{"id":11486,"date":"2026-02-11T08:05:00","date_gmt":"2026-02-11T08:05:00","guid":{"rendered":"https:\/\/mcsoc-forum.org\/site\/?page_id=11486"},"modified":"2026-02-11T08:05:01","modified_gmt":"2026-02-11T08:05:01","slug":"neurocore-t13","status":"publish","type":"page","link":"https:\/\/mcsoc-forum.org\/site\/index.php\/neurocore-t13\/","title":{"rendered":"Cognitive Computing Models and Brain\u2011Derived Algorithms"},"content":{"rendered":"\n
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\n \n \u2190 Back to the NeuroCore 2026 Page\n <\/a>\n<\/p>\n

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Track Chair<\/h2>\n
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\"Sichen<\/figure>

Sichen Tao<\/a><\/strong>
Tohoku University<\/span><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

Cognitive computing and brain-derived algorithms are fundamentally reshaping the landscape of artificial intelligence by moving beyond traditional deep learning toward more adaptive, efficient, and robust systems. This track invites high-quality contributions that explore the intersection of neuroscience, cognitive science, and computational modeling. We welcome original research, visionary concepts, and practical implementations that translate biological principles into next-generation algorithms, cognitive architectures, and intelligent systems capable of perception, reasoning, and autonomous decision-making.<\/p>\n\n\n\n

Topics of interest include, but are not limited to:<\/strong><\/h4>\n\n\n\n

Brain-Inspired Learning & Adaptation<\/strong><\/h3>\n\n\n\n
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  • Biologically plausible learning rules (Hebbian plasticity, predictive coding, local learning)<\/li>\n\n\n\n
  • Continuous, lifelong, and open-ended learning frameworks<\/li>\n\n\n\n
  • Few-shot, zero-shot, and meta-learning inspired by human cognition<\/li>\n\n\n\n
  • Neuro-symbolic integration and hybrid reasoning algorithms<\/li>\n<\/ul>\n\n\n\n

    Cognitive Architectures & Computational Neuroscience<\/strong><\/h3>\n\n\n\n
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    • Structural models of memory (working, episodic, semantic) and attention mechanisms<\/li>\n\n\n\n
    • Biologically grounded cognitive architectures (e.g., ACT-R, SOAR adaptations)<\/li>\n\n\n\n
    • Large-scale brain network modeling and functional connectivity simulation<\/li>\n\n\n\n
    • Neuromodulation, reward processing, and emotion-driven computational models<\/li>\n\n\n\n
    • Spiking Neural Network (SNN) topologies, neural dynamics, and temporal coding<\/li>\n<\/ul>\n\n\n\n

      Perception, Action, and Agency<\/strong><\/h3>\n\n\n\n
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      • Active perception, sensorimotor integration, and embodied cognition<\/li>\n\n\n\n
      • Predictive processing and the free-energy principle in AI<\/li>\n\n\n\n
      • Brain-derived algorithms for motor control and trajectory planning<\/li>\n\n\n\n
      • Social cognition, theory of mind, and multi-agent interactions<\/li>\n<\/ul>\n\n\n\n

        Applications of Cognitive and Brain-Derived AI<\/strong><\/h3>\n\n\n\n
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        • Cognitive robotics and learning methods for anthropomorphic systems<\/li>\n\n\n\n
        • Natural language understanding and grounded conceptualization<\/li>\n\n\n\n
        • Robust and adaptable AI for noisy, dynamic, and open-world environments<\/li>\n\n\n\n
        • Human-AI teaming, explainable AI (XAI), and brain-machine interface (BMI) decoding<\/li>\n<\/ul>\n\n\n\n

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