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.
The proposed architecture demonstrates the feasibility of persistent, context-aware, non-centralized spectrum cognition and can host neuro-fuzzy, swarm, evolutionary, reinforcement-learning, or hybrid mechanisms.
Ameer H. Ali, Ali Samir Saleem· International Journal of Fut...· 0 citations
This work introduces NeuralRNN, an open-source Python framework that unifies these paradigms and model variants in a general-purpose pipeline, and provides an integrated and extensible model library spanning generic, structure-constrained, gain-modulated, and gated RNNs.
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
Using neuro-symbolic AI as an end-to-end case study, it is shown that co-designing the full software-to-silicon stack yields orders-of-magnitude efficiency gains while preserving the accuracy and interpretability that make cognitive AI compelling.
Zi-Shen Wan, Yu Cao, S. K. Gupta et al.· IEEE Micro· 0 citations
A 2nd-order neuron with two-stage synaptic filtering for richer temporal dynamics; a fully-connected 6-neuron spiking network with configurable weights demonstrating weight-based inter-neuron communication; and a direct verification methodology enabling per-cycle observation of all internal states are contributed.
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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