The MNN architecture simulates the hierarchical compression, consensus optimization, and metacognitive arbitration mechanisms of biological intelligence, providing a theoretically self-consistent and engineeringly executable blueprint for building scalable, interpretable, and continuously evolving general artificial intelligence systems.
Objective: To develop and evaluate a brain-inspired cognitive architecture that enables non-centralized spectrum intelligence while preserving primary-user (PU) protection and local radio autonomy.
Methods: Each cognitive radio is modeled as an autonomous agent with perception and attention, working and long-term memo...
Ameer H. Ali, Ali Samir Saleem· International Journal of Fut...· 0 citations
Recurrent neural networks (RNNs) provide a central tool in neuroscience to optimize cognitive tasks and reconstruct dynamical systems. Around these two paradigms, many RNN model variants have been tailored to capture specific computations of the brain. In practice, however, this methodological diversity remains difficu...
Constructing dynamic virtual brain models is essential for understanding brain functions and pathological mechanisms, crucial in computational neuroscience. Current modeling methods can be grouped into two paradigms: deep learning models for accurate simulation, and neural dynamics models emphasizing physiological inte...
Lan Yang, Xiao-Yu Cui, Ting Li et al.· IEEE Transactions on Neural...· 0 citations
—The deep-learning scaling that drove the past decade of AI is colliding with hard limits in energy, data, robustness, and explainability, just as emerging applications demand the capabilities neural networks lack: reasoning, abstraction, and collaboration. The CoCoSys JUMP 2.0 center confronts this by co-designing cog...
Zi-Shen Wan, Yu Cao, S. K. Gupta et al.· IEEE Micro· 0 citations
Spiking Neural Networks (SNNs) offer a promising path toward ultra-low-power artificial intelligence inference by emulating the event-driven computation of biological neurons. However, two challenges limit their practical deployment. First, fixed-parameter Leaky Integrate-and-Fire (LIF) neurons lack the adaptation mech...
An automated optimization framework with a hierarchical two-layer tuning mechanism that synergizes theoretical I/O constraints with graph-level adaptive fusion while accounting for search overhead, the framework systematically explores high-performance execution patterns.
Rui Xia, Gencheng Liu, Quan-Li Li et al.· ACM Transactions on Architec...· 0 citations
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