain-to-brain wireless communication(B2BC).The physiologically meaningful features of brain responses were extracted to different contour and shape in images in Gestalt cognitive tests by combining persistent homology analysis with electroencephalogram (EEG).The experimental results show that more brain regions in the frontal lobe were involved when the subject perceives the random and disordered combination of images compared to the ordered Gestalt images.Meanwhile, the persistence entropy of EEG data evoked by random sequence diagram (RSD) was observed to be significantly different from that by the ordered Gestalt images(GST) in several frequency bands, which indicated that human cognition of shapes and contours, such as a preliminary advanced cognition process, could be separated to some extent through topological analysis.This method can digitize the neural signals while preserving the whole and local features of the original signals.In general, the cognitively related neural correlates by persistent homology features of EEG signal are revaluated and quantified, which provides an approach to realize the digitization of neural signals in brain-to-brain wireless communication.…”
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    Article
  4. 1864

    The Promise of Artificial Intelligence in Neuroanesthesia: An Update by Zhenrui Liao, Niharika Mathur, Vidur Joshi, Shailendra Joshi

    Published 2024-09-01
    “…Beyond direct medical care, AI could also automate many routine administrative tasks in health care, assist with teaching and training, and profoundly impact neuroscience research. This article introduces AI and its various approaches from a neuroanesthesiology perspective. …”
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