Cognitive Computing Models and Brain‑Derived Algorithms

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Track Chair

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Sichen Tao
Tohoku University

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.

Topics of interest include, but are not limited to:

Brain-Inspired Learning & Adaptation

• Biologically plausible learning rules (Hebbian plasticity, predictive coding, local learning)
• Continuous, lifelong, and open-ended learning frameworks
• Few-shot, zero-shot, and meta-learning inspired by human cognition
• Neuro-symbolic integration and hybrid reasoning algorithms

Cognitive Architectures & Computational Neuroscience

• Structural models of memory (working, episodic, semantic) and attention mechanisms
• Biologically grounded cognitive architectures (e.g., ACT-R, SOAR adaptations)
• Large-scale brain network modeling and functional connectivity simulation
• Neuromodulation, reward processing, and emotion-driven computational models
• Spiking Neural Network (SNN) topologies, neural dynamics, and temporal coding

Perception, Action, and Agency

• Active perception, sensorimotor integration, and embodied cognition
• Predictive processing and the free-energy principle in AI
• Brain-derived algorithms for motor control and trajectory planning
• Social cognition, theory of mind, and multi-agent interactions

Applications of Cognitive and Brain-Derived AI

• Cognitive robotics and learning methods for anthropomorphic systems
• Natural language understanding and grounded conceptualization
• Robust and adaptable AI for noisy, dynamic, and open-world environments
• Human-AI teaming, explainable AI (XAI), and brain-machine interface (BMI) decoding

PATRON, HOST, and SPONSORS